The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

January 15, 2022An Exploration Of Effective Pandas Practices With Matt Harrison
Summary
Pandas has grown to be a ubiquitous tool for working with data at every stage. It has become so well known that many people learn Python solely for the purpose of using Pandas. With all of this activity and the long history of the project it can be easy to find misleading or outdated information about how to use it. In this episode Matt Harrison shares his work on the book "Effective Pandas" and some of the best practices and potential pitfalls that you should know for applying Pandas in your own work.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of 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. 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 Matt Harrison about best practices for using Pandas for data exploration, manipulation, and analysis
Interview
Introductions
How did you get introduced to Python?
What motivated you to write a book about Pandas?
There are a number of books available that cover some aspect of the Pandas framework or its application. What was missing from the available literature?
Who is your target audience for this book?
What are some of the most surprising things that you have learned about Pandas while working on this book?
What are the sharp edges that you see newcomers to pandas run into most frequently?
It is easy to use Pandas in a naive manner and get things done. What are some of the bad habits that you have seen people form in their work with Pandas?
How and when do those habits become harmful?
What are the most interesting, innovative, or unexpected ways that you have seen Pandas used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on this book?
What are some of the projects that you are planning to work on in the near/medium term?
Keep In Touch
Website
@__mharrison__ on Twitter
Blog
mattharrison on GitHub
Picks
Tobias
MSR Snowshoes
Matt
Telemark Skiing
22 Designs
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
Links
Effective Pandas Book (affiliate link with 20% discount code applied)
Discount code INIT
TCL
Perl
Pandas
Podcast Episode
Pandas Extension Arrays
Podcast Episode
Koalas
Dask
Data Engineering Podcast Episode
Modin
Podcast Episode
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
50 min
January 13, 2022Generate Your Text Files With Python Using Cog
Summary
Developers hate wasting effort on manual processes when we can write code to do it instead. Cog is a tool to manage the work of automating the creation of text inside another file by executing arbitrary Python code. In this episode Ned Batchelder shares the story of why he created Cog in the first place, some of the interesting ways that he uses it in his daily work, and the unique challenges of maintaining a project with a small audience and a well defined scope.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of 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. 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 Ned Batchelder about Cog, a tool for generating files or text from embedded Python logic
Interview
Introductions
How did you get introduced to Python?
Can you describe what Cog is and the story behind it?
What are the use cases that you initially created Cog to address?
What were the shortcomings or extraneous overhead that you encountered in tools such as Jinja, Mako, Genshi, etc. that led you to create a new tool?
What was your path from a quick and dirty script that suited your own purposes to turning it into a niche open source project that was general and stable enough for the broader community?
One of your claims to fame is your role as the maintainer for coverage.py. How has your experience managing such a widely used project translated to the relatively small and low traffic project like Cog?
Can you describe how Cog is implemented?
How did you approach the design of the syntactic elements for embedding Python code into a host file?
What is the workflow for someone using Cog to generate all or parts of a file?
How does the introduction of third party dependencies impact the viability and utility of Cog as compared to other templating systems?
What are the most interesting, innovative, or unexpected ways that you have seen Cog used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cog?
When is Cog the wrong choice?
What do you have planned for the future of Cog?
Keep In Touch
Website
nedbat on GitHub
@nedbat on Twitter
LinkedIn
Picks
Tobias
Samson Q9U Microphone
Ned
McFly Command Line History Tool
Go for a walk
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
Links
Cog
Boston Python
Lotus
Lotus Notes
Zope
Cheetah Template Engine
Coverage.py
Podcast Episode
Unix Philosophy
Hungarian Notation
Jupyter Notebooks
GitHub Profile ReadMe
Ned’s GitHub Profile
Raw Markdown
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
51 min
January 02, 2022A Friendly Approach To Regression Models For Programmers
Summary
Statistical regression models are a staple of predictive forecasts in a wide range of applications. In this episode Matthew Rudd explains the various types of regression models, when to use them, and his work on the book "Regression: A Friendly Guide" to help programmers add regression techniques to their toolbox.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of 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. 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 Matthew Rudd about the applications of statistical modeling and regression, and how to start using it for your work
Interview
Introductions
How did you get introduced to Python?
Can you start by describing some use cases for statistical regression?
What was your motivation for writing a book to explain this family of algorithms to programmers?
What are your goals for the book?
Who is the target audience?
