Machine Learning Operations
Master end-to-end MLOps with Python, cloud platforms, and modern tools for building and deploying production-ready ML systems.
Master end-to-end MLOps with Python, cloud platforms, and modern tools for building and deploying production-ready ML systems.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning Operations program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Master Python fundamentals specifically targeted toward MLOps implementation requirements
Build end-to-end machine learning solutions using cloud platforms like AWS SageMaker and Azure ML
Develop and deploy production-ready pipelines for model training, evaluation, and deployment
Implement infrastructure for Large Language Models (LLMs) fine-tuning and deployment
Create containerized models using the ONNX format with Hugging Face for efficient deployment
Design complete MLOps pipelines with MLflow, including project management, model tracking, and feature monitoring
Skills you'll gain
This course includes:
18 Hours PreRecorded video
Hands-on Labs, Technical Projects, Platform-specific Implementations
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 courses in this program
Analysis for Business Systems
Course 1 · 9 Hours to complete · 5 modules
Engineering Maintainable Android Apps
Course 2 · 8 Hours to complete · 5 modules
Go for Cloud and Networks
Course 3 · 6 Hours to complete · 5 modules
ExpressJS Essentials: Building Modern Web APIs
Course 4 · 10 Hours to complete · 6 modules
Fee Structure
Payment options
Financial Aid
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