The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

June 04, 2019Building A Business On Serverless Technology
Summary
Serverless computing is a recent category of cloud service that provides new options for how we build and deploy applications. In this episode Raghu Murthy, founder of DataCoral, explains how he has built his entire business on these platforms. He explains how he approaches system architecture in a serverless world, the challenges that it introduces for local development and continuous integration, and how the landscape has grown and matured in recent years. If you are wondering how to incorporate serverless platforms in your projects then this is definitely worth your time to listen to.
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!
And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
Bots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of 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, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
The Python Software Foundation is the lifeblood of the community, supporting all of us who want to run workshops and conferences, run development sprints or meetups, and ensuring that PyCon is a success every year. They have extended the deadline for their 2019 fundraiser until June 30th and they need help to make sure they reach their goal. Go to pythonpodcast.com/psf2019 today to make a donation. If you’re listening to this after June 30th of 2019 then consider making a donation anyway!
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
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
Your host as usual is Tobias Macey and today I’m interviewing Raghu Murthy from DataCoral about his experience building and deploying a personalized SaaS platform on top of serverless technologies
Interview
Introductions
How did you get introduced to Python?
Can you start by giving a brief overview of DataCoral?
Before we get too deep can you share your definition of what types of technologies fall under the umbrella of "serverless"?
How are you using serverless technologies at DataCoral?
How has your usage evolved as your business and the underlying technologies have evolved?
How do serverless technologies impact your approach to application architecture?
What are some of the main benefits for someone to target services such as Lambda?
What is your litmus test for determining whether a given project would be a good fit for a Function as a Service platform?
What are the most challenging aspects of running code on Lambda?
What are some of the major design differences between running on Lambda vs the more familiar server-oriented paradigms?
What are some of the other services that are most commonly used alongside Function as as Service (e.g. Lambda) to build full featured applications?
With serverless function platforms there is the cold start problem, can you explain what that means and some application design patterns that can help mitigate it?
When building on cloud-based technologies, especially proprietary ones, local development can be a challenge. How are you handling that issue at DataCoral?
In addition to development this new deployment paradigm upends some of the traditional approaches to CI/CD. How are you approaching testing and deployment of your services?
How do you identify and maintain dependency graphs between your various microservices?
In addition to deployment, it is also necessary to track performance characteristics and error events across service boundaries. How are you managing observability and alerting in your product?
What are you most excited for in the serverless space that listeners should know about?
Keep In Touch
LinkedIn
Medium
Picks
Tobias
Avengers Endgame
Raghu
Golden State Warriors
Links
DataCoral
Data Engineering Podcast Interview
Perl
Airflow
Podcast Interview
Serverless Computing
DynamoDB
Aurora
SNS
SQS
Lambda
S3
API Gateway
EMR
Apache Hive
AWS Glue
RedShift
SnowflakeDB
Hadoop
Function As A Service
Distributed Systems
Conway’s Law
SRE == Site Reliability Engineer
Rollbar
AWS Batch
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
48 min
May 27, 2019 A Data Catalog For Your PyData Projects
One of the biggest pain points when working with data is getting is dealing with the boilerplate code to load it into a usable format. Intake encapsulates all of that and puts it behind a single API. In this episode Martin Durant explains how to use the Intake data catalogs for encapsulating source information, how it simplifies data science workflows, and how to incorporate it into your projects. It is a lightweight way to enable collaboration between data engineers and data scientists in the PyData ecosystem.
51 min
May 27, 2019A Data Catalog For Your PyData Projects
Summary
One of the biggest pain points when working with data is getting is dealing with the boilerplate code to load it into a usable format. Intake encapsulates all of that and puts it behind a single API. In this episode Martin Durant explains how to use the Intake data catalogs for encapsulating source information, how it simplifies data science workflows, and how to incorporate it into your projects. It is a lightweight way to enable collaboration between data engineers and data scientists in the PyData ecosystem.
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, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
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
Your host as usual is Tobias Macey and today I’m interviewing Martin Durant about Intake, a lightweight package for finding, investigating, loading and disseminating data
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what Intake is and the story behind its creation?
Can you outline some of the other projects and products that intersect with the functionality of Intake and describe where it fits in terms of use case and capabilities? (e.g. Quilt Data, Arrow, Data Retriever)
Can you describe the workflows for using Intake, both from the data scientist and the data engineer perspective?
One of the persistent challenges in working with data is that of cataloging and discovery of what already exists. In what ways does Intake address that problem?
Does it have any facilities for capturing and exposing data lineage?
For someone who needs to customize their usage of Intake, what are the extension points and what is involved in building a plugin?
Can you describe how Intake is implemented and how it has evolved since it first started?
What are some of the most challenging, complex, or novel aspects of the Intake implementation?
Intake focuses primarily on integrating with the PyData ecosystem (e.g. NumPy, Pandas, SciPy, etc.). What are some other communities that are, or could be, benefiting from the work being done on Intake?