What are some of the different categories of regression algorithms?
What are some heuristics for identifying which regression to use?
How have you approached the balance of using software principles for explaining the work of building the models with the mathematical underpinnings that make them work?
What are some of the concepts that are most challenging for people who are first working with regression models?
What are the most interesting, innovative, or unexpected ways that you have seen statistical regression models used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on your book?
What are some of the resources that you recommend for folks who want to learn more about the inner workings and applications of regression models after they finish your book?
Keep In Touch
LinkedIn
@MatthewBRudd on Twitter
Picks
Tobias
The Argument podcast from the NY Times
Matthew
Primus
Claypool Lennon Delirium
South of Reality
Links
Regression: A Friendly Guide
Sewanee University of the South
Sewanee Data Lab
Mark Lutz Python books
Elements of Statistical Learning
Linear Regression
Logistic Regression
Modeling Binary Data
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
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
46 min
December 27, 2021Fast, Flexible, and Incremental Task Automation With doit
Summary
Every software project needs a tool for managing the repetitive tasks that are involved in building, running, and deploying the code. Frustrated with the limitations of tools like Make, Scons, and others Eduardo Schettino created doit to handle task automation in his own work and released it as open source. In this episode he shares the story behind the project, how it is implemented under the hood, and how you can start using it in your own projects to save you time and effort.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of 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. 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 Eduardo Schettino about Doit, a flexible and low overhead task automation tool
Interview
Introductions
How did you get introduced to Python?
Can you describe what doit is and the story behind it?
What are the main goals and use cases of doit?
Can you describe how you approached the implementation of Doit?
How has the design changed or evolved since you first began working on it?
The realm of task automation tools for developers is an exceedingly crowded one, with each tool prioritizing certain use cases. How would you characterize the position of doit in the current ecosystem?
How does it compare to e.g. Click, Invoke, Typer, etc.?
What is your guiding philosophy for when and how to add new features?
You have been running the project for ~13 years now. How has the evolution of the Python language and ecosystem influenced your approach to the development and maintenance of doit?
What is the workflow for getting started with doit and integrating it into your development process?
For every project there are some tasks that are identical and some that are bespoke for that application. What are the options for maintaining a standard set of tasks across repositories and composing them with per-project activites?
What are some of the useful patterns that you and the community have established for designing tasks and execution graphs?
How do you use doit in your own work?
What are the most interesting, innovative, or unexpected ways that you have seen doit used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on doit?
When is doit the wrong choice?
What do you have planned for the future of doit?
Keep In Touch
LinkedIn
schettino72 on GitHub
Picks
Tobias
The Matrix series
Eduardo
John Pilger
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
Links
doit
Zope
Twisted
Django
Pyflakes
scons
Make
Nikola
Podcast Episode
Nose
Pytest
Podcast Episode
Click
Typer
Invoke
Puppet
Ansible
Chef
Sphinx
Snakemake
Airflow
Luigi
pytest-incremental
import-deps
dbm
MetalK8s
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
40 min
December 20, 2021The Technological, Business, and Sales Challenges Of Building The Ethical Ads Network
Summary
Whether we like it or not, advertising is a common and effective way to make money on the internet. In order to support the work being done at Read The Docs they decided to include advertisements on the documentation sites they were hosting, but they didn’t want to alienate their users or collect unnecessary information. In this episode David Fischer explains how they built the Ethical Ads network to solve their problem, the technical and business challenges that are involved, and the open source application that they built to power their network.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of 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. 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 David Fischer about the Ethical Ads marketplace and the technology that runs
Interview
Introductions
How did you get introduced to Python?
Can you describe what the Ethical Ads project is and the story behind it?
What are the technical and organizational requirements involved in running an ad network?
How have you approached the problem of kickstarting the flywheel for the two-sided marketplace?
What are some of the challenges that you face in building an accurate profile of your audience without using detailed tracking methods?
What are the benefits that you see in focusing exclusively on developers in your publisher relationships?
Can you describe the design and implementation of the ad server?
How has the architecture evolved since you first began working on it?
If you were to start over today what might you do differently?
How have you approached scaling for performance and geographic distribution?
What mechanisms do you use for tracking impressions/measuring ad effectiveness?
How can advertisers experiment with A/B testing of ad copy?
If someone wants to run their own advertisements with the ethical ads server, what is involved in getting it deployed and integrated into their sites?
