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Learn cloud computing fundamentals and advanced practices using Python through hands-on projects spanning multiple cloud platforms. Master multi-cloud onboarding strategies and understand various cloud service models including IaaS, PaaS, and SaaS. Build robust ETL pipelines in cloud environments and develop serverless solutions using cloud-native technologies. Explore containerization strategies for cloud deployment and implement continuous delivery pipelines specifically designed for machine learning engineering workflows. Dive into distributed computing concepts and learn how to leverage cloud infrastructure for data science applications. Gain practical experience with AutoML for rapid prototyping and implement MLOps containers using AWS and Google Cloud Platform. Master testing methodologies with comprehensive pytest techniques and explore Infrastructure as Code using AWS CDK. Work with AWS Lambda, Python Cloud9, and Boto3 for serverless development, and become proficient with AWS CloudShell for cloud-based development environments. Build production-ready Python microservices with continuous delivery pipelines from scratch and master DevOps practices including CI/CD, GitHub Actions, containers, and microservices architecture. Explore AWS machine learning certification pathways through a structured 52-week program and optimize Python code performance using GPUs with comparisons between CPU, JIT compilation, and CUDA acceleration. Develop software craftsmanship skills through key lessons and understand the differences between data-centric and model-centric MLOps workflows. Discover modern cloud developer workspaces as the future of software engineering environments and master Python functions for cloud computing applications.
