The Python Podcast.init

By Tobias Macey

The podcast about Python and the people who make it great... more

4.4

100100 ratings

Download on the App Store

Download on the App Store

Get it on Google Play

FAQs about The Python Podcast.init:

How many episodes does The Python Podcast.init have?

The podcast currently has 667 episodes available.

The Python Podcast.init episodes:

December 12, 2022Update Your Model's View Of The World In Real Time With Streaming Machine Learning Using River
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 [email protected]) 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\_\_!
...more
1h 17min
December 05, 2022Declarative Machine Learning For High Performance Deep Learning Models With Predibase
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 [email protected]) 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
...more
1h
November 28, 2022Build Better Machine Learning Models With Confidence By Adding Validation With Deepchecks
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 [email protected]) 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
...more
48min
November 21, 2022Build A Full Stack ML Powered App In An Afternoon With Baseten
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 [email protected]) 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
...more
46min
November 07, 2022Skip Straight To The Fun Part Of Your Project With PyScaffold
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 [email protected]) 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
...more
58min
October 30, 2022Add Configuration Best Practices To Your Application In An Afternoon With Dynaconf
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 [email protected]) 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
...more
1h 4min
October 23, 2022Take A Tour Of The Hidden Language Of Hardware And How It Powers Your Code
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
...more
42min
October 16, 2022Take Control Of Your Electrical Systems With The Open Source FlexMeasures Energy Management System
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 [email protected]) 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
...more
50min
October 10, 2022How And Why To Build Effective Teams As An Engineering Leader
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 [email protected]) 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
...more
1h 5min
October 03, 2022Complete Your Hardware "Weekend Projects" In An Actual Weekend With Belay
Summary
Working on hardware projects often has significant friction involved when compared to pure software. Brian Pugh enjoys tinkering with microcontrollers, but his "weekend projects" often took longer than a weekend to complete, so he created Belay. In this episode he explains how Belay simplifies the interactions involved in developing for MicroPython boards and how you can use it to speed up your own experimentation.
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 Brian Pugh about Belay, a python library that enables the rapid development of projects that interact with hardware via a micropython-compatible board.
Interview
Introductions
How did you get introduced to Python?
Can you describe what Belay is and the story behind it?
Who are the target users for Belay?
What are some of the points of friction involved in developing for hardware projects?
What are some of the features of Belay that make that a smoother process?
What are some of the ways that simplifying the develop/debug cycles can improve the overall experience of developing for hardware platforms?
What are some of the inherent limitations of constrained hardware that Belay is unable to paper over?
Can you describe how Belay is implemented?
What does the workflow look like when using Belay as compared to using MicroPython directly?
What are some of the ways that you are using Belay in your own projects?
What are the most interesting, innovative, or unexpected ways that you have seen Belay used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Belay?
When is Belay the wrong choice?
What do you have planned for the future of Belay?
Keep In Touch
BrianPugh on GitHub
LinkedIn
Picks
Tobias
Gunnar Computer Glasses
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 [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
Belay
Geomagical
PIC Microcontroller
AVR Microcontroller
Matlab
MicroPython
Podcast Episode
CircuitPython
Podcast Episode
Celery
Potentiometer
Raspberry Pi
Raspberry Pi Pico
ADC Converter
Thonny
Podcast Episode
Adafruit
Pyboard
Python Inspect Module
Python Tokenize
Magnetometer Project
Lidar
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
...more
49min

FAQs about The Python Podcast.init:

How many episodes does The Python Podcast.init have?

The podcast currently has 667 episodes available.

More shows like The Python Podcast.init

The Changelog: Software Development, Open Source by Changelog Media

The Changelog: Software Development, Open Source

277 Listeners

Mac Power Users by Relay FM

Mac Power Users

1,274 Listeners

LINUX Unplugged by Jupiter Broadcasting

LINUX Unplugged

258 Listeners

Data Skeptic by Kyle Polich

Data Skeptic

469 Listeners

Talk Python To Me by Michael Kennedy

Talk Python To Me

582 Listeners

Software Engineering Daily by Software Engineering Daily

Software Engineering Daily

630 Listeners

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) by Sam Charrington

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

428 Listeners

Super Data Science: ML & AI Podcast with Jon Krohn by Jon Krohn

Super Data Science: ML & AI Podcast with Jon Krohn

291 Listeners

Python Bytes by Michael Kennedy and Brian Okken

Python Bytes

213 Listeners

Data Engineering Podcast by Tobias Macey

Data Engineering Podcast

134 Listeners

JS Party: JavaScript, CSS, Web Development by Changelog Media

JS Party: JavaScript, CSS, Web Development

91 Listeners

Syntax - Tasty Web Development Treats by Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers

Syntax - Tasty Web Development Treats

973 Listeners

DataFramed by DataCamp

DataFramed

271 Listeners

Practical AI: Machine Learning, Data Science, LLM by Changelog Media

Practical AI: Machine Learning, Data Science, LLM

168 Listeners

The Real Python Podcast by Real Python

The Real Python Podcast

140 Listeners