What are some of the assumptions that are baked into Intake that would need to be modified to make it more broadly applicable?
What are some of the assumptions that were made going into this project that have needed to be reconsidered after digging deeper into the problem space?
What are some of the most interesting/unexpected/innovative ways that you have seen Intake leveraged?
What are your plans for the future of Intake?
Keep In Touch
martindurant on GitHub
Website
@martin_durant_ on Twitter
Picks
Tobias
Ubersuggest SEO tool
Links
Intake
Anaconda
Dask
Data Engineering Podcast Interview
Fast Parquet
IDL
Space Telescope Institute
Blaze
Quilt Data
Podcast Interview
Arrow
Data Retriever
Podcast Interview
Parquet
Data Engineering Podcast Interview
DataFrame
Apache Spark
Dremio
Data Engineering Podcast Interview
Dat Project – distributed peer-to-peer data sharing
Data Engineering Podcast Interview
GeoPandas
XArray
Solr
Streamz
PyViz
S3FS
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
51 min
May 20, 2019 Hardware Hacking Made Easy With CircuitPython
Learning to program can be a frustrating process, because even the simplest code relies on a complex stack of other moving pieces to function. When working with a microcontroller you are in full control of everything so there are fewer concepts that need to be understood in order to build a functioning project. CircuitPython is a platform for beginner developers that provides easy to use abstractions for working with hardware devices. In this episode Scott Shawcroft explains how the project got started, how it relates to MicroPython, some of the cool ways that it is being used, and how you can get started with it today. If you are interested in playing with low cost devices without having to learn and use C then give this a listen and start tinkering!
55 min
May 20, 2019Hardware Hacking Made Easy With CircuitPython
Summary
Learning to program can be a frustrating process, because even the simplest code relies on a complex stack of other moving pieces to function. When working with a microcontroller you are in full control of everything so there are fewer concepts that need to be understood in order to build a functioning project. CircuitPython is a platform for beginner developers that provides easy to use abstractions for working with hardware devices. In this episode Scott Shawcroft explains how the project got started, how it relates to MicroPython, some of the cool ways that it is being used, and how you can get started with it today. If you are interested in playing with low cost devices without having to learn and use C then give this a listen and start tinkering!
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, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
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
Your host as usual is Tobias Macey and today I’m interviewing Scott Shawcroft about CircuitPython, the easiest way to program microcontrollers
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what CircuitPython is and how the project got started?
I understand that you work at Adafruit and I know that a number of their products support CircuitPython. What other runtimes do you support?
Microcontrollers have typically been the domain of C because of the resource and performance constraints. What are the benefits of using Python to program hardware devices?
With the wide availability of powerful computing platforms, what are the benefits of experimenting with microcontrollers and their peripherals?
I understand that CircuitPython is a friendly fork of MicroPython. What have you changed in your version?
How do you structure your development to avoid conflicts with the upstream project?
What are some changes that you have contributed back to MicroPython?
What are some of the features of CircuitPython that make it easier for users to interact with sensors, motors, etc.?
CircuitPython provides an easy on-ramp for experimenting with hardware projects. Is there a point where a user will outgrow it and need to move to a different language or framework?
What are some of the most interesting/innovative/unexpected projects that you have seen people build using CircuitPython?
Are there any cases of someone building and shipping a production grade project in CircuitPython?
What have been some of the most interesting/challenging/unexpected aspects of building and maintaining CircuitPython?
What is in store for the future of the project?
Keep In Touch
@tannewt on Twitter
Website
tannewt on GitHub
Picks
Tobias
Wings Of Fire book series
Scott
Brandon Sanderson
The Wheel Of Time Series
Mist Born
Links
Adafruit
CircuitPython
MicroPython
Podcast Interview
Microcontroller
Arduino
Microsoft MakeCode
NodeBots
Espruino
I2C
Hackspace Magazine
Adafruit Blinka
learn.adafruit.com
Scott Hanselman Blog Post
Reflow Oven
Adafruit Crickit – Creative Robotics Platform
Adabox
Moore’s Law
SparkFun
DigiKey
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
55 min
May 13, 2019 Building A Privacy Preserving Voice Assistant
Being able to control a computer with your voice has rapidly moved from science fiction to science fact. Unfortunately, the majority of platforms that have been made available to consumers are controlled by large organizations with little incentive to respect users' privacy. The team at Snips are building a platform that runs entirely off-line and on-device so that your information is always in your control. In this episode Adrien Ball explains how the Snips architecture works, the challenges of building a speech recognition and natural language understanding toolchain that works on limited resources, and how they are tackling issues around usability for casual consumers. If you have been interested in taking advantage of personal voice assistants, but wary of using commercially available options, this is definitely worth a listen.
