Preamble This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning. Summary The majority of machine learning projects that you read about or work on are built around batch processes. The model is trained, and then validated, and then deployed, with each step being a discrete and isolated task. Unfortunately, the real world is rarely static, leading to concept drift and model failures. River is a framework for building streaming machine learning projects that can constantly adapt to new information. In this episode Max Halford explains how the project works, why you might (or might not) want to consider streaming ML, and how to get started building with River. Announcements * Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. * Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started! * Your host is Tobias Macey and today I’m interviewing Max Halford about River, a Python toolkit for streaming and online machine learning Interview * Introduction * How did you get involved in machine learning? * Can you describe what River is and the story behind it? * What is "online" machine learning? * What are the practical differences with batch ML? * Why is batch learning so predominant? * What are the cases where someone would want/need to use online or streaming ML? * The prevailing pattern for batch ML model lifecycles is to train, deploy, monitor, repeat. What does the ongoing maintenance for a streaming ML model look like? * Concept drift is typically due to a discrepancy between the data used to train a model and the actual data being observed. How does the use of online learning affect the incidence of drift? * Can you describe how the River framework is implemented? * How have the design and goals of the project changed since you started working on it? * How do the internal representations of the model differ from batch learning to allow for incremental updates to the model state? * In the documentation you note the use of Python dictionaries for state management and the flexibility offered by that choice. What are the benefits and potential pitfalls of that decision? * Can you describe the process of using River to design, implement, and validate a streaming ML model? * What are the operational requirements for deploying and serving the model once it has been developed? * What are some of the challenges that users of River might run into if they are coming from a batch learning background? * What are the most interesting, innovative, or unexpected ways that you have seen River used? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on River? * When is River the wrong choice? * What do you have planned for the future of River? Contact Info * Email * @halford_max on Twitter * MaxHalford on GitHub Parting Question * From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * River * scikit-multiflow * Federated Machine Learning * Hogwild! Google Paper * Chip Huyen concept drift blog post * Dan Crenshaw Berkeley Clipper MLOps * Robustness Principle * NY Taxi Dataset * RiverTorch * River Public Roadmap * Beaver tool for deploying online models * Prodigy ML human in the loop labeling The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0 Sponsored By: * Linode: Do you want to try out some of the tools and applications that you heard about on Podcast.\_\_init\_\_? Do you have a side project that you want to share with the world? With Linode's managed Kubernetes platform it's now even easier to get started with the latest in cloud technologies. With the combined power of the leading container orchestrator and the speed and reliability of Linode's object storage, node balancers, block storage, and dedicated CPU or GPU instances, you've got everything you need to scale up. Go to [pythonpodcast.com/linode](https://www.pythonpodcast.com/linode) today and get a $100 credit to launch a new cluster, run a server, upload some data, or... And don't forget to thank them for being a long time supporter of Podcast.\_\_init\_\_!

Preamble This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning. Summary Deep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference models. Along with that power comes a great deal of complexity in determining what neural architectures are best suited to a given task, engineering features, scaling computation, etc. Predibase is building on the successes of the Ludwig framework for declarative deep learning and Horovod for horizontally distributing model training. In this episode CTO and co-founder of Predibase, Travis Addair, explains how they are reducing the burden of model development even further with their managed service for declarative and low-code ML and how they are integrating with the growing ecosystem of solutions for the full ML lifecycle. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great! * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host is Tobias Macey and today I’m interviewing Travis Addair about Predibase, a low-code platform for building ML models in a declarative format Interview * Introduction * How did you get involved in machine learning? * Can you describe what Predibase is and the story behind it? * Who is your target audience and how does that focus influence your user experience and feature development priorities? * How would you describe the semantic differences between your chosen terminology of "declarative ML" and the "autoML" nomenclature that many projects and products have adopted? * Another platform that launched recently with a promise of "declarative ML" is Continual. How would you characterize your relative strengths? * Can you describe how the Predibase platform is implemented? * How have the design and goals of the product changed as you worked through the initial implementation and started working with early customers? * The operational aspects of the ML lifecycle