MLOps & Model Deployment
Bridge the gap between data science and production engineering. Master experiment tracking, model versioning, CI/CD for ML, monitoring, and end-to-end ML pipelines.
What You'll Learn
Track experiments and compare models with MLflow
Version datasets and models with DVC
Build reproducible ML pipelines with Prefect or Airflow
Package and serve models as REST APIs with FastAPI and Docker
Build CI/CD pipelines that automatically test and deploy models
Monitor model performance and detect data drift in production
Curriculum Breakdown
Learning Format
Video Lessons
High-quality recorded lessons you can watch at your own pace.
Hands-on Projects
Real-world projects that reinforce every concept you learn.
Certificate
Earn a verifiable certificate upon successful completion.
Certification Details
MLOps & Model Deployment Certificate
Issued by Tech101
Prove your ability to take ML models from notebook to production reliably.
Certificate Requirements
- Shareable on LinkedIn
- PDF download
- Unique verification ID
Completion Certificate
Awarded upon finishing all course content and submitting projects. Shows dedication and completion.
Graded Certificate
Earned by passing the final assessment with 70%+ score. Demonstrates verified skill proficiency.
Your Instructor
James Kim
Python Developer & Data Engineer
James specialises in Python and data engineering pipelines, with experience at Netflix and Amazon Web Services.
Requirements & Prerequisites
Technical Requirements
- Machine Learning Fundamentals and at least one deep learning course
- Basic Docker knowledge
- Comfort with the command line
Who This Course Is For
- Data scientists who want their models to actually reach production
- ML engineers and DevOps engineers working on AI products
- Data Science & AI track students finishing the program
Student Reviews
Frequently Asked Questions
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