The Model Performance Tracking with Weights & Biases course is designed to provide a comprehensive understanding of model performance monitoring using Weights & Biases (W&B).
Model Performance Tracking with Weights & Biases
The Model Performance Tracking with Weights & Biases course is designed to provide a comprehensive understanding of model performance monitoring using Weights & Biases (W&B). Weights & Biases is a popular tool used by data scientists and machine learning engineers to track experiments, visualize performance metrics, and streamline the model training and evaluation process. This course will introduce you to the essential tools provided by W&B to track and manage machine learning models throughout their lifecycle. Whether you are a beginner or an experienced professional, this course will equip you with the skills necessary to improve and scale your ML models effectively.
Throughout the course, you will gain hands-on experience using Weights & Biases to monitor and track key performance metrics such as accuracy, loss, and training time. By the end of the course, you will be proficient in visualizing and comparing experiment results, managing hyperparameters, and collaborating with team members. The course covers everything from basic setup to advanced features of W&B, enabling you to manage and optimize your machine learning models with ease.
This course is ideal for data scientists, machine learning engineers, and AI enthusiasts who are looking to improve their workflow for model tracking and performance monitoring. Whether you are working on small-scale projects or deploying models in production environments, this course will provide you with valuable tools to enhance model management. It is also suitable for researchers and practitioners who need to collaborate effectively and track the results of numerous experiments. A basic understanding of machine learning and Python programming is recommended, but prior experience with Weights & Biases is not required.
Understand the core principles of model performance tracking and how Weights & Biases supports this process.
Set up Weights & Biases for seamless experiment tracking and management.
Visualize and analyse model performance using W&B’s intuitive dashboard.
Efficiently manage and version models to ensure reproducibility and consistency.
Use Weights & Biases to optimize hyperparameters and improve model accuracy.
Collaborate effectively with teammates by sharing results and insights.
Detect model drift and implement performance monitoring strategies.
Explore advanced features of W&B for a more efficient model lifecycle.
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Learn the importance of tracking experiments and managing model performance. Get introduced to Weights & Biases, its features, and its role in the machine learning lifecycle.
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Discover how to set up your W&B account, install the library, and integrate it into your machine learning projects for efficient tracking and logging.
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Understand how to visualize key metrics such as accuracy, loss, and validation scores. Learn how to use W&B’s built-in tools for creating interactive plots and dashboards to monitor model performance.
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Explore best practices for model versioning and tracking changes across different versions of a model. Learn how to keep track of hyperparameters, configurations, and results across various experiments.
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Dive deep into hyperparameter tuning with W&B. Learn how to use W&B’s sweeps to automatically search for the best hyperparameters and improve model performance.
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Learn how to share your experiments and results with teammates. Explore collaboration features within W&B that allow multiple users to track and review model performance.
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Understand the concept of model drift and how to detect it using W&B. Learn how to set up monitoring and alerting systems to ensure model performance is maintained over time.
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Explore advanced features like Artefact Management, W&B Tables, and custom logging. Learn how to leverage these tools for even more powerful tracking and analysis.
Earn a Professional Certificate
Earn a certificate of completion issued by Learn Artificial Intelligence (LAI), recognised for demonstrating personal and professional development.
Jabir bin Zahidi
BS in Machine Learni...The course effectively teaches how to monitor, visualize, and improve machine learning models using...
The course primarily uses Python, as it is the most commonly used programming language for machine learning and integrates seamlessly with Weights & Biases.
While prior knowledge of machine learning is not mandatory, a basic understanding of machine learning concepts and Python programming will be helpful for following along with the exercises.
Yes! This course is self-paced, allowing you to work through the modules as per your schedule. You can revisit sections and practice as needed to solidify your understanding.
Model performance monitoring involves tracking and measuring how well a machine learning model performs on various tasks. It helps in evaluating metrics like accuracy, precision, and recall, as well as understanding the model’s behaviour over time, such as detecting over fitting or model drift.
In machine learning, "weights" refer to the parameters that are learned by the model during training, while "biases" are additional parameters that allow the model to make predictions. Together, these weights and biases define how the model interprets the input data.
MLflow is an open-source platform used for managing the machine learning lifecycle, including experiment tracking, model versioning, and deployment. It is commonly used for logging experiments, sharing results, and ensuring reproducibility of models across different environments.
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