are still fairly nascent. How have you thought about the boundaries for your product to avoid getting drawn into scope creep while providing a happy path to delivery? * Ludwig is a core element of your platform. What are the other capabilities that you are layering around and on top of it to build a differentiated product? * In addition to the existing interfaces for Ludwig you created a new language in the form of PQL. What was the motivation for that decision? * How did you approach the semantic and syntactic design of the dialect? * What is your vision for PQL in the space of "declarative ML" that you are working to define? * Can you describe the available workflows for an individual or team that is using Predibase for prototyping and validating an ML model? * Once a model has been deemed satisfactory, what is the path to production? * How are you approaching governance and sustainability of Ludwig and Horovod while balancing your reliance on them in Predibase? * What are some of the notable investments/improvements that you have made in Ludwig during your work of building Predibase? * What are the most interesting, innovative, or unexpected ways that you have seen Predibase used? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on Predibase? * When is Predibase the wrong choice? * What do you have planned for the future of Predibase? Contact Info * LinkedIn * tgaddair on GitHub * @travisaddair on Twitter Parting Question * From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * Predibase * Horovod * Ludwig * Podcast.__init__ Episode * Support Vector Machine * Hadoop * Tensorflow * Uber Michaelangelo * AutoML * Spark ML Lib * Deep Learning * PyTorch * Continual * Data Engineering Podcast Episode * Overton * Kubernetes * Ray * Nvidia Triton * Whylogs * Data Engineering Podcast Episode * Weights and Biases * MLFlow * Comet * Confusion Matrices * dbt * Data Engineering Podcast Episode * Torchscript * Self-supervised Learning The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Preamble This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning. Summary Machine learning has the potential to transform industries and revolutionize business capabilities, but only if the models are reliable and robust. Because of the fundamental probabilistic nature of machine learning techniques it can be challenging to test and validate the generated models. The team at Deepchecks understands the widespread need to easily and repeatably check and verify the outputs of machine learning models and the complexity involved in making it a reality. In this episode Shir Chorev and Philip Tannor explain how they are addressing the problem with their open source deepchecks library and how you can start using it today to build trust in your machine learning applications. Announcements * Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. * Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft[™] is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you. * Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! * Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today! * Your host is Tobias Macey and today I’m interviewing Shir Chorev and Philip Tannor about Deepchecks, a Python package for comprehensively validating your machine learning models and data with minimal effort. Interview * Introduction * How did you get involved in machine learning? * Can you describe what Deepchecks is and the story behind it? * Who is the target audience for the project? * What are the biggest challenges that these users face in bringing ML models from concept to production and how does DeepChecks address those problems? * In the absence of DeepChecks how are practitioners solving the problems of model validation and comparison across iteratiosn? * What are some of the other tools in this ecosystem and what are the differentiating features of DeepChecks? * What are some examples of the kinds of tests that are useful for understanding the "correctness" of models? * What are the methods by which ML engineers/data scientists/domain experts can define what "correctness" means in a given model or subject area? * In software engineering the categories of tests are tiered as unit -> integration -> end-to-end. What are the relevant categories of tests that need to be built for validating the behavior of machine learning models? * How do model monitoring utilities overlap with the kinds of tests that you are building with deepchecks? * Can you describe how the DeepChecks package is implemented? * How have the design and goals of the project changed or evolved from when you started working on it? * What are the assumptions that you have built up from your own experiences that have been challenged by your early users and design partners? * Can you describe the workflow for an individual or team using DeepChecks as part of their model training and deployment lifecycle? * Test engineering is a deep discipline in its own right. How have you approached the user experience and API design to reduce the overhead for ML practitioners to adopt good practices? * What are the interfaces available for creating reusable tests and composing test suites together? * What are the additional services/capabilities that you are providing in your commercial offering? * How are you managing the governance and sustainability of the OSS project and balancing that against the needs/priorities of the business? * What are the most interesting, innovative, or unexpected ways that you have seen DeepChecks used? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on DeepChecks? * When is DeepChecks the wrong choice? * What do you have planned for the future of DeepChecks? Contact Info * Shir * LinkedIn * shir22 on GitHub * Philip * LinkedIn * @philiptannor on Twitter Parting Question * From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * DeepChecks * Random Forest * Talpiot Program * SHAP * Podcast.