W&B Model Registry: Promote Models to Production
The W&B Model Registry isn't just a versioned file store; it's a living catalog where models transition through distinct stages, and "Production" is the.
Wandb Articles
48 articles
W&B Offline Mode: Log and Sync Without Internet
You can run Weights & Biases completely offline, logging all your experiments and syncing them later when you have a connection.
W&B Self-Hosted Server: On-Premise Deployment
The most surprising thing about W&B self-hosted deployments is that they often end up being more complex to manage than cloud-based solutions, precisely.
W&B Privacy: Mask PII Before Logging
Logging sensitive information like personally identifiable information PII to Weights & Biases W&B can expose it in your project's UI, which is accessib.
W&B Production ML Workflow Best Practices
The most surprising thing about Weights & Biases is how much it doesn't change your workflow, and yet fundamentally alters your understanding of it.
W&B Prompts: LLM Evaluation and Tracing
Prompts in Weights & Biases aren't just about collecting text inputs; they're a fundamental mechanism for understanding and debugging the decision-makin.
W&B PyTorch Training Integration: Auto-Log Metrics
Weights & Biases W&B can automatically log a vast array of metrics from your PyTorch training runs without you needing to write explicit wandb.
W&B Reports: Build Shareable ML Documentation
W&B Reports can feel like just a fancy dashboard, but they're actually a dynamic, programmatic way to build living documentation for your ML projects, d.
W&B RLHF Training Logging: Track Reward Model Runs
The most surprising thing about logging reward model runs in W&B is that you're not just logging a single number; you're logging a complex decision-maki.
W&B Run Comparison: Parallel Coordinates for Sweeps
Parallel coordinates plots are actually a poor tool for comparing individual runs within a sweep, but they excel at revealing emergent properties of the.
W&B SageMaker Training Integration
Weights & Biases W&B logging for SageMaker training jobs is designed to provide seamless experiment tracking and visualization directly from your SageMa.
W&B Sweeps: Bayesian Hyperparameter Optimization
A W&B sweep can find a better model than random search with fewer trials, but it's not magic; it's a sophisticated statistical model trying to guess whe.
W&B Sweeps: Automate Hyperparameter Search
W&B Sweeps: Automate Hyperparameter Search — practical guide covering wandb setup, configuration, and troubleshooting with real-world examples.
W&B Tables: Explore Data and Predictions Visually
W&B Tables aren't just a fancy spreadsheet for your ML metrics; they're a fundamental shift in how you debug and understand your models by treating data.
W&B Team Collaboration: Shared Projects and Roles
Shared projects and roles in W&B are the system's way of letting multiple people work on the same experiments without stepping on each other's toes or c.
W&B Vertex AI Training Integration
W&B Vertex AI Training Integration — Vertex AI Training jobs can't connect to W&B. The google.cloud.aiplatform.training.v1.TrainingServic.
W&B vs MLflow: Choose the Right ML Tracking Tool
Weights & Biases W&B and MLflow are both powerful tools for experiment tracking in machine learning, but they excel in different areas and cater to slig.
W&B Weave: LLM Observability and Evaluation Platform
Weave isn't just another logging tool; it’s a system designed to give you deep visibility into how your LLM applications actually behave in production, .
W&B Webhooks: Automate Actions on Run Events
Webhooks let you trigger actions in other systems when something interesting happens in W&B, like a model finishing training or a metric crossing a thre.
W&B Access Control RBAC: Manage Teams and Permissions
Teams are the fundamental building blocks for W&B access control, not individual users. Let's see how this plays out with a practical example
W&B Alerts: Notify Slack and Email on Metric Thresholds
You can configure W&B alerts to notify Slack and email when a metric crosses a certain threshold during your training runs.
W&B API: Programmatic Run Management and Querying
You can delete W&B runs programmatically, but it's not a straightforward run. delete call; instead, you're actually archiving them, and the actual delet.
W&B Artifacts: Version Models and Datasets
W&B Artifacts are not just fancy file storage; they are a system for tracking the lineage of everything that goes into making a machine learning model, .
W&B Azure ML Integration: Track Experiments
Weights & Biases W&B can track your Azure ML experiments by logging your metrics, parameters, and model artifacts to the W&B platform.
