Operationalizing Local LLMs Responsibly for MLOps

Abstract: 

I. Introduction to LLMs (5 mins)
Defining foundation of large language models
Use cases like search, content generation, programming

II. Architecting High-Performance LLM Pipelines (15 mins)
Storing training data efficiently at scale
Leveraging specialized hardware accelerators
Optimizing hyperparameters for cost/accuracy
Serving inferences with low latency

III. Monitoring and Maintaining LLMs (10 mins)
Tracking model accuracy and performance
Retraining triggers to stay performant
Evaluating inferences for bias indicators
Adding human oversight loops

IV. Building Ethical Guardrails for Local LLMs (10 mins)
Auditing training data composition
Establishing process transparency
Benchmarking rigorously on safety
Implementing accountability for production systems

V. The Future of Responsible Local LLMs (5 mins)
Advances that build trust and mitigate harms
Policy considerations around generative models
Promoting democratization through education

Bio: 

Bio Coming Soon!

Open Data Science

 

 

 

Open Data Science
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Cambridge, MA 02142
info@odsc.com

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