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Introducing Cloudera Fine Tuning Studio for Training, Evaluating, and Deploying LLMs with Cloudera AI
- Cloudera Fine Tuning Studio, a one-stop-shop studio application that covers the entire workflow and lifecycle of fine tuning, evaluating, and deploying fine-tuned LLMs in Cloudera’s AI Workbench.
- Large Language Models (LLMs) can optimize costs, save time, and increase productivity for both internal and external use cases in enterprises.
- Several LLMs are publicly available through APIs from OpenAI, Anthropic, AWS, and others, but enterprises often can’t use them because of private data sources, company-specific formatting, and hosting costs.
- Fine tuning can solve these issues to provide specific coding formats and standards, reduce training time, and achieve cost benefits with smaller models customized to specific tasks or use cases.
- Cloudera's Fine Tuning Studio enables users to track all resources for fine tuning and evaluating LLMs, build and test prompts, train new adapters for LLMs, evaluate trained LLMs, and deploy them to production environments.
- Fine Tuning Studio comes with MLFlow experiments integration, several ways to test the performance of trained models and compare their performance, and deep integrations with Cloudera’s AI suite of tools to deploy, host, and monitor LLMs.
- Fine Tuning Studio ships with a convenient Python client that makes calls to the Fine Tuning Studio’s core server, enabling data scientists to build and develop their own training scripts.
- Cloudera’s Fine Tuning Studio is available to Cloudera AI customers as an Accelerator for Machine Learning Projects (AMP).
- The team behind Fine Tuning Studio aims to provide customers with a streamlined approach to fine tune any model, on any data, for any enterprise application.
- Fine Tuning Studio can be utilized to fine tune smaller, cost-effective models to perform specific tasks, outperforming larger, more generalized models.
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