Dask Introduction unlocks the power of distributed computation for machine learning in a comprehensive one-day course for data and technology professionals. The day opens with a Dask overview. It introduces the task graph model that lets familiar NumPy, Pandas, and scikit-learn code scale across many cores without a full rewrite. From there, the course moves into Dask Distributed, the scheduler and cluster architecture behind real deployments. Attendees provision workers, submit jobs, and watch task execution unfold on the diagnostic dashboard.
The third module applies these building blocks to data analysis and machine learning. It walks through parallel DataFrame operations and distributed model training on datasets too large to fit comfortably in memory on a single machine. A closing module on advanced features covers delayed execution, custom task graphs, and tuning cluster resources for demanding workloads. Participants get hands-on experience deploying a cluster and building their own distributed machine learning and data analysis workflows through guided labs. This gives them the skills needed to put some of the Python ecosystem’s most scalable tools to work.
Who Should Attend
Developers, Data Scientists, Data Engineers, Machine Learning Engineers, MLOps professionals
What Attendees Will Learn
Upon completing Dask Introduction, participants will be able to:
- Gain an in-depth understanding of Dask, its architecture, deployment and use
- Acquire hands-on installing and deploying Dask
- Learn how to use Dask to build data analysis and machine learning workflows
- Understand Dask’s advanced features and functionality
- Develop the skills and expertise to build distributed machine learning workflows
Prerequisites
Participants should have intermediate programming skills (preferably in Python), an understanding of mathematics and statistics, basic familiarity with machine learning, a grasp of computer science fundamentals, familiarity with containers, Kubernetes, and basic command-line operations, and a willingness to review pre-course materials.
These prerequisites will help participants engage effectively with the course material and hands-on labs, making the learning experience more rewarding.