What are the integration and extension points available for customizing the behavior of the platform?
What are some of the most notable lessons that you have learned about online advertising since you first started working on the Ethical Ads project?
What are the most interesting, innovative, or unexpected ways that you have seen Ethical Ads used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on the Ethical Ads platform?
What do you have planned for the future of the Ethical Ads platform?
Keep In Touch
davidfischer on GitHub
@djfische on Twitter
LinkedIn
Picks
Tobias
Ship It! Podcast
David
Local Python Meetup
Click CLI framework
useragents library
TLD for parsing internet domains
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
Links
Ethical Ads Network
Ethical Ads Server
San Diego Python
Read The Docs
Podcast Episode
CodeFund
CPM == Cost Per Mille
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
56 min
December 12, 2021Accidentally Building A Business With Python At Listen Notes
Summary
Podcasts are one of the few mediums in the internet era that are still distributed through an open ecosystem. This has a number of benefits, but it also brings the challenge of making it difficult to find the content that you are looking for. Frustrated by the inability to pick and choose single episodes across various shows for his listening Wenbin Fang started the Listen Notes project to fulfill his own needs. He ended up turning that project into his full time business which has grown into the most full featured podcast search engine on the market. In this episode he explains how he build the Listen Notes application using Python and Django, his work to turn it into a sustainable business, and the various ways that you can build other applications and experiences on top of his API.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of 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. 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 Wenbin Fang about the technology powering the Listen Notes podcast discovery platform
Interview
Introductions
How did you get introduced to Python?
Can you describe what Listen Notes is and the story behind it?
What are some of the main goals that listeners have when searching for a podcast?
What are the challenges that they commonly encounter when looking for information in a podcast?
What are the different sources of information that you can use to extract useful details about a podcast?
How do you identify and prioritize new features or product enhancements?
Can you describe how the Listen Notes platform is architected?
How has it changed or evolved since you first began working on it?
How did you approach the technology selection for the initial version of Listen Notes?
If you were to start over today, what might you do differently?
What are the technical challenges that are posed by the ecosystem around podcasts?
What are the biggest changes that have happened in the methods of production and consumption for podcasts since you first became involved in the space?
How do you approach the design and contracts of the Listen Notes web API given how core that is to your platform?
What are the most complex or complicated engineering projects that you have done for Listen Notes?
What are the pieces of the infrastructure for podcasts that you would like to see improved, changed, or replaced?
What are some of the kinds of projects that developers can build with the Listen Notes API?
What, if any, impact have the introduction of podcasts to closed platforms such as Spotify, Amazon Music, etc. had on your business?
What are some of the most surprising things that you have learned about podcasts and their consumption while building Listen Notes?
What are the most interesting, innovative, or unexpected ways that you have seen Listen Notes used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Listen Notes?
What do you have planned for the future of Listen Notes?
Keep In Touch
Website
LinkedIn
wenbinf on GitHub
@wenbinf on Twitter
Picks
Tobias
Wheel of Time TV Series
Wenbin
Superhuman email client
Links
Listen Notes
Graphviz
NextDoor
PostgreSQL
Elasticsearch
Redis
RabbitMQ
Celery
ReactJS
Django
Bootstrap CSS
Digital Ocean
Tailwind CSS
Entity Resolution
Clickhouse
Data Engineering Podcast Episode
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
44 min
November 27, 2021Making Orbital Mechanics More Accessible With Poliastro
Summary
Outer space holds a deep fascination for people of all ages, and the key principle in its exploration both near and far is orbital mechanics. Poliastro is a pure Python package for exploring and simulating orbit calculations. In this episode Juan Luis Cano Rodriguez shares the story behind the project, how you can use it to learn more about space travel, and some of the interesting projects that have used it for planning planetary and interplanetary missions.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of 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. 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 Juan Luis Cano Rodriguez about Poliastro, an open source library for interactive Astrodynamics and Orbital Mechanics, with a focus on ease of use, speed, and quick visualization.
Interview
Introductions
How did you get introduced to Python?
Can you describe what Poliastro is and the story behind it?
What are some of the simulations that Poliastro is designed to be used for?
How much knowledge of orbital mechanics is necessary to get started with Poliastro?
Can you describe how the project is implemented?
How have the goals and design of the project changed or evolved since you first started it?
What are some of the design philosophies that you focus on to make the package accessible to the range of users that you support?