__init__ Episode * Airflow * Great Expectations * Data Engineering Podcast Episode The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Preamble This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning. Summary Building an ML model is getting easier than ever, but it is still a challenge to get that model in front of the people that you built it for. Baseten is a platform that helps you quickly generate a full stack application powered by your model. You can easily create a web interface and APIs powered by the model you created, or a pre-trained model from their library. In this episode Tuhin Srivastava, co-founder of Basten, explains how the platform empowers data scientists and ML engineers to get their work in production without having to negotiate for help from their application development colleagues. Announcements * Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host is Tobias Macey and today I’m interviewing Tuhin Srivastava about Baseten, an ML Application Builder for data science and machine learning teams Interview * Introduction * How did you get involved in machine learning? * Can you describe what Baseten is and the story behind it? * Who are the target users for Baseten and what problems are you solving for them? * What are some of the typical technical requirements for an application that is powered by a machine learning model? * In the absence of Baseten, what are some of the common utilities/patterns that teams might rely on? * What kinds of challenges do teams run into when serving a model in the context of an application? * There are a number of projects that aim to reduce the overhead of turning a model into a usable product (e.g. Streamlit, Hex, etc.). What is your assessment of the current ecosystem for lowering the barrier to product development for ML and data science teams? * Can you describe how the Baseten platform is designed? * How have the design and goals of the project changed or evolved since you started working on it? * How do you handle sandboxing of arbitrary user-managed code to ensure security and stability of the platform? * How did you approach the system design to allow for mapping application development paradigms into a structure that was accessible to ML professionals? * Can you describe the workflow for building an ML powered application? * What types of models do you support? (e.g. NLP, computer vision, timeseries, deep neural nets vs. linear regression, etc.) * How do the monitoring requirements shift for these different model types? * What other challenges are presented by these different model types? * What are the limitations in size/complexity/operational requirements that you have to impose to ensure a stable platform? * What is the process for deploying model updates? * For organizations that are relying on Baseten as a prototyping platform, what are the options for taking a successful application and handing it off to a product team for further customization? * What are the most interesting, innovative, or unexpected ways that you have seen Baseten used? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on Baseten? * When is Baseten the wrong choice? * What do you have planned for the future of Baseten? Contact Info * @tuhinone on Twitter * LinkedIn Parting Question * From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * Baseten * Gumroad * scikit-learn * Tensorflow * Keras * Streamlit * Podcast.__init__ Episode * Retool * Hex * Podcast.__init__ Episode * Kubernetes * React Monaco * Huggingface * Airtable * Dall-E 2 * GPT-3 * Weights and Biases The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary Starting a new project is always exciting and full of possibility, until you have to set up all of the repetitive boilerplate. Fortunately there are useful project templates that eliminate that drudgery. PyScaffold goes above and beyond simple template repositories, and gives you a toolkit for different application types that are packed with best practices to make your life easier. In this episode Florian Wilhelm shares the story behind PyScaffold, how the templates are designed to reduce friction when getting a new project off the ground, and how you can extend it to suit your needs. Stop wasting time with boring boilerplate and get straight to the fun part with PyScaffold! Announcements * Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great! * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing Florian Wilhelm about PyScaffold, a Python project template generator with batteries included Interview * Introductions * How did you get introduced to Python? * Can you describe what PyScaffold is and the story behind it? * What is the main goal of the project? * There are a huge number of templates and starter projects available (both in Python and other languages). What are the aspects of PyScaffold that might encourage someone to adopt it? * What are the different types/categories of applications that you are focused on supporting with the scaffolding? * For each category, what is your selection process for which dependencies to include? * How do you approach the work of keeping the various components up to date with community "best practices"? * Can you describe how PyScaffold is implemented? * How have the design and goals of the project changed since you first started it? * What is the user experience for someone bootstrapping a project with PyScaffold? * How can you adapt an existing project into the structure of a pyscaffold template? * Are there any facilities for updating a project started with PyScaffold to include patches/changes in the source template? * What are the most interesting, innovative, or unexpected ways that you have seen PyScaffold used? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on PyScaffold? * When is PyScaffold the wrong choice? * What do you have planned for the future of PyScaffold? Keep In Touch * Website * LinkedIn * FlorianWilhelm on GitHub * @florianwilhelm on Twitter Picks * Tobias * Daredevil TV series * Florian * The Peripheral Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * PyScaffold * Innovex * SAP * Cookiecutter * Pytest * Podcast Episode * Sphinx * pre-commit * Podcast Episode * Black * Flake8 * Podcast Episode * Poetry * Setuptools * mkdocs * ReStructured Text * Markdown * Setuptools-SCM * Hatch * Flit * Versioneer * Gource git visualization * MyPy Compiler * Rust Cargo The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Summary Application configuration is a deceptively complex problem. Everyone who is building a project that gets used more than once will end up needing to add configuration to control aspects of behavior or manage connections to other systems and services. At first glance it seems simple, but can quickly become unwieldy. Bruno Rocha created Dynaconf in an effort to provide a simple interface with powerful capabilities for managing settings across environments with a set of strong opinions. In this episode he shares the story behind the project, how its design allows for adapting to various applications, and how you can start using it today for your own projects. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great! * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing Bruno Rocha about Dynaconf, a powerful and flexible framework for managing your application’s configuration settings Interview * Introductions * How did you get introduced to Python? * Can you describe what Dynaconf is and the story behind it? * What are your main goals for Dynaconf? * What kinds of projects (e.g. web, devops, ML, etc.) are you focused on supporting with Dynaconf? * Settings management is a deceptively complex and detailed aspect of software engineering, with a lot of conflicting opinions about the "right way". What are the design philosophies that you lean on for Dynaconf? * Many engineers end up building their own frameworks for managing settings as their use cases and environments get increasingly complicated. What are some of the ways that those efforts can go wrong or become unmaintainable? * Can you describe how Dynaconf is implemented? * How have the design and goals of the project evolved since you first started it? * What is the workflow for getting started with Dynaconf on a new project? * How does the usage scale with the complexity of the host project? * What are some strategies that you recommend for integrating Dynaconf into an existing project that already has complex requirements for settings across multiple environments? * Secrets management is one of the most frequently under- or over-engineered aspects of application configuration. What are some of the ways that you have worked to strike a balance of making the "right way" easy? * What are some of the more advanced or under-utilized capabilities of Dynaconf? * What are the most interesting, innovative, or unexpected ways that you have seen Dynaconf used? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on Dynaconf? * When is Dynaconf the wrong choice? * What do you have planned for the future of Dynaconf? Keep In Touch * rochacbruno on GitHub * @rochacbruno on Twitter * Website * LinkedIn Picks * Tobias * SOPS * Bruno * Severance tv series * Learn Rust Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * Dynaconf * Dynaconf GitHub Org * Ansible * Bash * Perl * 12 Factor Applications * TOML * Hashicorp Vault * Pydantic * Airflow * Hydroconf The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Summary Software is eating the world, but that code has to have hardware to execute the instructions. Most people, and many software engineers, don’t have a proper understanding of how that hardware functions. Charles Petzold wrote the book "Code: The Hidden Language of Computer Hardware and Software" to make this a less opaque subject. In this episode he discusses what motivated him to revise that work in the second edition and the additional details that he packed in to explore the functioning of the CPU. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great! * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing Charles Petzold about his work on the second edition of Code: The Hidden Language of Computer Hardware and Software Interview * Introductions * How did you get introduced to Python? * Can you start by describing the focus and goal of "Code" and the story behind it? * Who is the target audience for the book? * The sequencing of the topics parallels the curriculum of a computer engineering course of study. Why do you think that it is useful/important for a general audience to understand the electrical engineering principles that underly modern computers? * What was your process for determining how to segment the information that you wanted to address in the book to balance the pacing of the reader with the density of the information? * Technical books are notoriously challenging to write due to the constantly changing subject matter. What are some of the ways that the first edition of "Code" was becoming outdated? * What are the most notable changes in the foundational elements of computing that have happened in the time since the first edition was published? * One of the concepts that I have found most helpful as a software engineer is that of "mechanical sympathy". What are some of the ways that a better understanding of computer hardware and electrical signal processing can influence and improve the way that an engineer writes code? * What are some of the insights that you gained about your own use of computers and software while working on this book? * What are the most interesting, unexpected, or