This comprehensive program equips you with critical MLOps skills, combining programming knowledge in Python and Rust with the use of GitHub Copilot for productivity enhancement. You'll leverage platforms like Amazon SageMaker, Azure ML, and MLflow while learning to fine-tune Large Language Models (LLMs) using Hugging Face. Additionally, you'll gain expertise in deploying sustainable and efficient binary embedded models in the ONNX format, preparing you for success in the ever-evolving MLOps field. The series covers various career paths, including Data Science, Machine Learning Engineering, Cloud ML Solutions Architecture, and Artificial Intelligence (AI) Product Management.
Overview
Syllabus
Courses under this program:
Course 1: Python Fundamentals for MLOps
Course 2: DevOps, DataOps, MLOps
Course 3: MLOps Tools: MLflow and Hugging Face
Course 4: MLOps Platforms: Amazon SageMaker and Azure ML
Course 1: Python Fundamentals for MLOps
Master Python for Seamless MLOps: From Fundamentals to Automation and Workflow Efficiency.
Course 2: DevOps, DataOps, MLOps
Streamline AI Operations: Leverage DevOps, DataOps, and MLOps for End-to-End Machine Learning Solutions.
Course 3: MLOps Tools: MLflow and Hugging Face
Enhance your MLOps Journey: Explore MLflow and Hugging Face for Streamlined ML Lifecycle Management.
Course 4: MLOps Platforms: Amazon SageMaker and Azure ML
Elevate Your MLOps Game: Master AWS SageMaker and Azure ML for Production-Ready AI Solutions.
Courses
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Master Python for efficient Machine Learning Operations by building strong programming foundations, creating MLOps automation, and gaining applicable experience.
- Fundamentals of Python programming: Data types, functions, modules
- Testing techniques
- Data manipulation and analysis
- Work with datasets using Pandas
- Leveraging NumPy for data science
- Hands-on coding exercises
- Apply Python in MLOps workflows
This comprehensive course covers the essential Python skills for succeeding in MLOps roles. Through hands-on exercises, you'll learn:
- Core Python programming concepts
- Data manipulation and analysis
- Containerization of ML models
- GitHub Actions for automation
Whether you're new to MLOps or an experienced professional, this course equips you with the foundational Python skills to excel in machine learning operations roles.
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Apply Real-World Machine Learning with DevOps, DataOps & MLOps
- Master end-to-end MLOps solutions through hands-on AI pair programming
- Leverage cutting-edge tools like GitHub Copilot, Gradio & Hugging Face
- Build containerized ML apps deployable across cloud platforms
Course Journey:
- Explore MLOps landscape and pre-trained models to solve business problems
- Apply ML/AI in practice through optimization, simulation & heuristics
- Develop integrated DevOps, DataOps & MLOps pipelines on GitHub
- Package ML solutions in containers for seamless cloud deployment
- Transition to Rust for high-performance GPU-accelerated ML tasks
Ideal for data scientists, software engineers, analysts & professionals working with machine learning. Gain holistic MLOps skills through real-world projects.
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Master MLFlow and Hugging Face, two powerful open-source platforms for MLOps:
MLflow : Streamline machine learning lifecycle
- Manage projects and models
- Use powerful tracking system
- Interact with registered models
- End-to-end lifecycle examples
Hugging Face:
- Collaborate and deploy models
- Store datasets and models
- Create live interactive demos
- Leverage community repositories
Key Takeaways:
- Understand MLOps fundamentals
- Fine-tune and deploy containerized models
- Apply MLOps concepts to real-world use cases
Ideal for aspiring MLOps professionals or experienced practitioners looking to enhance their skills. Break into the field or level up your proficiency in machine learning operations.
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Master Cloud MLOps: AWS SageMaker & Azure ML
- Build end-to-end machine learning pipelines on leading cloud platforms
- Gain practical experience through hands-on exercises and projects
- Prepare for AWS & Azure ML certifications and job roles
Course Highlights:
- Explore data engineering & ML foundations on AWS
- Create data repos, ETL pipelines & serverless solutions
- Learn data science skills - cleaning, visualization, analysis
- Train, select & tune ML models on AWS SageMaker
- Operationalize models for production with MLOps best practices
- Deploy & maintain ML solutions using CPU/GPU instances
Ideal for data scientists, ML engineers, analysts & cloud professionals. Master comprehensive MLOps skills on AWS & Azure through real-world training.
Taught by
Noah Gift and Alfredo Deza
