The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

November 26, 2019Faster And Safer Software Development With Feature Flags
Summary
Any software project that is worked on or used by multiple people will inevitably reach a point where certain capabilities need to be turned on or off. In this episode Pete Hodgson shares his experience and insight into when, how, and why to use feature flags in your projects as a way to enable that practice. In addition to the simple on and off controls for certain logic paths, feature toggles also allow for more advanced patterns such as canary releases and A/B testing. This episode has something useful for anyone who works on software in any language.
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 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Pete Hodgson about the concept of feature flags and how they can benefit your development workflow
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what a feature flag is?
What was your first experience with feature flags and how did it affect your approach to software development?
What are some of the ways that feature flags are used?
What are some antipatterns that you have seen for teams using feature flags?
What are some of the alternative development practices that teams will employ to achieve the same or similar outcomes to what is possible with feature flags?
Can you describe some of the different approaches to implementing feature flags in an application?
What are some of the common pitfalls or edge cases that teams run into when building an in-house solution?
What are some useful considerations when making a build vs. buy decision for a feature toggling service?
What are some of the complexities that get introduced by feature flags for mantaining application code over the long run?
What have you found to be useful or effective strategies for cataloging and documenting feature toggles in an application, particularly if they are long lived or for open source applications where there is no institutional context?
Can you describe some of the lifecycle considerations for feature flags, and how the design, implementation, or use of them changes for short-lived vs long-lived use cases?
What are some cases where the overhead of implementing and maintaining a feature flag infrastructure outweighs the potential benefit?
What advice or references do you recommend for anyone who is interested in using feature flags for their own work?
Keep In Touch
Website
@ph1 on Twitter
moredip on GitHub
Picks
Tobias
Circuit Playground Express
CircuitPython Episode
Pete
Accelerate by Nicole Forsgren, Jez Humble, and Gene Kim
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
Perl
Ruby
Django
Feature Flag
Pete’s Blog Post On Feature Flags
Thoughtworks
Continuous Delivery
Continuous Delivery Book
Trunk Based Development
Branch By Abstraction
Technical Debt
Strategy Pattern
Polymorphism
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr 2 min
November 18, 2019 From Simple Script To Beautiful Web Application With Streamlit
Building well designed and easy to use web applications requires a significant amount of knowledge and experience across a range of domains. This can act as an impediment to engineers who primarily work in so-called back-end technologies such as machine learning and systems administration. In this episode Adrien Treuille describes how the Streamlit framework empowers anyone who is comfortable writing Python scripts to create beautiful applications to share their work and make it accessible to their colleagues and customers. If you have ever struggled with hacking together a simple web application to make a useful script self-service then give this episode a listen and then go experiment with how Streamlit can level up your work.
50 min
November 18, 2019From Simple Script To Beautiful Web Application With Streamlit
Summary
Building well designed and easy to use web applications requires a significant amount of knowledge and experience across a range of domains. This can act as an impediment to engineers who primarily work in so-called back-end technologies such as machine learning and systems administration. In this episode Adrien Treuille describes how the Streamlit framework empowers anyone who is comfortable writing Python scripts to create beautiful applications to share their work and make it accessible to their colleagues and customers. If you have ever struggled with hacking together a simple web application to make a useful script self-service then give this episode a listen and then go experiment with how Streamlit can level up your work.
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 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
Having all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at pythonpodcast.com/chaossearch and don’t forget to thank them for supporting the show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Adrien Treuille about Streamlit, an open source app framework built for machine learning and data science teams
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what Streamlit is and its origin story?
What are some of the types of applications that are commonly built by data teams and who are the typical consumers of those projects?
What are some of the challenges or complications that are unique to this problem space?
What are some of the complications or challenges that you have faced to integrate Streamlit with so many different machine learning frameworks?
Can you describe the technical implementation of Streamlit and how it has evolved since you began working on it?
How did you approach the design of the API and development workflow to tailor it for the needs and capabilities of machine learning engineers?
If you were to start the project from scratch today what would you do differently?
What is a typical workflow for someone working on a machine learning application and how does Streamlit fit in?
What are some of the types of tools or processes that it replaces?
What are some of the most interesting or unexpected ways that you have seen Streamlit used?
What have you found to be some of the most challenging or unexpected aspects of building and evolving Streamlit?
How do you see Python evolving in light of Streamlit and other work in the machine learning space?
What do you have in store for the future of Streamlit or any adjacent products and services?
How are you approaching the governance and sustainability of the Streamlit open source project?