challenging lessons that you have learned while writing "Code" and revising it for the second edition? * Once the reader has finished with your book, what are some of the other references/resources that you recommend? Keep In Touch * Website Picks * Tobias * The Imitation Game movie * Charles * The Annotated Turing book by Charles Petzold * Confidence Man: The Making of Donald Trump and the Breaking of America by Maggie Haberman Links * Code: The Hidden Language of Computer Hardware and Software * Fortran * PL/I * BASIC * C# * Z80 * Intel 8080 * PC Magazine * Assembly Language * Logic Gates * C Language * ASCII == American Standard Code for Information Interchange * SkiaSharp * Algol * Code first edition bibliography The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Summary The generation, distribution, and consumption of energy is one of the most critical pieces of infrastructure for the modern world. With the rise of renewable energy there is an accompanying need for systems that can respond in real-time to the availability and demand for electricity. FlexMeasures is an open source energy management system that is designed to integrate a variety of inputs intelligently allocate energy resources to reduce waste in your home or grid. In this episode Nicolas Höning explains how the project is implemented, how it is being used in his startup Seita, and how you can try it out for your own energy needs. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great! * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing Nicolas Höning about FlexMeasures, an open source project designed to manage energy resources dynamically to improve efficiency Interview * Introductions * How did you get introduced to Python? * Can you describe what FlexMeasures is and the story behind it? * What are the primary goals/objectives of the project? * The energy sector is huge. Where can FlexMeasures be used? * Energy systems are typically governed by a marketplace system. What are the benefits that FlexMeasures can provide for each side of that market? * How do renewable sources of energy confuse/complicate the role that the different stakeholders represent? * What are the different points of interaction that producers/consumers might have with the FlexMeasures platform? * What are some examples of the types of decisions/recommendations that FlexMeasures might generate and how to they manifest in the energy systems? * What are the types of information that FlexMeasures relies on for driving those decisions? * Can you describe how FlexMeasures is implemented? * How have the design and goals of the system changed/evolved since you started working on it? * What are the interfaces that you provide for integrating with and extending the functionality of a FlexMeasures installation? * What are the operating scales that FlexMeasures is designed for? * What are the most interesting, innovative, or unexpected ways that you have seen FlexMeasures used? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on FlexMeasures? * When is FlexMeasures the wrong choice? * What do you have planned for the future of FlexMeasures? Keep In Touch * Website * @nhoening on Twitter * LinkedIn Picks * Tobias * She-Hulk * Nicholas * Kleo on Netflix * Altair Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * FlexMeasures: * Github * Linux Energy Foundation * Mailing List * Twitter * EyeQuant * Energy Management System * OpenEMS * ICT == Information and Communications Technology * HomeAssistant * Podcast Episode * FlexMeasures HomeAssistant Plugin * Universal Smart Energy Framework * PostgreSQL * Data Engineering Podcast Episode * TimescaleDB * Data Engineering Podcast Episode * OpenWeatherMap * Timely-Beliefs library * Flask * Click * Pyomo * scikit-learn * sktime * LF Energy * Flake8 * MyPy * Podcast Episode * Black * Arima Model * Random Forest The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Summary Your ability to build and maintain a software project is tempered by the strength of the team that you are working with. If you are in a position of leadership, then you are responsible for the growth and maintenance of that team. In this episode Jigar Desai, currently the SVP of engineering at Sisu Data, shares his experience as an engineering leader over the past several years and the useful insights he has gained into how to build effective engineering teams. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great! * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * The biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Select Star’s data discovery platform solves that out of the box, with a fully automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day. Just connect it to your dbt, Snowflake, Tableau, Looker, or whatever you’re using and Select Star will set everything up in just a few hours. Go to pythonpodcast.com/selectstar today to double the length of your free trial and get a swag package when you convert to a paid plan. * Your host as usual is Tobias Macey and today I’m interviewing Jigar Desai about building effective engineering teams Interview * Introductions * How did you get introduced to Python? * What have you found to be the central challenges involved in building an effective engineering team? * What are the measures that you use to determine what "effective" means for a given team? * how to establish mutual trust in an engineering team * challenges introduced at different levels of team size/organizational complexity * establishing and managing career ladders * You have mostly worked in heavily tech-focused companies. How do industry verticals impact the ways that you think about formation and structure of engineering teams? * What are some of the different roles that you might focus on hiring/team compositions in industries that aren’t purely