Challenges in Using LLM-as-a-Judge for Production Evaluations
Explore key techniques and challenges in using LLMs as evaluation tools for AI applications, with insights on improving RAG-based systems and implementing production-grade assessments.
LLMOps - LLM Bootcamp
Explore LLMOps principles for improving language model applications, covering model selection, prompt management, testing strategies, evaluation metrics, deployment, and test-driven development.
Quick Wins Across the Enterprise with Responsible AI
Explore rapid AI prototyping, use case identification, and responsible enterprise adoption. Learn about Microsoft AI services, Kyndryl's LLMOps framework, and human-centric implementation for balanced speed and responsibility.
End-to-End AI App Development: Prompt Engineering to LLMOps
Comprehensive guide to Azure AI's features for streamlining AI app development, from prototyping to deployment. Learn to maximize LLM capabilities and harness Generative AI potential with Azure AI Studio.
MLOps: Why DevOps Solutions Fall Short in the Machine Learning World
Explore MLOps challenges, differences from DevOps, and solutions for efficient machine learning development. Learn about GitLab's approach and the emerging field of LLMOps.
Building the Fine-Tuning Pipeline for Alignment of LLMs
Explore key aspects of aligning LLMs and set up infrastructure for a versatile alignment pipeline. Learn data collection, prompt tuning, and workflow management techniques.
Traceability and Observability in Multi-Step LLM Systems - LLMOps
Explore advanced techniques for implementing traceability and observability in multi-step LLM systems, focusing on RAG systems and leveraging OpenTelemetry for deep insights.
Vector and Keyword Search for Enhanced LLM Performance
Explore hybrid search techniques combining keyword and vector algorithms to enhance LLM performance, including metadata filtering, TF/IDF ranking, and advanced vector search methods.
Private RAG with Open Source and Custom LLMs - BentoML and OpenLLM
Explore practical considerations for building private RAG applications using open source and custom LLMs. Learn about OpenLLM and optimize inference performance for RAG deployments.
Config-Based Development for LLMs: Versioning, Routing, and Evaluating LLMs
Explore config-based development for LLMs, covering versioning, routing, and evaluation techniques to transition Generative AI apps from prototype to production efficiently.
Fine-tuning LLMs with Hugging Face SFT and QLoRA - LLMOps Techniques
Explore advanced LLM fine-tuning techniques, from specialized tuning to instruction enhancement. Learn dataset preparation, model optimization, and leverage powerful libraries for efficient fine-tuning and deployment.
Orchestrating RAG: Retrieval, Canopy, and Pinecone
Explore orchestrating RAG pipelines, optimizing retrieval strategies, and leveraging Canopy and Pinecone for scalable AI applications with billions of documents.
Evaluating LLM-Based Apps - Deepchecks LLM Validation
Explore LLM hallucinations, evaluation methodologies, and golden sets. Witness a live demo of Deepchecks' new LLM evaluation module for robust performance benchmarking and quality assurance.
Securing AI and LLM-based Applications: Best Practices
Explore security challenges in AI and LLM applications, learn attack scenarios, and discover best practices for mitigating risks and maintaining application integrity.
Building LLM-Powered Apps: Best Practices for Vector Databases and LLMOps
Explore best practices for LLM-based app development, covering design considerations, technical aspects, and deployment strategies for optimal performance and scalability.
Advanced RAG: Chunking, Embeddings, and Vector Databases
Explore advanced RAG concepts: chunking for efficient retrieval, embeddings for text representation, and vector databases for optimized storage and retrieval in LLM applications.
Leveraging Open Source Large Language Models for Production
Explore open-source LLMs, compare with proprietary options, understand economic aspects, evaluate performance, and learn to operate and fine-tune models using dstack.
Building an LLMOps Stack for Large Language Models
Explore LLMOps architecture: MLFlow for LLMs, Vector Databases, RAG strategies, and prompt evaluation. Gain insights into optimizing and deploying large language models effectively.
ML and MLOps with Databricks: LLMOps, MLFlow, Transformers, and Databricks
Explore LLMOps, MLFlow, and Databricks for advanced machine learning operations. Master transformers and enhance your data science skills.
LLMOps with Langchain, OpenAI, and Faiss on Databricks - Creating a Retrieval QA Tool
Develop a Retrieval QA tool using Langchain, OpenAI, and Faiss. Learn to save components in Databricks' MLFLOW, work with Model Governance, and perform inference after chain retrieval.
ML and MLOps with Databricks: LLMOps, MLFlow, OpenAI, and Databricks
Build a code reviewer using GPT-4, encapsulate it in MLflow, and deploy for inference in Databricks pipelines. Enhance your ML workflow with LLMOps techniques.