Introducing AI Model Sharing with Databricks
databricks
JANUARY 31, 2024
Today, we're excited to announce that AI model sharing is available in both Databricks Delta Sharing and on the Databricks Marketplace. With Delta.
databricks
JANUARY 31, 2024
Today, we're excited to announce that AI model sharing is available in both Databricks Delta Sharing and on the Databricks Marketplace. With Delta.
AWS Machine Learning Blog
NOVEMBER 14, 2023
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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IBM Journey to AI blog
JANUARY 18, 2023
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.
AWS Machine Learning Blog
NOVEMBER 30, 2023
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.
ODSC - Open Data Science
APRIL 21, 2023
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.
The MLOps Blog
MARCH 21, 2023
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.
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