Explore how reinforcement learning helps AI learn from trial and error, with key algorithms, methods like RLHF, and real-world applications.

Building a AI powered coding agent with Claude 3.5 Sonnet!

Discover how to use Amazon Bedrock in combination with W&B Weave to evaluate and compare Large Language Models (LLMs) for summarization tasks, leveraging Bedrock’s managed infrastructure and Weave’s advanced evaluation features.



A guide to PII redaction with AI, covering open-source tools, proprietary models, HIPAA compliance, and how logging supports secure data handling.

We will focus on understanding prompt injection attacks in AI systems and explore effective strategies to prevent against them!

Learn how to compare and evaluate OpenAI’s GPT models on Azure with W&B Weave on text summarization tasks, leveraging Azure’s managed infrastructure and Weave’s customizable evaluation tools.

Discover how LLaVA-o1 tackles reasoning challenges in multimodal AI with structured problem-solving. Learn about its dataset, capabilities, and performance analysis using W&B Weave.

A battle between Open Source Pixtral Large and closed source foundation models like Claude 3.5 Sonnet and GPT-4o Vision

A guide to building a RAG system with DSPy, and evaluating it with W&B Weave.

We'll explore how to combine multiple models together in order to create a more powerful AI model with ensemble learning.

A guide to Apple's new deep learning framework. It is faster than Torch on the M1?

A guide to evaluating a Langchain RAG system with RAGAs and Weights & Biases.

Learn how to enforce consistency on GPT-4o outputs, and build reliable Gen-AI Apps.

A practical guide on working with health data, safely, with multiple approaches for handling PHI

Learn to vision fine-tune GPT-4o on a custom dataset, with evaluation and tracking.

Learn to vision fine-tune GPT-4o on a custom dataset, with evaluation and tracking.

This tutorial explores LLM routers, inspired by the RouteLLM paper, covering training, evaluation, and practical use cases for managing LLMs effectively.

Unlock the power of knowledge distillation by learning how to efficiently transfer complex model insights from teacher to student models, step by step.