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Introducing AI Model Sharing with Databricks

databricks

Today, we're excited to announce that AI model sharing is available in both Databricks Delta Sharing and on the Databricks Marketplace. With Delta.

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Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker

AWS Machine Learning Blog

In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. One of the hottest areas in AI right now is generative AI, and for good reason. Generative AI offers powerful solutions that push the boundaries of what’s possible in terms of creativity and innovation.

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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

And then a framework will be introduced, which will show how these three concepts may lead to one another or be used with each other. Concept of lakehouse was made popular by Databricks. For these workloads, data lake vendors usually recommend extracting data into flat files to be used solely for model training and testing purposes.

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Welcome to a New Era of Building in the Cloud with Generative AI on AWS

AWS Machine Learning Blog

We believe generative AI has the potential over time to transform virtually every customer experience we know. Innovative startups like Perplexity AI are going all in on AWS for generative AI. And at the top layer, we’ve been investing in game-changing applications in key areas like generative AI-based coding.

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Databricks Introduces Dolly 2.0: The World’s First Open Instruction-Tuned LLM

ODSC - Open Data Science

CEO & Co-Founder of Databricks, Ali Ghodsi took to LinkedIn to introduce to the world, Dolly 2.0 — the world’s first open-source LLM that is instruction-following and fine-tuned on a human-generated instruction dataset licensed for commercial use. In a blog post , Databricks opened up about Dolly 2.0.

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Definite Guide to Building a Machine Learning Platform

The MLOps Blog

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.