W&B
The AI Developer Platform
Weights & Biases helps AI developers build better models faster. Quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, and manage your ML workflows end-to-end.
AI in healthcare: Medical imaging with NVIDIA MONAI
Accurately segment organs and tissues in 3D medical scans. Implement cutting-edge models in PyTorch and build production-ready models with MLOps tools. Gain practical experience applying ML to real-world medical imaging challenges.Building LLM-Powered Applications
In this course, participants learn how to build LLM-powered applications using LLM APIs, LangChain and W&B Prompts. This includes practical exercises, quizzes and a final project assignment covering the entire process of designing, experimenting, and evaluating LLM-based apps.CI/CD for Machine Learning (GitOps)
In this course, participants learn how to streamline ML workflows and save valuable time by automating ML pipelines and deploying models with confidence. They use GitHub Actions and integrate W&B experiment tracking in this practical, hands-on learning experience. The course contains 5 hours of video content and a challenging, hands-on project ...Data Validation for Machine Learning
In this course, participants gain expertise in data validation to build robust ML pipelines, detect data drift, and manage data quality using tools like TensorFlow Data Validation and GATE.Developer's guide to LLM prompting
This course covers everything you need to get started with prompt engineering, from system prompts and structural techniques to model-specific strategies. Using a text-understanding use case, you will gain experience with code and industry knowledge.Effective MLOps - Model Development
In this course, participants learn about principled ML workflows for model development and experience working with W&B tools. In order to become certified, participants are required to complete a project showing their hands-on experience with the demonstrated MLOps tools and techniques. - Lesson 1 - Building an End-To-End Prototype (1.5 hrs) - Lesson ...Introduction to Weights & Biases Workshop
During this event participants learn basic Weights & Biases functionality including experiment tracking, W&B Tables, Sweeps, Artifacts, Dashboards/Reports, and Integrations. In the end participants are challenged to use their new skills in a Kaggle classification competition.LLM Apps: Evaluation
Develop techniques for building, optimizing, and scaling AI evaluators with minimal human input. Learn to build reliable evaluation pipelines for LLM applications by combining programmatic checks with LLM-based judges.
LLM Engineering: Structured Outputs
In this course, participants expand their understanding of LLMs with a focus on Pydantic and the Instructor Library. The course dives into the details of structured output handling, complex validation implementation, and efficient function integration in ML models.Machine Learning for Business Decision Optimization
The participants learned to optimize decision rules, translating machine learning predictions into actionable insights. During the course they discover how to achieve practical value and business impact by measuring performance using business metrics, and deploy ML models successfully.
