Using AI To Transform Your Business Without The Headache Using Graft
AI Engineering Podcast
00:00
ML Science - How to Get Embeddings for a Deck
So this actually immediately got my head's gears turning whatnot. Let me frame a particular problem or like sub flavor of the magic problem. Given I have these cards available to me, let's actually first not talk about that. Let's say I have any cards I can choose to build any deck. A thing that pros regularly go through in the game is what deck should they bring to pro two or XYZ or some world championship? And it's very much dependent on the meta game that they're going to encounter. So with that kind of idea in mind, I don't know exactly how do we get there but assume I can get a notion of embeddings for individual cards.
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Summary
Machine learning is a transformative tool for the organizations that can take advantage of it. While the frameworks and platforms for building machine learning applications are becoming more powerful and broadly available, there is still a significant investment of time, money, and talent required to take full advantage of it. In order to reduce that barrier further Adam Oliner and Brian Calvert, along with their other co-founders, started Graft. In this episode Adam and Brian explain how they have built a platform designed to empower everyone in the business to take part in designing and building ML projects, while managing the end-to-end workflow required to go from data to production.
Announcements
Parting Question
Machine learning is a transformative tool for the organizations that can take advantage of it. While the frameworks and platforms for building machine learning applications are becoming more powerful and broadly available, there is still a significant investment of time, money, and talent required to take full advantage of it. In order to reduce that barrier further Adam Oliner and Brian Calvert, along with their other co-founders, started Graft. In this episode Adam and Brian explain how they have built a platform designed to empower everyone in the business to take part in designing and building ML projects, while managing the end-to-end workflow required to go from data to production.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
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- Your host is Tobias Macey and today I’m interviewing Brian Calvert and Adam Oliner about Graft, a cloud-native platform designed to simplify the work of applying AI to business problems
- Introduction
- How did you get involved in machine learning?
- Can you describe what Graft is and the story behind it?
- What is the core thesis of the problem you are targeting?
- How does the Graft product address that problem?
- Who are the personas that you are focused on working with both now in your early stages and in the future as you evolve the product?
- What are the capabilities that can be unlocked in different organizations by reducing the friction and up-front investment required to adopt ML/AI?
- What are the user-facing interfaces that you are focused on providing to make that adoption curve as shallow as possible?
- What are some of the unavoidable bits of complexity that need to be surfaced to the end user?
- What are the user-facing interfaces that you are focused on providing to make that adoption curve as shallow as possible?
- Can you describe the infrastructure and platform design that you are relying on for the Graft product?
- What are some of the emerging "best practices" around ML/AI that you have been able to build on top of?
- As new techniques and practices are discovered/introduced how are you thinking about the adoption process and how/when to integrate them into the Graft product?
- What are some of the new engineering challenges that you have had to tackle as a result of your specific product?
- What are some of the emerging "best practices" around ML/AI that you have been able to build on top of?
- Machine learning can be a very data and compute intensive endeavor. How are you thinking about scalability in a multi-tenant system?
- Different model and data types can be widely divergent in terms of the cost (monetary, time, compute, etc.) required. How are you thinking about amortizing vs. passing through those costs to the end user?
- Can you describe the adoption/integration process for someone using Graft?
- Once they are onboarded and they have connected to their various data sources, what is the workflow for someone to apply ML capabilities to their problems?
- One of the challenges about the current state of ML capabilities and adoption is understanding what is possible and what is impractical. How have you designed Graft to help identify and expose opportunities for applying ML within the organization?
- What are some of the challenges of customer education and overall messaging that you are working through?
- What are the most interesting, innovative, or unexpected ways that you have seen Graft used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Graft?
- When is Graft the wrong choice?
- What do you have planned for the future of Graft?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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
- Graft
- High Energy Particle Physics
- LHC
- Cruise
- Slack
- Splunk
- Marvin Minsky
- Patrick Henry Winston
- AI Winter
- Sebastian Thrun
- DARPA Grand Challenge
- Higss Boson
- Supersymmetry
- Kinematics
- Transfer Learning
- Foundation Models
- ML Embeddings
- BERT
- Airflow
- Dagster
- Prefect
- Dask
- Kubeflow
- MySQL
- PostgreSQL
- Snowflake
- Redshift
- S3
- Kubernetes
- Multi-modal models
- Multi-task models
- Magic: The Gathering
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