Keep In Touch
Website
LinkedIn
@myelbows on Twitter
treuille on GitHub
Picks
Tobias
The Book Of Why by Judea Pearl
Adrien
No Self, No Problem by Anam Thubten
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
Streamlit
Forum
GitHub
Twitter
Carnegie Mellon University
Google X
Zoox
IBM
Cornell University
NumPy
SciPy
Machine Learning Engineer
Jupyter
DeckGL
Matplotlib
Plotly
Seaborn
Altair
PyTorch
Tensorflow
Protocol Buffers
Streamlit for teams
Heroku
EC2
React JS
Awesome Streamlit
Flask
Plotly Dash
Voila
NeurIPS
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
50 min
November 11, 2019 Automate Your Server Security With GrapheneX
The internet is rife with bots and bad actors trying to compromise your servers. To counteract these threats it is necessary to diligently harden your systems to improve server security. Unfortunately, the hardening process can be complex or confusing. In this week's episode 18 year old Orhun Parmaksiz shares the story of how he and his friends created the GrapheneX framework to simplify the process of securing and maintaining your servers using the power and flexibility of Python. If you run your own software then this is definitely worth a listen.
36 min
November 11, 2019Automate Your Server Security With GrapheneX
Summary
The internet is rife with bots and bad actors trying to compromise your servers. To counteract these threats it is necessary to diligently harden your systems to improve server security. Unfortunately, the hardening process can be complex or confusing. In this week’s episode 18 year old Orhun Parmaksiz shares the story of how he and his friends created the GrapheneX framework to simplify the process of securing and maintaining your servers using the power and flexibility of Python. If you run your own software then this is definitely worth a listen.
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 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
Having all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at pythonpodcast.com/chaossearch and don’t forget to thank them for supporting the show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Orhun Parmaksiz about GrapheneX, a framework for simplifying the process of hardening your servers
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what we mean when we talk about hardening of servers?
What are the common ways of hardening a system, which techniques can we use for this purpose?
What are some of the high level categories of threats that operators should be considering?
What is GrapheneX and what was your motivation for creating it?
How does GrapheneX aid users in the process of increasing the security of their infrastructure?
Is any extra operating system knowledge required for using GrapheneX?
Can you talk through the workflow for someone using GrapheneX to harden their systems?
What options does it support for managing deployment across a fleet of servers?
Some security controls can actually prevent proper operation of the applications and services that are deployed on a server. How do you approach preventing those scenarios or educating the users in determining which controls are appropriate?
Why did you choose Python for a project like GrapheneX?
How is GrapheneX implemented?
How has the design evolved since you first began working on it?
If you were to start the project over today, what would you do differently?
Do you accept contributions to the framework? If so, what kind of contributions are needed for improving GrapheneX?
For someone who is interested in adding a new module to the framework, what is involved?
What have you found to be the most interesting or challenging aspects of your work on GrapheneX?
What, if any, aspects of server security have you consciously avoided implementing in GrapheneX?
What are your future plans about the GrapheneX?
Keep In Touch
Orhun
GitHub
Twitter
LinkedIn
Picks
Tobias
Chess
Orhun
Creeping in My Soul by Cryoshell
Gravity Hurts by Cryoshell
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
GrapheneX
GitHub
Website
PyPI
Twitter
Trello
Graphene
New Modules for GNU/Linux & Windows (Issue)
Flask
Flask-SocketIO
React
trimstray/linux-hardening-checklist
The Windows Server Hardening Checklist
Firewall
Windows Firewall
Linux iptables
PCI-DSS 2.2 requirement- server hardening standards
CIS Benchmarks
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
36 min
November 03, 2019 Accelerating The Adoption Of Python At Wayfair
Large companies often have a variety of programming languages and technologies being used across departments to keep the business running. Python has been gaining ground in these environments because of its flexibility, ease of use, and developer productivity. In order to accelerate the rate of adoption at Wayfair this week's guest Jonathan Biddle started a team to work with other engineering groups on their projects and show them how best to take advantage of the benefits of Python. In this episode he explains their operating model, shares their success stories, and provides advice on the pitfalls to avoid if you want to follow in his footsteps. This is definitely worth a listen if you are using Python in your work or would like to aid in its adoption.
43 min
November 03, 2019Accelerating The Adoption Of Python At Wayfair
Summary
Large companies often have a variety of programming languages and technologies being used across departments to keep the business running. Python has been gaining ground in these environments because of its flexibility, ease of use, and developer productivity. In order to accelerate the rate of adoption at Wayfair this week’s guest Jonathan Biddle started a team to work with other engineering groups on their projects and show them how best to take advantage of the benefits of Python. In this episode he explains their operating model, shares their success stories, and provides advice on the pitfalls to avoid if you want to follow in his footsteps. This is definitely worth a listen if you are using Python in your work or would like to aid in its adoption.
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 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
Having all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at pythonpodcast.com/chaossearch and don’t forget to thank them for supporting the show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Jonathan Biddle about his work to encourage and empower Wayfair engineers in their use of Python
Interview
Introductions
How did you get introduced to Python?
Can you start by describing the mission statement for you and your team at Wayfair?
What is the origin story for how your group got started?
