AI Engineering Podcast

Latest Episodes:

Surveying The Landscape Of AI and ML From An Investor's Perspective Oct 15, 2023

Show notes

Summary
Artificial Intelligence is experiencing a renaissance in the wake of breakthrough natural language models. With new businesses sprouting up to address the various needs of ML and AI teams across the industry, it is a constant challenge to stay informed. Matt Turck has been compiling a report on the state of ML, AI, and Data for his work at FirstMark Capital. In this episode he shares his findings on the ML and AI landscape and the interesting trends that are developing.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES.
Your host is Tobias Macey and today I'm interviewing Matt Turck about his work on the MAD (ML, AI, and Data) landscape and the insights he has gained on the ML ecosystem

Interview

Introduction
How did you get involved in machine learning?
Can you describe what the MAD landscape project is and the story behind it?
What are the major changes in the ML ecosystem that you have seen since you first started compiling the landscape?
- How have the developments in consumer-grade AI in recent years changed the business opportunities for ML/AI?
What are the coarse divisions that you see as the boundaries that define the different categories for ML/AI in the landscape?
For ML infrastructure products/companies, what are the biggest challenges that they face in engineering and customer acquisition?
What are some of the challenges in building momentum for startups in AI (existing moats around data access, talent acquisition, etc.)?
- For products/companies that have ML/AI as their core offering, what are some strategies that they use to compete with "big tech" companies that already have a large corpus of data?
What do you see as the societal vs. business importance of open source models as AI becomes more integrated into consumer facing products?
What are the most interesting, innovative, or unexpected ways that you have seen ML/AI used in business and social contexts?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on the ML/AI elements of the MAD landscape?
When is ML/AI the wrong choice for businesses?
What are the areas of ML/AI that you are paying closest attention to in your own work?

Contact Info

Website
@mattturck on Twitter

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Applying Federated Machine Learning To Sensitive Healthcare Data At Rhino Health Sep 11, 2023

Show notes

Summary
A core challenge of machine learning systems is getting access to quality data. This often means centralizing information in a single system, but that is impractical in highly regulated industries, such as healthchare. To address this hurdle Rhino Health is building a platform for federated learning on health data, so that everyone can maintain data privacy while benefiting from AI capabilities. In this episode Ittai Dayan explains the barriers to ML in healthcare and how they have designed the Rhino platform to overcome them.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Ittai Dayan about using federated learning at Rhino Health to bring AI capabilities to the tightly regulated healthcare industry

Interview

Introduction
How did you get involved in machine learning?
Can you describe what Rhino Health is and the story behind it?
What is federated learning and what are the trade-offs that it introduces?
- What are the benefits to healthcare and pharmalogical organizations from using federated learning?
What are some of the challenges that you face in validating that patient data is properly de-identified in the federated models?
Can you describe what the Rhino Health platform offers and how it is implemented?
- How have the design and goals of the system changed since you started working on it?
What are the technological capabilities that are needed for an organization to be able to start using Rhino Health to gain insights into their patient and clinical data?
- How have you approached the design of your product to reduce the effort to onboard new customers and solutions?
What are some examples of the types of automation that you are able to provide to your customers? (e.g. medical diagnosis, radiology review, health outcome predictions, etc.)
What are the ethical and regulatory challenges that you have had to address in the development of your platform?
What are the most interesting, innovative, or unexpected ways that you have seen Rhino Health used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Rhino Health?
When is Rhino Health the wrong choice?
What do you have planned for the future of Rhino Health?

