Google Colab Pro: Scalable Python and AI Workflows in the Cloud

• Experience with Python programming
• Familiarity with Jupyter notebooks and basic data analysis
• An understanding of common machine learning workflows

Audience

• Data scientists and analysts
• Machine learning engineers
• Python developers working on AI or research projects

Google Colab Pro is a cloud-based environment for scalable Python development, offering high-performance GPUs, longer runtimes, and more memory for demanding AI and data science workloads.

This instructor-led, live training (online or onsite) is aimed at intermediate-level Python users who wish to use Google Colab Pro for machine learning, data processing, and collaborative research in a powerful notebook interface.

By the end of this training, participants will be able to:

• Set up and manage cloud-based Python notebooks using Colab Pro.
• Access GPUs and TPUs for accelerated computation.
• Streamline machine learning workflows using popular libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Integrate with Google Drive and external data sources for collaborative projects.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Google Colab Pro

• Colab vs. Colab Pro: features and limitations
• Creating and managing notebooks
• Hardware accelerators and runtime settings

Python Programming in the Cloud

• Code cells, markdown, and notebook structure
• Package installation and environment setup
• Saving and versioning notebooks in Google Drive

Data Processing and Visualization

• Loading and analyzing data from files, Google Sheets, or APIs
• Using Pandas, Matplotlib, and Seaborn
• Streaming and visualizing large datasets

Machine Learning with Colab Pro

• Using Scikit-learn and TensorFlow in Colab
• Training models on GPU/TPU
• Evaluating and tuning model performance

Working with Deep Learning Frameworks

• Using PyTorch with Colab Pro
• Managing memory and runtime resources
• Saving checkpoints and training logs

Integration and Collaboration

• Mounting Google Drive and loading shared datasets
• Collaborating via shared notebooks
• Exporting to GitHub or PDF for distribution

Performance Optimization and Best Practices

• Managing session lifetime and timeouts
• Efficient code organization in notebooks
• Tips for long-running or production-level tasks

Summary and Next Steps