How and where was Python being used within Wayfair at the time?
What are the primary languages that are used throughout Wayfair?
What is involved in the selection process for a language and technology stack for new projects within Wayfair?
Can you describe how and why you work with different groups throughout Wayfair?
What are some of the common misconceptions or barriers that you encounter when working with other engineering and product teams about how and where Python will be useful?
How large is your team currently and what is the length of a typical engagement?
How has the scale and scope of your work changed since your group was first formed?
How many different product teams have you worked with at this point and what are some of the notable outcomes?
What are some of the most challenging aspects, both technical and organizational, of educating other engineers on when and how to use Python?
Can you share some examples of engagements that you would classify as a failure?
What lessons have you learned from those situations?
What advice do you have for other groups or organizations who may be considering or actively launching similar initiatives?
Keep In Touch
Website
LinkedIn
@jonbiddle on Twitter
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Picks
Tobias
Learning Bayesian Statistics Podcast
Jonathan
PyDantic
FastAPI
MKDocs
Links
Wayfair
Zope
Django
PHP
Java
Javascript
.NET
Kafka
Jack Diederich – Stop Writing Classes
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
43 min
October 29, 2019 Building Quantum Computing Algorithms In Python
Quantum computers are the biggest jump forward in processing power that the industry has seen in decades. As part of this revolution it is necessary to change our approach to algorithm design. D-Wave is one of the companies who are pushing the boundaries in quantum processing and they have created a Python SDK for experimenting with quantum algorithms. In this episode Alexander Condello explains what is involved in designing and implementing these algorithms, how the Ocean SDK helps you in that endeavor, and what types of problems are well suited to this approach.
37 min
October 29, 2019Building Quantum Computing Algorithms In Python
Summary
Quantum computers are the biggest jump forward in processing power that the industry has seen in decades. As part of this revolution it is necessary to change our approach to algorithm design. D-Wave is one of the companies who are pushing the boundaries in quantum processing and they have created a Python SDK for experimenting with quantum algorithms. In this episode Alexander Condello explains what is involved in designing and implementing these algorithms, how the Ocean SDK helps you in that endeavor, and what types of problems are well suited to this approach.
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 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Alex Condello about the Ocean SDK from D-Wave for building quantum algorithms in Python
Interview
Introductions
How did you get introduced to Python?
Can you start by giving a high-level overview of quantum computing?
What is the Ocean SDK and how does it fit into the business model for D-Wave?
What are some of the problem types that a quantum processor is uniquely well suited for?
How does the overall system design for a quantum computer compare to that of the Von Neumann architecture that is common for the machines that we are all familiar with?
What are some of the differences in algorithm design when programming for a quantum processor?
Is there any specialized background knowledge that is necessary for making effective use of the QPU’s capabilities?
What are some of the common difficulties that you have seen users struggle with?
How does the Ocean SDK assist the developer in implementing and understanding the patterns necessary for Quantum algorithms?
What was the motivation for choosing Python as the target language for an SDK to attract developers to experiment with quantum algorithms?
Can you describe how the SDK is implemented and some of the integrations that are necessary for being able to operate on a quantum processor?
What have you found to be some of the most interesting, challenging, or unexpected aspects of your work on the Ocean software stack?
How do you handle the abstraction of the execution context to allow for replicating the program behavior on CPU/GPU vs QPU
Is there any potential for quantum computing to impact research in previously intractable computer science research, such as the P vs NP problem?
What are your current scaling limits in terms of providing compute to customers for their problems?
What are some of the most interesting, innovative, or unexpected ways that you have seen developers use the Ocean SDK and quantum processors?
What are you most excited for as you look to the future capabilities of quantum systems?
What are some of the upcoming challenges that you anticipate for the quantum computing industry?
Keep In Touch
arcondello on GitHub
Picks
Tobias
QuTip Podcast Interview
Alex
Cython
Podcast Interview
Links
Ocean SDK
D-Wave
Quantum Computing
Quantum Annealing
Quantum Superposition
Qubit
D-Wave Leap
Von Neumann Architecture
Cuda
Linear Programming
D-Wave ML Papers
D-Wave NetworkX
Maximum Cut Problem
Ising Problem
Los Alamos National Laboratory
Vertex Cover Problem
D-Wave Hybrid
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
37 min
October 22, 2019 Illustrating The Landscape And Applications Of Deep Learning
Deep learning is a phrase that is used more often as it continues to transform the standard approach to artificial intelligence and machine learning projects. Despite its ubiquity, it is often difficult to get a firm understanding of how it works and how it can be applied to a particular problem. In this episode Jon Krohn, author of Deep Learning Illustrated, shares the general concepts and useful applications of this technique, as well as sharing some of his practical experience in using it for his work. This is definitely a helpful episode for getting a better comprehension of the field of deep learning and when to reach for it in your own projects.
57 min