Can you talk through the workflow of using Poliastro to do something like track the path of the ISS and its traversal of the debris field from the recent satellite destruction?
What are some of the other libraries or frameworks that are commonly used with Poliastro?
How are you using Poliastro in your own work?
What are some overlooked or underused aspects of the project that you would like to highlight?
What are the most interesting, innovative, or unexpected ways that you have seen Poliastro used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Poliastro?
When is Poliastro the wrong choice?
What do you have planned for the future of Poliastro?
Keep In Touch
LinkedIn
GitHub
Email
Twitter
Picks
Tobias
Josh Blue (comedian)
Juan Luis
DJ Cotts
DJ Weaver
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
Links
Poliastro
Fortran 90 (if only this community existed back then! https://ondrejcertik.com/blog/2021/03/resurrecting-fortran/)?utm_source=rss&utm_medium=rss
Satellogic
Read the Docs
Wolfram Alpha
Mathematica
SageMath
2-Body Problem
AstroPy
Podcast Episode
Numba
Import Linter
Vallado "Fundamentals of Astrodynamics"
International Space Station
Starlink Satellites
Planetary Ephemeritas Data
Satellite Data
Kerbal Space Program
NumFOCUS
Open Collective
Python SGP4
Libre Space Foundation
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
59 min
November 22, 2021Declarative Deep Learning From Your Laptop To Production With Ludwig and Horovod
Summary
Deep learning frameworks encourage you to focus on the structure of your model ahead of the data that you are working with. Ludwig is a tool that uses a data oriented approach to building and training deep learning models so that you can experiment faster based on the information that you actually have, rather than spending all of our time manipulating features to make them match your inputs. In this episode Travis Addair explains how Ludwig is designed to improve the adoption of deep learning for more companies and a wider range of users. He also explains how the Horovod framework plugs in easily to allow for scaling your training workflow from your laptop out to a massive cluster of servers and GPUs. The combination of these tools allows for a declarative workflow that starts off easy but gives you full control over the end result.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of 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. 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 Travis Adair about building and training machine learning models with Ludwig and Horovod
Interview
Introductions
How did you get introduced to Python?
Can you describe what Horovod and Ludwig are?
How do the projects work together?
What was your path to being involved in those projects and what is your current role?
There are a number of AutoML libraries available for frameworks such as scikit-learn, etc. What are the challenges that are introduced by applying that workflow to deep learning architectures?
What are the use cases that Ludwig is designed to enable?
Who are the target users of Ludwig?
How do the workflows change/progress for the different personas?
How is the underlying framework architected?
What are the available extension points to provide a progressive exposure of complexity?
How have the goals and design of the project changed or evolved as it has gained more widespread adoption beyond Uber?
What was the motivation for migrating the core of Ludwig from Tensorflow to Pytorch?
Can you describe the workflow of building a model definition with Ludwig?
How much knowledge of neural network architectures and their relevant characteristics is necessary to use Ludwig effectively?
What are the motivating factors for adding Horovod to the process?
What is involved in moving from a single machine/single process training loop to a multi-core or multi-machine distributed training process?
The combination of Ludwig and Horovod provide a shallower learning curve for building and scaling model training. What do you see as their potential impact on the availability and adoption of more sophisticated ML capabilities across organizations of varying scale?
What do you see as other significant barriers to widespread use of ML functionality?
What are the most interesting, innovative, or unexpected ways that you have seen Ludwig and/or Horovod used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ludwig and Horovod?
When is Ludwig and/or Horovod the wrong choice?
What do you have planned for the future of both projects?
Keep In Touch
LinkedIn
@TravisAddair on Twitter
tgaddair on GitHub
Picks
Tobias
Zeal and Ardor
Travis
Opeth
Agaloch
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
Links
Ludwig
Horovod
Predibase
Uber
Michelangelo
Tensorflow
PyTorch
Podcast Episode
Gradient Boosted Trees
XGBoost
CatBoost
LightGBM
PyCaret
HyperBand
scikit-optimize
Keras
Vision Transformer Architecture
HuggingFace
Jax
DeepSpeed
AllReduce
Nvidia Collective Communications Library (NCCL)
Training Epoch
ElasticDL
Raft Consensus Algorithm
TorchScript
Transfer Learning
Gordon Bell Prize
Anyscale
Ray
Podcast Episode
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr 5 min
November 22, 2021Build Better Analytics And Models With A Focus On The Data Experience
Summary
A lot of time and energy goes into data analysis and machine learning projects to address various goals. Most of the effort is focused on the technical aspects and validating the results, but how much time do you spend on considering the experience of the people who are using the outputs of these projects? In this episode Benn Stancil explores the impact that our technical focus has on the perceived value of our work, and how taking the time to consider what the desired experience will be can lead us to approach our work more holistically and increase the satisfaction of everyone involved.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of 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. 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 Benn Stancil about the perennial frustrations of working with data and thoughts on how to improve the experience
Interview
Introductions
How did you get introduced to Python?