software? (e.g. fintech, logistics, etc.) * notable evolutions in engineering practices/paradigm shifts in the industry * What are some of the predictions that you have about how the future of engineering will look? * What impact do you think low-code/no-code solutions will have on the types of projects that code-first developers will be tasked with? * What are the most interesting, innovative, or unexpected ways that you have seen organizational leaders address the work of building and scaling engineering capacity? * What are the most interesting, unexpected, or challenging lessons that you have learned while working in engineering leadership? * What are the most informative mistakes that you would like to share? * What are some resources and reference material that you recommend for anyone responsible for the success of their engineering teams? Keep In Touch * LinkedIn Picks * Tobias * Bullet Train movie * Jigar * Top Gun Maverick movie Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * Sisu Data * OpenStack * Java The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Summary Static typing versus dynamic typing is one of the oldest debates in software development. In recent years a number of dynamic languages have worked toward a middle ground by adding support for type hints. Python’s type annotations have given rise to an ecosystem of tools that use that type information to validate the correctness of programs and help identify potential bugs. At Instagram they created the Pyre project with a focus on speed to allow for scaling to huge Python projects. In this episode Shannon Zhu discusses how it is implemented, how to use it in your development process, and how it compares to other type checkers in the Python ecosystem. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science. * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing Shannon Zhu about Pyre, a type checker for Python 3 built from the ground up to support gradual typing and deliver responsive incremental checks Interview * Introductions * How did you get introduced to Python? * Can you describe what Pyre is and the story behind it? * There have been a number of tools created to support various aspects of typing for Python. How would you describe the various goals that they support and how Pyre fits in that ecosystem? * What are the core goals and notable features of Pyre? * Can you describe how Pyre is implemented? * How have the design and goals of the project changed/evolved since you started working on it? * What are the different ways that Pyre is used in the development workflow for a team or individual? * What are some of the challenges/roadblocks that people run into when adopting type definitions in their Python projects? * How has the evolution of type annotations and overall support for them affected your work on Pyre? * As someone who is working closely with type systems, what are the strongest aspects of Python’s implementation and opportunities for improvement? * What are the most interesting, innovative, or unexpected ways that you have seen Pyre used? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pyre? * When is Pyre the wrong choice? * What do you have planned for the future of Pyre? Keep In Touch * shannonzhu on GitHub Picks * Tobias * Lord Of The Rings: The Rings of Power on Amazon Video * Shannon * King’s Dilemma board game Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * PYre * MyPy * Podcast Episode * PyRight * PyType * MonkeyType * Podcast Episode * Java * C * PEP 484 * Flow * Hack * Continuous Integration * OCaml * PEP 675 – Arbitrary literal strings * Gradual Typing * AST == Abstract Syntax Tree * Language Server Protocol * Tensor * Type Arithmetic * PyCon: Securing Code With The Python Type System * PyCon: Type Checked Python In The Real World * PyCon: Łukasz Lange 2022 Keynote The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Summary Every software project is subject to a series of decisions and tradeoffs. One of the first decisions to make is which programming language to use. For companies where their product is software, this is a decision that can have significant impact on their overall success. In this episode Sean Knapp discusses the languages that his team at Ascend use for building a service that powers complex and business critical data workflows. He also explains his motivation to standardize on Python for all layers of their system to improve developer productivity. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science. * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing Sean Knapp about his motivations and experiences standardizing on Python for development at Ascend Interview * Introductions * How did you get introduced to Python? * Can you describe what Ascend is and the story behind it? * How many engineers work at Ascend? * What are their different areas of focus? * What are your policies for selecting which technologies (e.g. languages, frameworks, dev tooling, deployment, etc.) are supported at Ascend? * What does it mean for a technology to be supported? * You recently started standardizing on Python as the default language for development. How has Python been used up to now? * What other languages are in common use at Ascend? * What are some of the challenges/difficulties that motivated you to establish this policy? * What are some of the tradeoffs that you have seen in the adoption of Python in place of your other adopted languages? * How are you managing ongoing maintenance of projects/products that are not written in Python? * What are some of the potential pitfalls/risks that you are guarding against in your investment in Python? * What are the most interesting, innovative, or unexpected ways that you have seen Python used where it was previously a different technology? