Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

In deep learning, computer vision and LLM projects where training runs last hours or days, teams need live GPU telemetry, interactive inspection of image and text predictions, and end-to-end data-to-model lineage. This course covers the enterprise use of the Weights & Biases (W&B) platform across all of its core modules.

PyTorch and Hugging Face integrations, visual and textual error analysis with W&B Tables, distributed hyperparameter tuning with W&B Sweeps, dataset and model versioning with W&B Artifacts, Registry automations and LLM tracing/evaluation with W&B Weave are practised hands-on. Participants build an interactive project workspace, a verified lineage graph and a collaborative W&B Report for stakeholder sign-off.

Who Should Attend?

  • AI engineers and data scientists training deep learning, computer vision and LLM models
  • Research and product teams currently using or migrating to Weights & Biases
  • MLOps specialists setting up standardised reporting and deployment automations
  • Prerequisite: Python and experience training models with PyTorch or Hugging Face

Key Takeaways

  • Monitor PyTorch, Lightning and Hugging Face Trainer runs alongside live GPU and system telemetry in W&B
  • Filter, group and compare misclassified images, audio or text samples interactively using W&B Tables
  • Orchestrate parallel hyperparameter searches across multiple GPUs or nodes with W&B Sweep agents
  • Track exact dataset-to-model lineage using W&B Artifacts and reference buckets
  • Connect W&B Registry webhooks to CI/CD pipelines and evaluate LLM apps with W&B Weave

Curriculum

W&B Experiments and Deep Learning Integrations
  • The wandb SDK, project/group/job_type structure, offline mode and enterprise S3/GCS storage settings
  • Tracking gradients, weights and GPU bottlenecks with PyTorch, Lightning, Ultralytics and Hugging Face Trainer
Interactive Error Analysis with W&B Tables and Rich Media
  • Logging images, bounding boxes, segmentation masks, audio and text generations as rich media
  • Querying W&B Tables interactively, comparing predictions across runs and slicing failure modes
Distributed Hyperparameter Tuning with W&B Sweeps
  • Configuring Bayesian, random and grid sweeps in YAML/Python with Hyperband early termination
  • Launching parallel Sweep Agents across multi-GPU nodes and interpreting parameter-importance plots
W&B Artifacts, Lineage Graphs and Model Registry
  • Versioning datasets and checkpoints by digest or external S3/GCS reference URI without duplicating storage
  • End-to-end input-run-output lineage DAGs, W&B Registry collections, aliases and CI/CD webhook automations
Collaborative W&B Reports and LLM Tracing with Weave
  • Authoring live, reproducible technical review documents for stakeholders using W&B Reports
  • Tracing LLM/agent calls, running dataset evaluations and tracking token costs with W&B Weave (@weave.op)

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