Build Your Second Brain One Piece At A Time
Data Engineering Podcast
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Introduction
Exploring the features of DAGS-TUR, a cloud-native orchestrator, and introducing DAGS-TUR Cloud for enterprise deployment. Additionally, the chapter covers the introduction of Starburst, a Data Lake analytics platform supporting various data technologies.
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Summary
Generative AI promises to accelerate the productivity of human collaborators. Currently the primary way of working with these tools is through a conversational prompt, which is often cumbersome and unwieldy. In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that developers already use. In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain.
Announcements
Parting Question
Generative AI promises to accelerate the productivity of human collaborators. Currently the primary way of working with these tools is through a conversational prompt, which is often cumbersome and unwieldy. In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that developers already use. In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!
- Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
- Your host is Tobias Macey and today I'm interviewing Tsavo Knott about Pieces, a personal AI toolkit to improve the efficiency of developers
- Introduction
- How did you get involved in machine learning?
- Can you describe what Pieces is and the story behind it?
- The past few months have seen an endless series of personalized AI tools launched. What are the features and focus of Pieces that might encourage someone to use it over the alternatives?
- model selections
- architecture of Pieces application
- local vs. hybrid vs. online models
- model update/delivery process
- data preparation/serving for models in context of Pieces app
- application of AI to developer workflows
- types of workflows that people are building with pieces
- What are the most interesting, innovative, or unexpected ways that you have seen Pieces used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pieces?
- When is Pieces the wrong choice?
- What do you have planned for the future of Pieces?
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. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- 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.
- Pieces
- NPU == Neural Processing Unit
- Tensor Chip
- LoRA == Low Rank Adaptation
- Generative Adversarial Networks
- Mistral
- Emacs
- Vim
- NeoVim
- Dart
- Flutter
- Typescript
- Lua
- Retrieval Augmented Generation
- ONNX
- LSTM == Long Short-Term Memory
- LLama 2
- GitHub Copilot
- Tabnine
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