Contact Info

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Using Machine Learning To Keep An Eye On The Planet Jun 17, 2023

Show notes

Summary
Satellite imagery has given us a new perspective on our world, but it is limited by the field of view for the cameras. Synthetic Aperture Radar (SAR) allows for collecting images through clouds and in the dark, giving us a more consistent means of collecting data. In order to identify interesting details in such a vast amount of data it is necessary to use the power of machine learning. ICEYE has a fleet of satellites continuously collecting information about our planet. In this episode Tapio Friberg shares how they are applying ML to that data set to provide useful insights about fires, floods, and other terrestrial phenomena.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Tapio Friberg about building machine learning applications on top of SAR (Synthetic Aperture Radar) data to generate insights about our planet

Interview

Introduction
How did you get involved in machine learning?
Can you describe what ICEYE is and the story behind it?
What are some of the applications of ML at ICEYE?
What are some of the ways that SAR data poses a unique challenge to ML applications?
What are some of the elements of the ML workflow that you are able to use "off the shelf" and where are the areas that you have had to build custom solutions?
Can you share the structure of your engineering team and the role that the ML function plays in the larger organization?
What does the end-to-end workflow for your ML model development and deployment look like?
- What are the operational requirements for your models? (e.g. batch execution, real-time, interactive inference, etc.)
In the model definitions, what are the elements of the source domain that create the largest challenges? (e.g. noise from backscatter, variance in resolution, etc.)
Once you have an output from an ML model how do you manage mapping between data domains to reflect insights from SAR sources onto a human understandable representation?
Given that SAR data and earth imaging is still a very niche domain, how does that influence your ability to hire for open positions and the ways that you think about your contributions to the overall ML ecosystem?
How can your work on using SAR as a representation of physical attributes help to improve capabilities in e.g. LIDAR, computer vision, etc.?
What are the most interesting, innovative, or unexpected ways that you have seen ICEYE and SAR data used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on ML for SAR data?
What do you have planned for the future of ML applications at ICEYE?

Contact Info

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

The Role Of Model Development In Machine Learning Systems May 29, 2023

Show notes

Summary
The focus of machine learning projects has long been the model that is built in the process. As AI powered applications grow in popularity and power, the model is just the beginning. In this episode Josh Tobin shares his experience from his time as a machine learning researcher up to his current work as a founder at Gantry, and the shift in focus from model development to machine learning systems.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Josh Tobin about the state of industry best practices for designing and building ML models

Interview

Introduction
How did you get involved in machine learning?
Can you start by describing what a "traditional" process for building a model looks like?
- What are the forces that shaped those "best practices"?
What are some of the practices that are still necessary/useful and what is becoming outdated?
- What are the changes in the ecosystem (tooling, research, communal knowledge, etc.) that are forcing teams to reconsider how they think about modeling?
What are the most critical practices/capabilities for teams who are building services powered by ML/AI?
- What systems do they need to support them in those efforts?
Can you describe what you are building at Gantry and how it aids in the process of developing/deploying/maintaining models with "modern" workflows?
What are the most challenging aspects of building a platform that supports ML teams in their workflows?
What are the most interesting, innovative, or unexpected ways that you have seen teams approach model development/validation?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gantry?
When is Gantry the wrong choice?
What are some of the resources that you find most helpful to stay apprised of how modeling and ML practices are evolving?

Contact Info

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Real-Time Machine Learning Has Entered The Realm Of The Possible Mar 09, 2023

Show notes

Summary
Machine learning models have predominantly been built and updated in a batch modality. While this is operationally simpler, it doesn't always provide the best experience or capabilities for end users of the model. Tecton has been investing in the infrastructure and workflows that enable building and updating ML models with real-time data to allow you to react to real-world events as they happen. In this episode CTO Kevin Stumpf explores they benefits of real-time machine learning and the systems that are necessary to support the development and maintenance of those models.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Kevin Stumpf about the challenges and promise of real-time ML applications

Interview

Introduction
How did you get involved in machine learning?
Can you describe what real-time ML is and some examples of where it might be applied?
What are the operational and organizational requirements for being able to adopt real-time approaches for ML projects?
What are some of the ways that real-time requirements influence the scale/scope/architecture of an ML model?
What are some of the failure modes for real-time vs analytical or operational ML?
Given the low latency between source/input data being generated or received and a prediction being generated, how does that influence susceptibility to e.g. data drift?
- Data quality and accuracy also become more critical. What are some of the validation strategies that teams need to consider as they move to real-time?
What are the most interesting, innovative, or unexpected ways that you have seen real-time ML applied?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on real-time ML systems?
When is real-time the wrong choice for ML?
What do you have planned for the future of real-time support for ML in Tecton?

Contact Info

LinkedIn
@kevinmstumpf on Twitter

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

How Shopify Built A Machine Learning Platform That Encourages Experimentation Feb 02, 2023

Show notes

Summary
Shopify uses machine learning to power multiple features in their platform. In order to reduce the amount of effort required to develop and deploy models they have invested in building an opinionated platform for their engineers. They have gone through multiple iterations of the platform and their most recent version is called Merlin. In this episode Isaac Vidas shares the use cases that they are optimizing for, how it integrates into the rest of their data platform, and how they have designed it to let machine learning engineers experiment freely and safely.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Isaac Vidas about his work on the ML platform used by Shopify

Interview

Introduction
How did you get involved in machine learning?
Can you describe what Shopify is and some of the ways that you are using ML at Shopify?
- What are the challenges that you have encountered as an organization in applying ML to your business needs?
Can you describe how you have designed your current technical platform for supporting ML workloads?
- Who are the target personas for this platform?
- What does the workflow look like for a given data scientist/ML engineer/etc.?
What are the capabilities that you are trying to optimize for in your current platform?
- What are some of the previous iterations of ML infrastructure and process that you have built?
- What are the most useful lessons that you gathered from those previous experiences that informed your current approach?
How have the capabilities of the Merlin platform influenced the ways that ML is viewed and applied across Shopify?
What are the most interesting, innovative, or unexpected ways that you have seen Merlin used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Merlin?
When is Merlin the wrong choice?
What do you have planned for the future of Merlin?

Contact Info

@kazuaros on Twitter
LinkedIn
kazuar on GitHub

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Applying Machine Learning To The Problem Of Bad Data At Anomalo Jan 24, 2023

Show notes

Summary
All data systems are subject to the "garbage in, garbage out" problem. For machine learning applications bad data can lead to unreliable models and unpredictable results. Anomalo is a product designed to alert on bad data by applying machine learning models to various storage and processing systems. In this episode Jeremy Stanley discusses the various challenges that are involved in building useful and reliable machine learning models with unreliable data and the interesting problems that they are solving in the process.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Jeremy Stanley about his work at Anomalo, applying ML to the problem of data quality monitoring

Interview

Introduction
How did you get involved in machine learning?
Can you describe what Anomalo is and the story behind it?
What are some of the ML approaches that you are using to address challenges with data quality/observability?
What are some of the difficulties posed by your application of ML technologies on data sets that you don't control?
- How does the scale and quality of data that you are working with influence/constrain the algorithmic approaches that you are using to build and train your models?
How have you implemented the infrastructure and workflows that you are using to support your ML applications?
What are some of the ways that you are addressing data quality challenges in your own platform?
- What are the opportunities that you have for dogfooding your product?
What are the most interesting, innovative, or unexpected ways that you have seen Anomalo used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Anomalo?
When is Anomalo the wrong choice?
What do you have planned for the future of Anomalo?

Contact Info

@jeremystan on Twitter
LinkedIn

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Anomalo
- Data Engineering Podcast Episode
Partial Differential Equations
Neural Network
Neural Networks For Pattern Recognition by Christopher M. Bishop (affiliate link)
Gradient Boosted Decision Trees
Shapley Values
Sentry
dbt
Altair

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Build More Reliable Machine Learning Systems With The Dagster Orchestration Engine Dec 02, 2022

Show notes

Summary
Building a machine learning model one time can be done in an ad-hoc manner, but if you ever want to update it and serve it in production you need a way of repeating a complex sequence of operations. Dagster is an orchestration engine that understands the data that it is manipulating so that you can move beyond coarse task-based representations of your dependencies. In this episode Sandy Ryza explains how his background in machine learning has informed his work on the Dagster project and the foundational principles that it is built on to allow for collaboration across data engineering and machine learning concerns.
Interview

Introduction
How did you get involved in machine learning?
Can you start by sharing a definition of "orchestration" in the context of machine learning projects?
What is your assessment of the state of the orchestration ecosystem as it pertains to ML?
modeling cycles and managing experiment iterations in the execution graph
how to balance flexibility with repeatability
What are the most interesting, innovative, or unexpected ways that you have seen orchestration implemented/applied for machine learning?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on orchestration of ML workflows?
When is Dagster the wrong choice?
What do you have planned for the future of ML support in Dagster?

Contact Info

LinkedIn
@s_ryz on Twitter
sryza on GitHub

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Solve The Cold Start Problem For Machine Learning By Letting Humans Teach The Computer With Aitomatic Sep 28, 2022

Show notes

Summary
Machine learning is a data-hungry approach to problem solving. Unfortunately, there are a number of problems that would benefit from the automation provided by artificial intelligence capabilities that don’t come with troves of data to build from. Christopher Nguyen and his team at Aitomatic are working to address the "cold start" problem for ML by letting humans generate models by sharing their expertise through natural language. In this episode he explains how that works, the various ways that we can start to layer machine learning capabilities on top of each other, as well as the risks involved in doing so without incorporating lessons learned in the growth of the software industry.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!
Your host is Tobias Macey and today I’m interviewing Christopher Nguyen about how to address the cold start problem for ML/AI projects

Interview

Introduction
How did you get involved in machine learning?
Can you describe what the "cold start" or "small data" problem is and its impact on an organization’s ability to invest in machine learning?
What are some examples of use cases where ML is a viable solution but there is a corresponding lack of usable data?
How does the model design influence the data requirements to build it? (e.g. statistical model vs. deep learning, etc.)
What are the available options for addressing a lack of data for ML?
- What are the characteristics of a given data set that make it suitable for ML use cases?
Can you describe what you are building at Aitomatic and how it helps to address the cold start problem?
- How have the design and goals of the product changed since you first started working on it?
What are some of the education challenges that you face when working with organizations to help them understand how to think about ML/AI investment and practical limitations? What are the most interesting, innovative, or unexpected ways that you have seen Aitomatic/H1st used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Aitomatic/H1st?
When is a human/knowledge driven approach to ML development the wrong choice?
What do you have planned for the future of Aitomatic?

Contact Info

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Convert Your Unstructured Data To Embedding Vectors For More Efficient Machine Learning With Towhee Sep 21, 2022

Show notes

Summary
Data is one of the core ingredients for machine learning, but the format in which it is understandable to humans is not a useful representation for models. Embedding vectors are a way to structure data in a way that is native to how models interpret and manipulate information. In this episode Frank Liu shares how the Towhee library simplifies the work of translating your unstructured data assets (e.g. images, audio, video, etc.) into embeddings that you can use efficiently for machine learning, and how it fits into your workflow for model development.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!
Your host is Tobias Macey and today I’m interviewing Frank Liu about how to use vector embeddings in your ML projects and how Towhee can reduce the effort involved

Interview

Introduction
How did you get involved in machine learning?
Can you describe what Towhee is and the story behind it?
What is the problem that Towhee is aimed at solving?
What are the elements of generating vector embeddings that pose the greatest challenge or require the most effort?
Once you have an embedding, what are some of the ways that it might be used in a machine learning project?
- Are there any design considerations that need to be addressed in the form that an embedding takes and how it impacts the resultant model that relies on it? (whether for training or inference)
Can you describe how the Towhee framework is implemented?
- What are some of the interesting engineering challenges that needed to be addressed?
- How have the design/goals/scope of the project shifted since it began?
What is the workflow for someone using Towhee in the context of an ML project?
What are some of the types optimizations that you have incorporated into Towhee?
- What are some of the scaling considerations that users need to be aware of as they increase the volume or complexity of data that they are processing?
What are some of the ways that using Towhee impacts the way a data scientist or ML engineer approach the design development of their model code?
What are the interfaces available for integrating with and extending Towhee?
What are the most interesting, innovative, or unexpected ways that you have seen Towhee used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Towhee?
When is Towhee the wrong choice?
What do you have planned for the future of Towhee?

Contact Info

LinkedIn
fzliu on GitHub
Website
@frankzliu on Twitter

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links