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Weights & Biases
Master Experiment Tracking, Model Monitoring, and Collaboration with Weights & Biases (W&B)Preview Weights & Biases course
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Log and visualize your training experiments in real-time
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Compare model performance across runs using interactive dashboards
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Track hyperparameters, gradients, weights, and losses
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Version datasets and label changes over time
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Build collaborative reports with visualizations and summaries
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Automate model tracking within Jupyter Notebooks and pipelines
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A CNN classification tracker comparing multiple architectures
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A hyperparameter sweep for model tuning using W&B Sweeps
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A team dashboard for tracking NLP experiments and model accuracy
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Machine Learning Engineers and Data Scientists
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Deep Learning Practitioners using PyTorch or TensorFlow
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Research teams needing version control and reproducibility
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MLOps professionals managing large-scale model pipelines
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Beginners aiming to implement experiment tracking from day one
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Install and Connect Early – Set up your W&B account and project workspace
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Log Everything – Track hyperparameters, metrics, gradients, and artifacts
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Compare Results Visually – Use dashboards and parallel coordinates
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Automate Sweeps – Use configuration files to explore parameter space
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Use Artifacts for Datasets and Models – Maintain version control
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Create Reports – Summarize experiments and share with collaborators
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Integrate with MLOps Pipelines – Use W&B with Docker, Airflow, or MLflow
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By the end of this course, you will be able to:
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Set up and manage W&B projects and workspaces
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Log metrics, losses, and artifacts during training runs
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Conduct hyperparameter sweeps and visualize performance
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Use artifacts for dataset and model versioning
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Build custom dashboards and share visual reports
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Integrate W&B into deep learning pipelines and research notebooks
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Ensure reproducibility and auditability of all ML experiments
Course Syllabus
Module 1: Introduction to Weights & Biases
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What is W&B and Why Use It?
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Creating a Project and Logging In
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Installation and Environment Setup
Module 2: Logging Training Metrics
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Initializing W&B in Your Python Script
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Logging Loss, Accuracy, and Hyperparameters
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Visualizing Live Training Progress
Module 3: Comparing Experiments
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Creating Interactive Comparison Dashboards
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Viewing Tables, Graphs, and Media
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Using Group Tags and Notes
Module 4: Hyperparameter Tuning with Sweeps
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Creating Sweep Config Files
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Running Random, Grid, and Bayesian Sweeps
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Selecting the Best Model
Module 5: Dataset and Model Versioning (Artifacts)
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Uploading and Managing Artifacts
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Linking Artifacts in Runs
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Dataset Lineage and Model Tracking
Module 6: Collaboration and Reports
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Sharing Projects and Collaborating in Teams
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Creating Visual Reports for Experiments
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Managing Teams and Permissions
Module 7: Advanced Logging and Custom Visualizations
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Logging Images, Videos, and Gradients
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Adding Custom Charts and Panels
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Embedding W&B in Jupyter Notebooks
Module 8: Integration with ML Frameworks
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Using W&B with PyTorch Lightning
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Logging with TensorFlow and Keras Callbacks
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Scikit-learn Integration
Module 9: MLOps and Deployment Integration
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Logging from Docker and CLI
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W&B with MLflow, DVC, and Airflow
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Monitoring and Production Feedback Loops
Module 10: Final Projects and Case Studies
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Build a Full Training Tracker
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Run a Multi-Model Hyperparameter Benchmark
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Version and Deploy Models with Audit Trail
Module 11: Weights & Biases Interview Questions & Answers
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Platform Features and Use Cases
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Logging, Sweeps, and Artifacts
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Integration and Automation Scenarios
Upon successful completion, learners will receive a Certificate of Completion from Uplatz, validating their skills in experiment tracking, model management, and team collaboration using Weights & Biases. This certification adds credibility for roles in ML engineering, research, and MLOps operations.
Weights & Biases is used across startups, research institutions, and AI companies to scale and manage machine learning workflows. This course prepares you for roles such as:
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Machine Learning Engineer
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MLOps Specialist
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AI Research Assistant
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Data Scientist (Deep Learning)
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Experimentation Workflow Engineer
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What is Weights & Biases (W&B)?
Answer: W&B is a platform for tracking and visualizing machine learning experiments. It helps manage metrics, models, datasets, and training artifacts to ensure reproducibility and collaboration. -
What are the core features of W&B?
Answer:-
Experiment tracking
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Hyperparameter sweeps
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Dataset and model versioning (artifacts)
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Dashboards and reports
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Collaboration and integrations
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How do you log a training run with W&B?
Answer: You initialize W&B withwandb.init()and log values usingwandb.log({'loss': ..., 'accuracy': ...}). The logs are synced to your online dashboard in real time. -
What is a sweep in W&B?
Answer: A sweep is an automated process for running multiple experiments with different hyperparameter combinations to optimize model performance. -
How does W&B handle version control?
Answer: W&B uses artifacts to version datasets and models. Each version is stored, tracked, and linked to specific runs, allowing full reproducibility. -
Can W&B be used in Jupyter Notebooks?
Answer: Yes. You can usewandb.init()andwandb.log()in notebooks, and even embed live W&B dashboards directly into your notebook outputs. -
What are the integration options for W&B?
Answer: W&B integrates with PyTorch, TensorFlow, Keras, Scikit-learn, MLflow, Docker, and orchestration tools like Airflow and Kubernetes. -
What’s the difference between a run and a project in W&B?
Answer: A run is a single experiment, while a project groups multiple runs for comparison and organization under a common name. -
How can you monitor production models with W&B?
Answer: W&B allows logging from production environments using APIs or CLI. You can track inference statistics, drift, and user feedback for deployed models. -
Why is W&B useful for team collaboration?
Answer: W&B allows multiple users to share runs, visualize results together, comment on experiments, and build joint reports, improving transparency and teamwork.