Operationalize AI at scale with MLOps and LLMOps

Nearly three-quarters (72%) of organizations plan to increase their use of AI, and generative AI (GenAI) is driving even greater interest with 85% of enterprises believing that investment in the next 24 months is important or critical.

Ventana Research shows that the most common benefits reported by participants include gaining competitive advantage and improving customer experiences. Participants also report being able to respond faster to opportunities and to threats in the market, and an improvement in the bottom line, with increased sales and lower costs.

But scaling AI can be challenging, particularly with emerging GenAI techniques such as prompt-engineering, and retrieval augmented generation (RAG), and model fine-tuning - demanding even more. flexibility.

Balancing the needs of data scientists, managing intensive and variable workloads, investing in infrastructure, and governance throughout the data science lifecycle requires Machine Learning Operations (MLOps), or Large Language Model Operations (LLMOps).

Read the eBook and learn how:

• Purpose-built AI infrastructure optimized with an MLOps/LLMOps platform can help improve time-to-value for data science projects.
• An MLOps platform supporting LLMOps is critical for quickly governing, developing, deploying, and monitoring AI models in production applications.