MLA 012 Docker for Machine Learning Workflows

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Primary technology recommendations for building a customer-facing machine learning product include React and React Native for the front end, serverless platforms like AWS Amplify or GCP Firebase for authentication and basic server/database needs, and Postgres as the rela ...

Builders can scale ML from simple API calls to full MLOps pipelines using SST on AWS, utilizing Aurora pgvector for search and Spot instances for 90 percent cost savings. External platforms like Modal or GCP Cloud Run provide superior serverless GPU options for real-time inferenc ...  Show more

SummaryIn this episode of the Data Engineering Podcast Prashanth Rao, an AI engineer at KuzuDB, talks about their embeddable graph database. Prashanth explains how KuzuDB addresses performance shortcomings in existing solutions through columnar storage and novel join algorithms. ...  Show more

Episode 13: What impact will AI-generated content have on the entertainment industry? Matt Wolfe ( https://x.com/mreflow) and Nathan Lands ( https://x.com/NathanLands) dive into this topic, envisioning a future where AI generates interactive movies and complex gaming worlds with in ...  Show more

SummaryIn this episode of the AI Engineering Podcast Mark Brooker, VP and Distinguished Engineer at AWS, talks about how agentic workflows are transforming database usage and infrastructure design. He discusses the evolving role of data in AI systems, from traditional models to m ...  Show more

Our guest today is Loubna Ben Allal, Machine Learning Engineer at Hugging Face 🤗 . In our conversation, Loubna first explains how she built two impressive code generation models: StarCoder and StarCoder2. We dig into the importance of data when training large models and what can ...  Show more