Week 5: Switch from Python to Rust to build solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and MLOps. Week 4: Build containers for ML and package solutions in a uniformed manner to enable deployment in Cloud systems that accept containers. Week 3: Develop operations pipelines, including DevOps, DataOps, and MLOps, with Github. Week 2: Apply ML and AI in practice through optimization, heuristics, and simulations. Week 1: Explore MLOps technologies and pre-trained models to solve problems for customers.
#Practice it gitup software
This course is for people working (or seeking to work) as data scientists, software engineers or developers, data analysts, or other roles that use ML.īy the end of the course, you will be able to use web frameworks (e.g., Gradio and Hugging Face) for ML solutions, build a command-line tool using the Click framework, and leverage Rust for GPU-accelerated ML tasks. The course covers end-to-end solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub Copilot to build solutions for machine learning (ML) and AI applications.
#Practice it gitup how to
Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.Ĥ. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.ģ. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.Ģ. Through this series, you will begin to learn skills for various career paths:ġ. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers.
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