W&B Benchmark Comparison: Build Model Leaderboards
Building a model leaderboard in Weights & Biases W&B isn't just about displaying results; it's about creating a dynamic, reproducible system for compari.
W&B Callbacks: Keras, PyTorch Lightning Integration
Weights & Biases W&B callbacks for Keras and PyTorch Lightning don't just log metrics; they act as intelligent agents, dynamically influencing your trai.
W&B Config Management: Track Hyperparameters Per Run
Weights & Biases W&B config management isn't just about logging hyperparameters; it's about creating an immutable, auditable record of your experiment's.
W&B Confusion Matrix and PR Curve Logging
The most surprising thing about W&B's confusion matrix and PR curve logging is that they aren't just static images; they're live, interactive components.
W&B Cost Optimization: Control Storage and Compute
W&B Cost Optimization: Control Storage and Compute — practical guide covering wandb setup, configuration, and troubleshooting with real-world examples.
W&B Custom Metrics: Log and Visualize Any Scalar
Logging custom scalar metrics in Weights & Biases W&B lets you track exactly what matters for your specific machine learning project beyond the standard.
W&B Data Pipeline Validation: Log Data Quality Metrics
This is about how Weights & Biases W&B helps you catch data issues before they mess up your training, by letting you log data quality metrics directly i.
W&B Distributed Training: Log Across Multiple GPUs
Logging metrics from multiple GPUs in a distributed training setup can feel like trying to get a single, coherent story from a room full of people shout.
W&B Embedding Projector: Visualize High-Dimensional Vectors
The W&B Embedding Projector is a powerful tool for visualizing high-dimensional data, like the embeddings generated by machine learning models.
W&B Enterprise Security: SSO, Audit Logs, and Compliance
Single Sign-On SSO for W&B Enterprise isn't just about convenience; it's a fundamental shift in how your team accesses and interacts with your machine l.
W&B Environment Variables: Configure in CI/CD Pipelines
Weights & Biases W&B environment variables are the primary way you configure its behavior, especially within automated systems like CI/CD pipelines.
W&B Evaluation Metrics: Custom Scoring Functions
The most surprising thing about W&B evaluation metrics is that they aren't just for displaying results; they're a powerful, programmable way to guide yo.
W&B Experiment Tracking Quickstart: Log Your First Run
Logging your first W&B run isn't about just saving metrics; it's about creating a living document of your experiment that tells the whole story.
W&B Git Integration: Track Code Version Per Run
W&B Git Integration: Track Code Version Per Run — practical guide covering wandb setup, configuration, and troubleshooting with real-world examples.
W&B Gradient Logging: Debug Training Instability
The most surprising thing about W&B gradient logging is that it's not just for visualizing gradients; it's your first line of defense against training i.
W&B HuggingFace Trainer Integration: Auto-Log Training
The most surprising thing about the wandb HuggingFace Trainer integration is that it doesn't just log metrics; it automatically captures your entire tra.
W&B XGBoost and Scikit-Learn Integration
The most surprising thing about integrating Weights & Biases with XGBoost and Scikit-Learn is how little code you actually need to write to get massive .
W&B JAX and Flax Training Logging
The most surprising thing about logging JAX/Flax training with Weights & Biases is how little of your existing JAX/Flax code you actually need to touch.
W&B Kubernetes Agent: Launch Training Jobs on K8s
The W&B Kubernetes Agent lets you run your machine learning training jobs as pods on your Kubernetes cluster, managed by Weights & Biases.
W&B Large Artifact Upload: Optimize Speed and Cost
Artifacts in Weights & Biases are how you save and version your data, models, and other outputs. Uploading large artifacts can be slow and expensive, bu.
W&B Launch: Queue and Run Remote Training Jobs
The most surprising thing about W&B Launch is that it’s not just a fancy scheduler; it’s a distributed system that fundamentally changes how you think a.
W&B LLM Fine-Tuning Tracking: Full Guide
W&B LLM Fine-Tuning Tracking: Full Guide — practical guide covering wandb setup, configuration, and troubleshooting with real-world examples.
W&B Media Logging: Images, Audio, and Video
Logging media in Weights & Biases W&B doesn't just save files; it fundamentally changes how you debug and understand your model's behavior by making raw.
W&B Model Performance Monitoring in Production
Weights & Biases W&B doesn't just track your training runs; it can keep an eye on your production models too, flagging when they start to drift or perfo.