Weights & Biases Mastery: Experiment Tracking, Visualization & ML Workflow
Learn how to track, visualize, and manage machine learning experiments using Weights & Biases (W&B).
🏷️
Weights_Biases
W&B
Experiment
Tracking
MLOps
Monitoring
AIDevelopment
Hyperparameter_Tuning
Versioning
Workflow
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- ▶️ Recorded Course
- Course Instructor: Md Abdullah Al Mamun
- Category: DevOps
- Sub Category: Monitoring Tools
- Child Category: Weight&Bias
- Last updated: 05/2026
- Language: English
| Course Style Plan | ||||
|---|---|---|---|---|
| 🎬 Course Mode | ⏰ Daily Spend | 🗓️ Weekly Schedule | 📊 Total Class | ⏳ Course Duration |
| ▶️Recorded Course | 1 Hours | Mon, Wed, Sat | 1 | 1 Hours |
Weights & Biases Mastery: Experiment Tracking, Visualization & ML Workflow
DevOps
>
Monitoring Tools
>
Weight&Bias
Weights_Biases
W&B
Experiment
Tracking
MLOps
Monitoring
AIDevelopment
Hyperparameter_Tuning
Versioning
Workflow
Learn how to track, visualize, and manage machine learning experiments using Weights & Biases (W&B).
Course Requirements
- 💻 Basic Computer Knowledge: Familiarity with using a computer, installing software, and browsing the internet.
- 🧠 Basic Programming Understanding (Optional but Helpful): Knowledge of any programming language (like C, Java, or JavaScript) will be beneficial.
- ⚙️ Laptop / PC Configuration: Minimum 8GB RAM (recommended), SSD storage, and a modern processor for smooth development.
- 🌐 Internet Connection: Stable internet connection for downloading tools, dependencies, and accessing resources.
- 🧑💻 Software Installation: Ability to install tools like Flutter SDK, Android Studio, or VS Code.
- 📱 Android Device (Optional): A physical Android phone for real-device testing (emulator can also be used).
- 🧩 Google Account: Required for accessing Google services and publishing apps on Play Store.
- 🔥 Willingness to Learn & Practice: Dedication to coding, debugging, and building real-world projects.
Course Details
This course is designed to help you master Weights & Biases (W&B), a powerful platform for tracking, visualizing, and managing machine learning experiments efficiently.
You’ll begin with an introduction to Weights & Biases, understanding its core features, benefits, and how it fits into modern machine learning workflows.
Next, you’ll learn how to set up W&B in your projects, log experiments, track hyperparameters, and monitor training metrics in real time. The course will guide you through visualizing results using dashboards, charts, and reports to better understand model performance.
You’ll also explore advanced features such as experiment comparison, model versioning, artifact management, and team collaboration—helping you scale your ML workflow professionally.
By the end of this course, you will:
- Understand W&B and its role in ML workflows
- Track experiments and log metrics effectively
- Visualize training results with interactive dashboards
- Compare multiple experiments and optimize models
- Manage datasets and model artifacts
- Collaborate with teams using shared reports
This course is perfect for data scientists, ML engineers, AI researchers, and developers who want to streamline and scale their machine learning experimentation process.
Course content
4 sections • 7 lectures • 02h 37m total length
Chapter
1. Intro about W&B
1 lecture
00h 03m
▶
Log Your First Run With W&B
Video
3m
Learn how to log your first machine learning experiment using Weights & Biases (W&B) and understand the basics of experiment tracking, metrics visualization, and run management. This tutorial demonstrates how to integrate W&B into your ML workflow to monitor training performance, organize experiments, and improve reproducibility efficiently.
Chapter
2. Integrate Weights & Biases
2 lectures
00h 44m
▶
Integrate Weights & Biases with PyTorch
Video
24m
Learn how to integrate Weights & Biases (W&B) with PyTorch to track experiments, monitor training performance, visualize metrics, and manage machine learning workflows efficiently. This tutorial demonstrates step-by-step setup, logging, model tracking, and real-time experiment monitoring to improve deep learning development and reproducibility.
▶
🥕 Integrate Weights & Biases with Keras
Video
20m
Learn how to integrate Weights & Biases with Keras to track machine learning experiments, visualize training metrics, and manage deep learning workflows efficiently. This tutorial demonstrates how to connect Keras models with Weights & Biases for experiment logging, model monitoring, and improved AI development productivity.
Chapter
3. Version Control by W&B
2 lectures
01h 09m
▶
🏺 Version Control Data and Models with W&B Artifacts
Video
36m
Learn how to version control datasets, machine learning models, and experiment outputs using Weights & Biases Artifacts for better reproducibility and collaboration in AI workflows. This tutorial demonstrates how W&B Artifacts help track data lineage, manage model versions, and organize machine learning pipelines efficiently.
▶
⚡ Supercharge your Training with PyTorch Lightning + Weights & Biases
Video
33m
Learn how to accelerate deep learning model training using PyTorch Lightning and Weights & Biases for experiment tracking, visualization, and workflow automation. This tutorial demonstrates how to simplify PyTorch development, monitor training performance in real time, and build scalable machine learning pipelines efficiently.
Chapter
4. Log & Hyperparameters manage by W&B
2 lectures
00h 41m
▶
🎨 Log (Almost) Anything with Weights & Biases
Video
16m
Learn how to log and visualize almost any type of machine learning data using Weights & Biases, including metrics, images, videos, tables, models, and more for better experiment tracking and analysis. This tutorial demonstrates practical logging workflows that help AI developers monitor training progress, debug models, and improve collaboration in modern machine learning projects.
▶
🧹 Tune Hyperparameters Easily with W&B Sweeps
Video
25m
Learn how to optimize machine learning models efficiently using Weights & Biases Sweeps for automated hyperparameter tuning and experiment tracking. In this tutorial, you’ll explore how to configure sweeps, run multiple experiments, compare performance metrics, and improve model accuracy with minimal manual effort.
What you'll learn?
-
Write Python programs confidently from scratch
-
Analyze and manipulate datasets using NumPy and Pandas
-
Create professional data visualizations with Matplotlib and Seaborn
-
Build machine learning models with Scikit-learn
-
Perform exploratory data analysis on real-world datasets
What will be included in this course?
24/7 Access to Course Materials
Downloadable Resources
Certificate of Completion
Lifetime Access
Mobile & TV Access
Frequently Asked Question
OrbitOCP is an online classroom platform that offers live and recorded courses across various fields like DevOps, Cloud, Cyber Security, and Data Science.
You can enroll by selecting your desired course, clicking the Enroll Now button, and completing the registration and payment process.
OrbitOCP offers both live interactive classes and recorded sessions so you can learn at your own pace.
Yes, most courses provide lifetime access to recorded materials and resources after enrollment.
Yes, you will receive a certificate of completion after successfully finishing the course requirements.
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Professional software engineer with 12+ years of experience leading end-to-end application development with project delivery. Results-driven Generative AI & Data Science Engineer with 8+ years of experience delivering enterprise-scale software and AI solutions across fintech, telecom, and education domains. Strong expertise in Python, machine learning, GenAI frameworks, and DevSecOps, with hands-on experience in model fine-tuning, NLP pipelines, vector databases, and cloud platforms. Proven leader in building secure, scalable systems, translating data into business insights, and driving end-to-end AI projects from design to deployment.