Google Colab Pro: Scalable Python and AI Workflows in the Cloud Training Course
Google Colab Pro provides a cloud-based environment designed for scalable Python development, delivering high-performance GPUs, extended runtimes, and enhanced memory capacity to support intensive AI and data science workloads.
This instructor-led training session, available either online or onsite, is tailored for intermediate Python users who intend to leverage Google Colab Pro for machine learning, data processing, and collaborative research within a powerful notebook interface.
Upon completing this training, participants will be capable of:
- Establishing and managing cloud-based Python notebooks using Colab Pro.
- Accessing GPUs and TPUs to accelerate computational tasks.
- Streamlining machine learning workflows by utilising popular libraries such as TensorFlow, PyTorch, and Scikit-learn.
- Integrating with Google Drive and external data sources to facilitate collaborative projects.
Course Format
- Interactive lectures and discussions.
- Ample exercises and practical practice.
- Hands-on implementation within a live laboratory environment.
Course Customisation Options
- For those seeking a customised training version of this course, please contact us to arrange the details.
Course Outline
Introduction to Google Colab Pro
- Colab versus 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 Visualisation
- Loading and analysing data from files, Google Sheets, or APIs
- Utilising Pandas, Matplotlib, and Seaborn
- Streaming and visualising 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 Optimisation and Best Practices
- Managing session lifetime and timeouts
- Efficient code organisation in notebooks
- Tips for long-running or production-level tasks
Summary and Next Steps
Requirements
- Experience with Python programming
- Familiarity with Jupyter notebooks and fundamental data analysis
- Understanding of common machine learning workflows
Audience
- Data scientists and analysts
- Machine learning engineers
- Python developers engaged in AI or research projects
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Google Colab Pro: Scalable Python and AI Workflows in the Cloud Training Course - Enquiry
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