Prepare Your Unstructured Data For Machine Learning And Computer Vision Without The Toil Using Activeloop
Data Engineering Podcast
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Is There a Way to Sort the Data?
Oce is building a new way to store data. Instead of having columns in the table, you will have tensers in the columns. This makes it much easier for the machine learning process to be able to interact with it. The main problem that deep learning models hate is that they don't really care about structure or vidios. That's what we are trying to fix by treating all our data as tenser rays.
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Summary
The vast majority of data tools and platforms that you hear about are designed for working with structured, text-based data. What do you do when you need to manage unstructured information, or build a computer vision model? Activeloop was created for exactly that purpose. In this episode Davit Buniatyan, founder and CEO of Activeloop, explains why he is spending his time and energy on building a platform to simplify the work of getting your unstructured data ready for machine learning. He discusses the inefficiencies that teams run into from having to reprocess data multiple times, his work on the open source Hub library to solve this problem for everyone, and his thoughts on the vast potential that exists for using computer vision to solve hard and meaningful problems.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Davit Buniatyan about Activeloop, a platform for hosting and delivering datasets optimized for machine learning
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Activeloop is and the story behind it?
- How does the form and function of data storage introduce friction in the development and deployment of machine learning projects?
- How does the work that you are doing at Activeloop compare to vector databases such as Pinecone?
- You have a focus on image oriented data and computer vision projects. How does the specific applications of ML/DL influence the format and interactions with the data?
- Can you describe how the Activeloop platform is architected?
- How have the design and goals of the system changed or evolved since you began working on it?
- What are the feature and performance tradeoffs between self-managed storage locations (e.g. S3, GCS) and the Activeloop platform?
- What is the process for sourcing, processing, and storing data to be used by Hub/Activeloop?
- Many data assets are useful across ML/DL and analytical purposes. What are the considerations for managing the lifecycle of data between Activeloop/Hub and a data lake/warehouse?
- What do you see as the opportunity and effort to generalize Hub and Activeloop to support arbitrary ML frameworks/languages?
- What are the most interesting, innovative, or unexpected ways that you have seen Activeloop and Hub used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Activeloop?
- When is Hub/Activeloop the wrong choice?
- What do you have planned for the future of Activeloop?
Contact Info
- @DBuniatyan on Twitter
- davidbuniat on GitHub
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.
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Links
- Activeloop
- Princeton University
- ImageNet
- Tensorflow
- PyTorch
- Activeloop Hub
- Delta Lake
- Tensor
- Wasabi
- Ray/Anyscale
- Humans In The Loop podcast
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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