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Private AI vs Public AI: 4 Key Facts

Artificial intelligence (AI) has reached a tipping point in the public consciousness. Much of this has been driven by technology developments related to large language models (LLMs) and the release of generative AI tools, including ChatGPT from OpenAI. However, for enterprises shaping forward-looking AI strategy, a critical part of the conversation that needs to be addressed is the issue of private AI vs. public AI.

Generative AI in Software Testing

Generative AI in software testing is an advanced approach that augments human testers to make the testing process faster and more efficient while improving the quality of the software test results. At Testlio, we use artificial intelligence in software testing to assist expert QA testing managers in new test creation, faster test case refactoring, and creating issue reports with significantly fewer errors. In essence, generative AI helps enhance human performance.

MLOps for Generative AI with MLRun

The influx of new tools like ChatGPT spark the imagination and highlight the importance of Generative AI and foundation models as the basis for modern AI applications. However, the rise of generative AI also brings a new set of MLOps challenges. Challenges like handling massive amounts of data, large scale computation and memory, complex pipelines, transfer learning, extensive testing, monitoring, and so on. In this 9 minute demo video, we share MLOps orchestration best practices and explore open source technologies available to help tackle these challenges.

LLM ChatBot Augmented with Enterprise Data

This video demonstrates how to use an open source pre-trained instruction-following LLM (Large Language Model) to build a ChatBot-like web application. The responses of the LLM are enhanced by giving it context from an internal knowledge base. This context is retrieved by using an open source Vector Database to do semantic search.