FREE 2 HOUR WEBMINAR | ML OPS & GEN AI | CODING MACAW
WebinarOnline
Coding Macaw FREE Bootcamps
Start your journey into MLOps (Machine Learning Operations) with our FREE 1-Day Live Online Webinar designed for beginners, students, freshers, software developers, data scientists, machine learning enthusiasts, IT professionals, and career switchers.
This 2-hour webinar provides a beginner-friendly introduction to MLOps, Machine Learning Operations, Python, Machine Learning Model Deployment, ML Pipelines, Docker, Kubernetes, AWS, CI/CD, and Model Monitoring. Learn how machine learning models move from development to production and understand the key skills and technologies used by MLOps Engineers and Machine Learning Engineers.
If you're interested in MLOps, Machine Learning Engineering, AI Engineering, Machine Learning Model Deployment, AWS, Docker, Kubernetes, or DevOps, this webinar is a great place to start.
Topics Covered:
Introduction to MLOps & Machine Learning Operations
MLOps Lifecycle & Machine Learning Workflow
Python Fundamentals for MLOps
Machine Learning Model Deployment Basics
ML Pipelines & Automation
Introduction to Docker & Containerization
Kubernetes Fundamentals for MLOps
AWS & Cloud Computing for MLOps
CI/CD for Machine Learning
Model Monitoring & MLOps Career Roadmap
Who Should Attend?
Students, freshers, software developers, data scientists, machine learning beginners, AI enthusiasts, Python developers, IT professionals, DevOps beginners, and career switchers interested in MLOps, Machine Learning Engineering, AI Engineering, and Cloud Technologies.
No prior professional experience in MLOps is required.
Technologies Covered:
Python · Machine Learning · MLOps · AWS · Docker · Kubernetes · ML Pipelines · Model Deployment · CI/CD · Model Monitoring · Cloud Computing · DevOps
Webinar Details:
Date: AUGUST 21, 2026
Time: 10:30 AM – 12:30 PM EDT
Duration: 2 Hours
Session: Single Live Session
Mode: 100% Live Online
Limited seats available. Register now and discover how to start your journey toward a career in MLOps, Machine Learn