LLM Observability 101

Over half (53%) of teams say they plan to deploy LLM apps into production in the next 12 months or “ASAP” – however, nearly as many (43%) cite issues like accuracy of responses and hallucinations or needless abstraction as barriers to implementation. 

How can you deploy LLMs reliably and responsibly?

LLM Observability 101 covers:

  • LLM Benchmarks
  • Common Use Cases
  • LLM Model Evals and LLM System Evals
  • LLM Traces and Spans
  • Fine-Tuning
  • Benchmarking Evaluation of RAG
  • LLM Guardrails
  • Read the eBook

    About the author

    Aparna Dhinakaran
    Co-founder & Chief Product Officer

    Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a pioneer and early leader in machine learning (ML) observability. A frequent speaker at top conferences and thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michealangelo. She has a bachelor’s from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.

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