Databricks inked a five-year partnership with Anthropic to offer Claude models directly through the Databricks Data Intelligence Platform. Databricks also highlighted a system to enhance large language model performance without requiring label data.
With the Anthropic deal, Databricks will be able to add Claude 3.7 Sonnet, Anthropic's latest LLM, natively to its platform. Databricks said the Anthropic's models can be paired with its own Databricks Mosaic AI models.
The Anthropic models are available on Databricks on AWS, Azure and Google Cloud.
Databricks' deal highlights how data platforms are increasingly looking to add top shelf models. For instance, Snowflake announced a partnership to add OpenAI's ChatGPT to its platform. The data platform space has seen a flurry of deals and partnerships. SAP and Databricks paired up on SAP Business Cloud. IBM acquired DataStax to add to its watsonx platform. Salesforce and Google Cloud also expanded a partnership that includes Data Cloud.
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According to Databricks, the plan is to enable Anthropic models to "reason over their enterprise data." Databricks Mosaic AI has the tools to build domain-specific AI agents on unique data. The hope for Databricks is that enterprises will pair up Anthropic and Mosaic AI.
What remains to be seen is how many Databricks customers are already leveraging Anthropic models via AWS and Google Cloud already.
Separately, Databricks outlined TAO (Test-time Adaptive Optimization), an approach that enhances LLM performance on a task without labeled data. Real-time compute augments an existing model for tuning. TAO only needs LLM usage data but can surpass traditional fine tuning on labeled examples.
According to Databricks, TAO can enable open-source models outperform proprietary models.
In a blog post, Databricks outlined how TAO improved performance of Llama 3.3 70B by 2.4%. Although TAO may not push Llama over proprietary models in all categories, Databricks does get the model close.
TAO is available in preview and Databricks said it will be embedded in several products in the future.