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on aligning all of your development on a single language? * When is Python the wrong choice? * What do you have planned for the future of engineering practices at Ascend? Keep In Touch * LinkedIn * @seanknapp on Twitter Picks * Tobias * Delver Lens app for scanning Magic: The Gathering cards * Sean * Typer * DuckDB * Amp It Up book (affiliate link) Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * Ascend * Data Engineering Podcast Episode * Perl * Google Sawzall * Technical Debt * Ruby * gRPC * Go Language * Java * PySpark * Apache Arrow * Thrift * SQL * Scala * Snowflake runtime for Python Snowpark * Typer CLI framework * Pydantic * Podcast Episode * Pulumi * Podcast Episode * PyInfra * Podcast Episode * Packer * Plot.ly Dash * DuckDB The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Summary Writing code is only one piece of creating good software. Code reviews are an important step in the process of building applications that are maintainable and sustainable. In this episode On Freund shares his thoughts on the myriad purposes that code reviews serve, as well as exploring some of the patterns and anti-patterns that grow up around a seemingly simple process. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science. * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing On Freund about the intricacies and importance of code reviews Interview * Introductions * How did you get introduced to Python? * Can you start by giving us your description of what a code review is? * What is the purpose of the code review? * At face value a code review appears to be a simple task. What are some of the subtleties that become evident with time and experience? * What are some of the ways that code reviews can go wrong? * What are some common anti-patterns that get applied to code reviews? * What are the elements of code review that are useful to automate? * What are some of the risks/bad habits that can result from overdoing automated checks/fixes or over-reliance on those tools in code reviews? * identifying who can/should do a review for a piece of code * how to use code reviews as a teaching tool for new/junior engineers * how to use code reviews for avoiding siloed experience/promoting cross-training * PR templates for capturing relevant context * What are the most interesting, innovative, or unexpected ways that you have seen code reviews used? * What are the most interesting, unexpected, or challenging lessons that you have learned while leading and supporting engineering teams? * What are some resources that you recommend for anyone who wants to learn more about code review strategies and how to use them to scale their teams? Keep In Touch * LinkedIn * @onfreund on Twitter Picks * Tobias * The Girl Who Drank The Moon * On * Better Call Saul Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * Wilco * Code Review * Home Assistant * Podcast Episode * Trunk-based Development * Git Flow * Pair Programming * Feature Flags * Podcast Episode * KPI == Key Performance Indicator * MIT Open Learning Engineering Handbook * PEP Repository The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Summary Quality assurance in the software industry has become a shared responsibility in most organizations. Given the rapid pace of development and delivery it can be challenging to ensure that your application is still working the way it’s supposed to with each release. In this episode Jonathon Wright discusses the role of quality assurance in modern software teams and how automation can help. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science. * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing Jonathon Wright about the role of automation in your testing and QA strategies Interview * Introductions * How did you get introduced to Python? * Can you share your relationship with software testing/QA and automation? * What are the main categories of how companies and software teams address testing and validation of their applications? * What are some of the notable tradeoffs/challenges among those approaches? * With the increased adoption of agile practices and the "shift left" mentality of DevOps, who is responsible for software quality? * What are some of the cases where a discrete QA role or team becomes necessary? (or is it always necessary?) * With testing and validation being a shared responsibility, competing with other priorities, what role does automation play? * What are some of the ways that automation manifests in software quality and testing? * How is automation distinct from software tests and CI/CD? * For teams who are investing in automation for their applications, what are the questions they should be asking to identify what solutions to adopt? (what are the decision points in the build vs. buy equation?) * At what stage(s) of the software lifecycle does automation live? * What is the process for identifying which capabilities and interactions to target during the initial application of automation for QA and validation? * One of the perennial challenges with any software testing, particularly for anything in the UI, is that it is a constantly moving target. What are some of the patterns and techniques, both from a developer and tooling perspective, that increase the robustness of automated validation? * What are the most interesting, innovative, or unexpected ways that you have seen automation used for QA? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on QA and automation? * When is automation the wrong choice? * What are some of the resources that you recommend for anyone who wants to learn more about this topic? Keep In Touch * LinkedIn * @Jonathon_Wright on Twitter * Website Picks * Tobias * The Sandman Netflix series and Graphic Novels by Neil Gaimain * Jonathon * House of the Dragon HBO series * Mystic Quest TV series * It’s Always Sunny in Philadelphia Links * Haskell * Idris * Esperanto * Klingon * Planguage * Lisp Language * TDD == Test Driven Development * BDD == Behavior Driven Development * Gherkin Format * Integration Testing * Chaos Engineering * Gremlin * Chaos Toolkit * Podcast Episode * Requirements Engineering * Keysight * QA Lead Podcast * Cognitive Learning TED Talk * OpenTelemetry * Podcast Episode * Quality Engineering * Selenium * Swagger * XPath * Regular Expression * Test Guild The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Summary The goal of every software team is to get their code into production without breaking anything. This requires establishing a repeatable process that doesn’t introduce unnecessary roadblocks and friction. In this episode Ronak Rahman discusses the challenges that development teams encounter when trying to build and maintain velocity in their work, the role that access to infrastructure plays in that process, and how to build automation and guardrails for everyone to take part in the delivery process. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science. * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing Ronak Rahman about how automating the path to production helps to build and maintain development velocity Interview * Introductions * How did you get introduced to Python? * Can you describe what Quali is and the story behind it? * What are the problems that you are trying to solve for software teams? * How does Quali help to address those challenges? * What are the bad habits that engineers fall into when they experience friction with getting their code into test and production environments? * How do those habits contribute to negative feedback loops? * What are signs that developers and managers need to watch for that signal the need for investment in developer experience improvements on the path to production? * Can you describe what you have built at Quali and how it is implemented? * How have the design and goals shifted/evolved from when you first started working on it? * What are the positive and negative impacts that you have seen from the evolving set of options for application deployments? (e.g. K8s, containers, VMs, PaaS, FaaS, etc.) * Can you describe how Quali fits into the workflow of software teams? * Once a team has established patterns for deploying their software, what are some of the disruptions to their flow that they should guard against? * What are the most interesting, innovative, or unexpected ways that you have seen Quali used? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on Quali? * When is Quali the wrong choice? * What do you have planned for the future of Quali? Keep In Touch * @OfRonak on Twitter Picks * Tobias * The Terminal List on Amazon * Ronak * Midnight Gospel on Amazon Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * Quali * Torque * Visual Studio Plugin * Subversion * IaC == Infrastructure as Code * DevOps * Terraform * Pulumi * Podcast Episode * Cloudformation * Flask The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Summary Every startup begins with an idea, but that won’t get you very far without testing the feasibility of that idea. A common practice is to build a Minimum Viable Product (MVP) that addresses the problem that you are trying to solve and working with early customers as they engage with that MVP. In this episode Tony Pavlovych shares his thoughts on Python’s strengths when building and launching that MVP and some of the potential pitfalls that businesses can run into on that path. Announcements * Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science. * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing Tony Pavlovych about Python’s strengths for startups and the steps to building an MVP (minimum viable product) Interview * Introductions * How did you get introduced to Python? * Can you describe what PLANEKS is and the story behind it? * One of the services that you offer is building an MVP. What are the goals and outcomes associated with an MVP? * What is the process for identifying the product focus and feature scope? * What are some of the common misconceptions about building and launching MVPs that you have dealt with in your work with customers? * What are the common pitfalls that companies encounter when building and validating an MVP? * Can you describe the set of tools and frameworks (e.g. Django, Poetry, cookiecutter, etc.) that you have invested in to reduce the overhead of starting and maintaining velocity on multiple projects? * What are the configurations that are most critical to keep constant across projects to maintain familiarity and sanity for your developers? (e.g. linting rules, build toolchains, etc.) * What are the architectural patterns that you have found most useful to make MVPs flexible for adaptation and extension? * Once the MVP is built and launched, what are the next steps to validate the product and determine priorities? * What benefits do you get from choosing Python as your language for building an MVP/launching a startup? * What are the challenges/risks involved in that choice? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on MVPs for your clients at PLANEKS? * When is an MVP the wrong choice? * What are the developments in the Python and broader software ecosystem that you are most interested in for the work you are doing for your team and clients? Keep In Touch * LinkedIn Picks * Tobias * datamodel-code-generator * Tony * Screw It, Let’s Do It by Richard Branson (affiliate link) Closing Announcements * Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links * PLANEKS * Minimum Viable Product * Django * Cookiecutter * Django Boilerplate * OCR == Optical Character Recognition * Tesseract OCR framework The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA