Feature Platforms for Data-Centric AI with Mike Del Balso - #577
In the latest installment of our Data-Centric AI series, we’re joined by a friend of the show Mike Del Balso, Co-founder and CEO of Tecton. If you’ve heard any of our other conversations with Mike, you know we spend a lot of time discussing feature stores, or as he now refers to them, feature platforms. We explore the current complexity of data infrastructure broadly and how that has changed over the last five years, as well as the maturation of streaming data platforms. We discuss the wide vs deep paradox that exists around ML tooling, and the idea around the “ML Flywheel”, a strategy that leverages data to accelerate machine learning. Finally, we spend time discussing internal ML team construction, some of the challenges that organizations face when building their ML platforms teams, and how they can avoid the pitfalls as they arise.
The complete show notes for this episode can be found at twimlai.com/go/577
The Grand Vision And Present Reality of DataOps
Summary
The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. More than just a collection of tools, there are a number of organizational and conceptual changes that a proper DataOps approach depends on. In this episode Kevin Stumpf, CTO of Tecton, Maxime Beauchemin, CEO of Preset, and Lior Gavish, CTO of Monte Carlo, discuss the grand vision and present realities of DataOps. They explain how to think about your data systems in a holistic and maintainable fashion, the security challenges that threaten to derail your efforts, and the power of using metadata as the foundation of everything that you do. If you are wondering how to get control of your data platforms and bring all of your stakeholders onto the same page then this conversation is for you.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today 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!
Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask.
RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today.
Your host is Tobias Macey and today I’m interviewing Max Beauchemin, Lior Gavish, and Kevin Stumpf about the real world challenges of embracing DataOps practices and systems, and how to keep things secure as you scale
Interview
Introduction
How did you get involved in the area of data management?
Before we get started, can you each give your definition of what "DataOps" means to you?
How does this differ from "business as usual" in the data industry?
What are some of the things that DataOps isn’t (despite what marketers might say)?
What are the biggest difficulties that you have faced in going from concept to production with a workflow or system intended to power self-serve access to other members of the organization?
What are the weak points in the current state of the industry, whether technological or social, that contribute to your greatest sense of unease from a security perspective?
As founders of companies that aim to facilitate adoption of various aspects of DataOps, how are you applying the products that you are building to your own internal systems?
How does security factor into the design of robust DataOps systems? What are some of the biggest challenges related to security when it comes to putting these systems into production?
What are the biggest differences between DevOps and DataOps, particularly when it concerns designing distributed systems?
What areas of the DataOps landscape do you think are ripe for innovation?
Nowadays, it seems like new DataOps companies are cropping up every day to try and solve some of these problems. Why do you think DataOps is becoming such an important component of the modern data stack?
There’s been a lot of conversation recently around the "rise of the data engineer" versus other roles in the data ecosystem (i.e. data scientist or data analyst). Why do you think that is?
What are some of the most valuable lessons that you have learned from working with your customers about how to apply DataOps principles?
What are some of the most interesting, unexpected, or challenging lessons that you have learned while building your respective platforms and businesses?
What are the industry trends that you are each keeping an eye on to inform you future product direction?
Contact Info
Kevin
LinkedIn
kevinstumpf on GitHub
@kevinstumpf on Twitter
Maxime
LinkedIn
@mistercrunch on Twitter
mistercrunch on GitHub
Lior
LinkedIn
@lgavish on Twitter
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about 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@dataengineeringpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
Links
Tecton
Monte Carlo
Superset
Preset
Barracuda Networks
Feature Store
DataOps
DevOps
Data Catalog
Amundsen
OpenLineage
The Downfall of the Data Engineer
Hashicorp Vault
Reverse ELT
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Support Data Engineering Podcast
Bringing Feature Stores and MLOps to the Enterprise at Tecton
Summary
As more organizations are gaining experience with data management and incorporating analytics into their decision making, their next move is to adopt machine learning. In order to make those efforts sustainable, the core capability they need is for data scientists and analysts to be able to build and deploy features in a self service manner. As a result the feature store is becoming a required piece of the data platform. To fill that need Kevin Stumpf and the team at Tecton are building an enterprise feature store as a service. In this episode he explains how his experience building the Michelanagelo platform at Uber has informed the design and architecture of Tecton, how it integrates with your existing data systems, and the elements that are required for well engineered feature store.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to dataengineeringpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s dataengineeringpodcast.com/talkpython, and don’t forget to thank them for supporting the show.
You invest so much in your data infrastructure – you simply can’t afford to settle for unreliable data. Fortunately, there’s hope: in the same way that New Relic, DataDog, and other Application Performance Management solutions ensure reliable software and keep application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo’s end-to-end Data Observability Platform monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact through lineage, and notify those who need to know before it impacts the business. By empowering data teams with end-to-end data reliability, Monte Carlo helps organizations save time, increase revenue, and restore trust in their data. Visit dataengineeringpodcast.com/montecarlo today to request a demo and see how Monte Carlo delivers data observability across your data infrastructure. The first 25 will receive a free, limited edition Monte Carlo hat!
Your host is Tobias Macey and today I’m interviewing Kevin Stumpf about Tecton and the role that the feature store plays in a modern MLOps platform
Interview
Introduction
How did you get involved in the area of data management?
Can you start by describing what you are building at Tecton and your motivation for starting the business?
For anyone who isn’t familiar with the concept, what is an example of a feature?
How do you define what a feature store is?
What role does a feature store play in the overall lifecycle of a machine learning project?
How would you characterize the current landscape of feature stores?
What are the other components that are necessary for a complete ML operations platform?
At what points in the lifecycle of data does the feature store get integrated?
What types of data can feature stores manage? (e.g. text vs. image/binary vs. spatial, etc.)
How is the Tecton platform implemented?
How has the design evolved since you first began building it?
How did your work on Uber’s Michelangelo inform your work on Tecton?
What is the workflow and lifecycle of developing, testing, and deploying a feature to a feature store?
What aspects of a feature do you monitor to determine whether it has drifted?
How do you define drift in the context of a feature?
How does that differ from drift in an ML model?
How does Tecton handle versioning of features and associating those different versions with the models that are using them?
What are some of the most interesting, innovative, or unexpected projects that you have seen built with Tecton?
When is Tecton the wrong choice?
What do you have planned for the future of the product?
Contact Info
LinkedIn
kevinstumpf on GitHub
@kevinstumpf on Twitter
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about 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@dataengineeringpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
Links
Tecton
Uber Michelangelo
MLOps
Feature Store
Blog: What Is A Feature Store
StreamSQL
Podcast Episode
AWS Feature Store
Logical Clocks
EMR
Kotlin
DynamoDB
scikit-learn
Tensorflow
MLFlow
Algorithmia
SageMaker
Feast open source feature store
Jaeger
OpenTelemetry
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Support Data Engineering Podcast
Feature Stores for Accelerating AI Development - #432
In this special episode of the podcast, we're joined by Kevin Stumpf, Co-Founder and CTO of Tecton, Willem Pienaar, an engineering lead at Gojek and founder of the Feast Project, and Maxime Beauchemin, Founder & CEO of Preset, for a discussion on Feature Stores for Accelerating AI Development.
In this panel discussion, Sam and our guests explored how organizations can increase value and decrease time-to-market for machine learning using feature stores, MLOps, and open source. We also discuss the main data challenges of AI/ML, and the role of the feature store in solving those challenges.
The complete show notes for this episode can be found at twimlai.com/go/432.
Feature Stores for MLOps with Mike del Balso - #420
Today we’re joined by Mike del Balso, co-Founder and CEO of Tecton.
Mike, who you might remember from our last conversation on the podcast, was a foundational member of the Uber team that created their ML platform, Michelangelo. Since his departure from the company in 2018, he has been busy building up Tecton, and their enterprise feature store.
In our conversation, Mike walks us through why he chose to focus on the feature store aspects of the machine learning platform, the journey, personal and otherwise, to operationalizing machine learning, and the capabilities that more mature platforms teams tend to look for or need to build. We also explore the differences between standalone components and feature stores, if organizations are taking their existing databases and building feature stores with them, and what a dynamic, always available feature store looks like in deployment.
Finally, we explore what sets Tecton apart from other vendors in this space, including enterprise cloud providers who are throwing their hat in the ring.
The complete show notes for this episode can be found at twimlai.com/go/420.
Thanks to our friends at Tecton for sponsoring this episode of the podcast! Find out more about what they're up to at tecton.ai.
UBER and Intel’s Machine Learning platforms
We recently met up with Cormac Brick (Intel) and Mike Del Balso (Uber) at O’Reilly AI in SF. As the director of machine intelligence in Intel’s Movidius group, Cormac is an expert in porting deep learning models to all sorts of embedded devices (cameras, robots, drones, etc.). He helped us understand some of the techniques for developing portable networks to maximize performance on different compute architectures.
In our discussion with Mike, we talked about the ins and outs of Michelangelo, Uber’s machine learning platform, which he manages. He also described why it was necessary for Uber to build out a machine learning platform and some of the new features they are exploring.
Sponsors:
DigitalOcean – DigitalOcean is simplicity at scale. Whether your business is running one virtual machine or ten thousand, DigitalOcean gets out of your way so your team can build, deploy, and scale faster and more efficiently. New accounts get $100 in credit to use in your first 60 days.
Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com.
Rollbar – We catch our errors before our users do because of Rollbar. Resolve errors in minutes, and deploy your code with confidence. Learn more at rollbar.com/changelog.
Linode – Our cloud server of choice. Deploy a fast, efficient, native SSD cloud server for only $5/month. Get 4 months free using the code changelog2018. Start your server - head to linode.com/changelog
Featuring:
Cormac Brick – Website, X
Mike Del Balso – Website
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Show Notes:
Intel’s Movidius Group
OpenVINO Toolkit
ngraph
Uber’s Michelangelo
Upcoming Events:
Register for upcoming webinars here!
Machine Learning Platforms at Uber with Mike Del Balso - TWiML Talk #115
In this episode, I speak with Mike Del Balso, Product Manager for Machine Learning Platforms at Uber. Mike and I sat down last fall at the Georgian Partners Portfolio conference to discuss his presentation “Finding success with machine learning in your company.” In our discussion, Mike shares some great advice for organizations looking to get value out of machine learning. He also details some of the pitfalls companies run into, such as not have proper infrastructure in place for maintenance and monitoring, not managing their expectations, and not putting the right tools in place for data science and development teams. On this last point, we touch on the Michelangelo platform, which Uber uses internally to build, deploy and maintain ML systems at scale, and the open source distributed TensorFlow system they’ve created, Horovod. This was a very insightful interview, so get your notepad ready! Vote on our #MyAI Contest! Over the past few weeks, you’ve heard us talk quite a bit about our #MyAI Contest, which explores the role we see for AI in our personal lives! We received some outstanding entries, and now it’s your turn to check them out and vote for a winner. Do this by visiting our contest page at https://twimlai.com/myai. Voting remains open until Sunday, March 4th at 11:59 PM Eastern time. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/115.