MLOps. Machine Learning deployment: AWS, GCP & Apple in<7hrs

🎓 Master MLOps & Deploy ML Models Across Environments - [Maxim Migutin]

🚀 Course Updates & Market Insights

• 08.2023: Enhanced with New Extra materials in Chapters #3 & #7, diving deeper into market stats, trends, and roles.
• 10.2023: Introducing the "MLOps Market Overview" chapter, offering insights into the current industry landscape.
• 11.2023: Added a new "Data Drifts" section (+1 hour of content) to help you identify and address data shifts. Integrated EvidentlyAI + MLFlow for robust experiment tracking.
• 12.2023: Course Structure Updates and new video: "What you will learn" for a clearer roadmap of your learning journey.
• 02.2024: New MLFLow Section, including the deployment of ML models to AWS, and much more! 🌟

🛠️ Hands-On Learning with Real-World Applications

Would you like to master best practices for Automation & ML model Deployment?

Are you eager to practice deploying robust, maintainable, and state-of-the-art ML models in various industries such as IT, Food, or Travel? Look no further!

🎓 About the Course & Instructor

For over 6 years, Maxim Migutin has been leveraging modern Cloud Computing and open source software to automate and deploy ML research projects across complex business challenges. This course will harness his expertise, focusing on AWS Sagemaker, Kubeflow, Azure Machine Learning, MLFlow, GCP Vertex AI, dvc, and more!

🤖 Mastering MLOps & Cloud Technologies

Dive into the world of Data Drifts to understand how to detect and mitigate their impact on your ML model's performance. Learn through various techniques, from basic visual analysis to mastering EvidentlyAI for advanced model monitoring.

🔍 Course Highlights & Skills You Will Gain

By the end of this course, you will be equipped with the skills to:

• Set up CI/CD pipelines to streamline your ML workflow.
• Package and containerize ML models using Docker.
• Conduct AutoML experiments locally or in the cloud.
• Train ML models specifically for Apple devices.
• Use mlflow to monitor, log, and manage ML experiments effectively.
• Operate Model Registry & Endpoints within GCP VertexAI.
• Employ EvidentlyAI to detect data drifts and conduct sophisticated ML model performance analysis.
• Integrate MLFlow with dagshub for cloud-based ML tracking.
• Deploy ML-powered Web Apps with UI to the AWS Cloud.

🌱 Continuous Improvement & Dynamic Content

This course is not set in stone! We value your feedback and continuously update the materials, adding new lectures and practice cases to keep you at the cutting edge of MLOps. Join a community of learners who are as passionate about advancing their careers in MLOps and Cloud technologies as you are!

🎯 Career Enhancement with Industry-Relevant Knowledge

Elevate your career by gaining a deep understanding of the latest MLOps practices, techniques, and tools. This course is designed to enhance your knowledge and acumen in the field, ensuring you stay ahead in an ever-evolving industry.

Ready to transform your expertise with actionable MLOps skills? Enroll now and take your career to new heights! 🚀🎉