57 min
May 13, 2019Building A Privacy Preserving Voice Assistant
Summary
Being able to control a computer with your voice has rapidly moved from science fiction to science fact. Unfortunately, the majority of platforms that have been made available to consumers are controlled by large organizations with little incentive to respect users’ privacy. The team at Snips are building a platform that runs entirely off-line and on-device so that your information is always in your control. In this episode Adrien Ball explains how the Snips architecture works, the challenges of building a speech recognition and natural language understanding toolchain that works on limited resources, and how they are tackling issues around usability for casual consumers. If you have been interested in taking advantage of personal voice assistants, but wary of using commercially available options, 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!
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, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
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
Your host as usual is Tobias Macey and today I’m interviewing Adrien Ball about SNIPS, a set of technologies to make voice controlled systems that respect user’s privacy
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what the Snips is and how it got started?
For someone who wants to use Snips can you talk through the onboarding proces?
One of the interesting features of your platform is the option for automated training data generation. Can you explain how that works?
Can you describe the overall architecture of the Snips platform and how it has evolved since you first began working on it?
Two of the main components that can be used independently are the ASR (Automated Speech Recognition) and NLU (Natural Language Understanding) engines. Each of those have a number of competitors in the market, both open source and commercial. How would you describe your overall position in the market for each of those projects?
I know that one of the biggest challenges in conversational interfaces is maintaining context for multi-step interactions. How is that handled in Snips?
For the NLU engine, you recently ported it from Python to Rust. What was your motivation for doing so and how would you characterize your experience between the two languages?
Are you continuing to maintain both implementations and if so how are you maintaining feature parity?
How do you approach the overall usability and user experience, particularly for non-technical end users?
How is discoverability handled (e.g. finding out what capabilities/skills are available)
One of the compelling aspects of Snips is the ability to deploy to a wide variety of devices, including offline support. Can you talk through that deployment process, both from a user perspective and how it is implemented under the covers?
What is involved in updating deployed models and keeping track of which versions are deployed to which devices?
What is involved in adding new capabilities or integrations to the Snips platform?
What are the limitations of running everything offline and on-device?
When is Snips the wrong choice?
In the process of building and maintaining the various components of Snips, what have been some of the most useful/interesting/unexpected lessons that you have learned?
What have been the most challenging aspects?
What are some of the most interesting/innovative/unexpected ways that you have seen the Snips technologies used?
What is in store for the future of Snips?
Keep In Touch
LinkedIn
adrienball on GitHub
@adrien_ball on Medium
@adrien_ball on Twitter
Picks
Tobias
Chrome OS
Adrien
Google I/O
Facebook F8
User Privacy
Links
Snips
2048 Game
Smart Cities
Raspberry Pi
WikiData
MQTT
Google Assistant
Amazon Alexa
Microsoft Cortana
Mozilla Common Voice
Rust Language
Snips Hermes messaging library
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
57 min
May 07, 2019 Hacking The Government With The USDS
The U.S. government has a vast quantity of software projects across the various agencies, and many of them would benefit from a modern approach to development and deployment. The U.S. Digital Services Agency has been tasked with making that happen. In this episode the current director of engineering for the USDS, David Holmes, explains how the agency operates, how they are using Python in their efforts to provide the greatest good to the largest number of people, and why you might want to get involved. Even if you don't live in the U.S.A. this conversation is worth listening to so you can see an interesting model of how to improve government services for everyone.
35 min
May 07, 2019Hacking The Government With The USDS
Summary
The U.S. government has a vast quantity of software projects across the various agencies, and many of them would benefit from a modern approach to development and deployment. The U.S. Digital Services Agency has been tasked with making that happen. In this episode the current director of engineering for the USDS, David Holmes, explains how the agency operates, how they are using Python in their efforts to provide the greatest good to the largest number of people, and why you might want to get involved. Even if you don’t live in the U.S.A. this conversation is worth listening to so you can see an interesting model of how to improve government services for everyone.
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!
Bots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of 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, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
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
Your host as usual is Tobias Macey and today I’m interviewing David Holmes about his work at the US Digital Services organization
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what the USDS is and how you got involved with it?
The terminology that is used around "Tours of Service" is interesting. Can you explain what that entails?
relocation
what if you have a house and career?
Can you explain the model of how the USDS works?
What is involved in staffing a new project?
What is your typical toolkit, and how does that vary with the specific departments that you are working with?
What are some of the most interesting projects that you and the team at USDS have worked on?
What are some of the most challenging projects that you have been involved with?
What are some projects that you hope to be asked to work on?
Keep In Touch
davideholmes on GitHub
Picks
Tobias
Captain Marvel movie
David
Avengers: Endgame
Game Of Thrones television series
Links
US Digital Services
US Digital Services Job Application
US Digital Services Projects
18F
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
35 min
April 29, 2019 Probabilistic Modeling In Python (And What That Even Means)
Most programming is deterministic, relying on concrete logic to determine the way that it operates. However, there are problems that require a way to work with uncertainty. PyMC3 is a library designed for building models to predict the likelihood of certain outcomes. In this episode Thomas Wiecki explains the use cases where Bayesian statistics are necessary, how PyMC3 is designed and implemented, and some great examples of how it is being used in real projects.
55 min