Can you start by discussing your perspective on the most frustrating elements of working with data in an organization?
How might that compound when working with machine learning?
What are the sources of the disconnect between our level of technical sophistication and our ability to produce meaningful insights from our data?
There have been a number of formulations about a "hierarchy of needs" pertaining to data. When the goal is to bring ML/AI methods to bear on an organization’s processes or products how can thinking about the intended experience act to improve the end result?
What are some failure modes or suboptimal outcomes that might be expected when building from a tooling/technology/technique first mindset?
What are some of the design elements that we can incorporate into our development environments/data infrastructure/data modeling that can incentivize a more experience driven process for building data products/analyses/ML models?
How does the design and capabilities of the Mode platform allow teams to progress along the journey from data discovery to descriptive analytics, to ML experiments?
What are the most interesting, innovative, or unexpected approaches that you have seen for encouraging the creation of positive data experiences?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Mode and data analysis?
When is a data experience the wrong approach?
What do you have planned for the future of Mode to support this ideal?
Keep In Touch
LinkedIn
@bennstancil on Twitter
Picks
Tobias
Venture Unlocked Podcast
Benn
Wrap Text by Bobby Pinero
Counting Stuff by Randy Au
Ray Data Co by Mr Ben
Modern Data Democracy By JP Monteiro
Bad Blood Podcast
Bad Blood Book
Links
Mode Analytics
Tidyverse
Airflow
Fivetran
Data Engineering Podcast Episode
dbt
Data Engineering Podcast Episode
Conway’s Law
Cinchy
Data Engineering Podcast Episode
Reverse ETL
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr
November 06, 2021Building Conversational AI to Augment Sales Teams at Structurely
Summary
The true power of artificial intelligence is its ability to work collaboratively with humans. Nate Joens co-founded Structurely to create a conversational AI platform that augments human sales teams to help guide potential customers through the initial steps of the funnel. In this episode he discusses the technical and social considerations that need to be combined for a seamless conversational experience and how he and his team are tackling the problem.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of 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. 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 Nate Joens about his work at Structurely to build conversational AI utilities that augment human sales interactions
Interview
Introductions
How did you get introduced to Python?
Can you describe what Structurely is and the story behind it?
What are the elements that comprise a "conversational AI"?
How is it distinct from the wave of chatbots that were popular in recent years?
What lessons from that approach can we take forward into AI enabled conversational platforms?
How are you applying AI to the sales process?
How much domain expertise is necessary to make an effective and engaging conversational AI? (e.g. knowledge of sales techniques vs. knowledge of real estate, etc.)
Can you describe how you have designed the Structurely platform?
What are the biggest engineering challenges that you have had to work through?
What challenges or complexities have been most persistent?
What are the design complexities that you have to work through to make the AI accessible for end users?
What are some of the advancements in AI/NLP/transfer learning that have been most beneficial for teams building conversational AI?
What are the signals that you emphasize when monitoring the performance of your models?
What is your approach for feeding real-world customer interactions back into your model development and training loop?
What are the most active areas of research in conversational AI applications and techniques?
What are the most interesting, innovative, or unexpected ways that you have seen Structurely and/or conversational AI used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on conversational AI at Structurely?
When is conversational AI the wrong choice?
What do you have planned for the future of Structurely?
Keep In Touch
@whonatejoens on Twitter
LinkedIn
Picks
Tobias
Vantage AWS Cost Management
Nate
VideoForm
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.
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Links
Stucturely
GIS
Generative AI
GPT-3
Sanky Diagram
PyTorch
Podcast Episode
Allen Institute for AI
F Score
Snorkel
Podcast Episode
Few-Shot Learning
Zero Shot Learning
Voxable
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
51 min