MLOps Academy

Name: MLOps Academy
Availability: InStock

PYTHONAiData_scienceCloud

Take ML models to production: experiment tracking, serving, CI/CD, monitoring, and drift detection with MLflow, Docker, and Kubernetes.

🤖 AI-Powered📚 30 courses👥 100,000+ learners⭐ 4.9 rating

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Course Overview

MLOps: Deploy & Monitor ML Models

Take ML models to production: experiment tracking, serving, CI/CD, monitoring, and drift detection with MLflow, Docker, and Kubernetes. This track covers 30 progressive mini-courses from absolute beginner (A1) through advanced (B2), with short focused lessons and quick quizzes to lock in each concept.

What You Will Learn

You start with the fundamentals and build up through intermediate and advanced topics, each course building on the last. Every lesson is practical and bite-sized, with a 24/7 AI tutor available when you need help.

How It Works

Each course is broken into four focused, bite-sized lessons. Complete a few lessons a day and you will master the full track in weeks, not months.

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Tools

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Create LessonPRO

AI-powered lesson builder

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Start Certification ExamPRO

Earn your certificate

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Start Challenge

Test your skills

How You'll Learn

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Interactive Lessons

Hands-on coding exercises with real-time feedback

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AI Tutor

Get instant help from our AI when you're stuck

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Built-in Editor

Write and run code directly in your browser

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Certificate

Earn a certificate when you complete the course

Curriculum

30 Courses

Every course in the MLOps Academy learning path.

What MLOps Actually Is

A14 lessons

Explain MLOps in plain terms and map the full ML lifecycle from data to production.

Why Models Break in Production

A14 lessons

Name the real reasons a great offline model fails once real users hit it.

Track Every Experiment with MLflow

A14 lessonsPRO

Log parameters, metrics, and runs so no experiment is ever lost again.

Reproducible ML Environments

A14 lessonsPRO

Pin every dependency so your training run produces the same result anywhere.

Version Your Data with DVC

A14 lessonsPRO

Track large datasets in Git without bloating the repo.

The Model Registry Explained

A24 lessonsPRO

Store, version, and stage trained models as first-class artifacts.

Package a Model for Deployment

A24 lessonsPRO

Turn a trained model into a portable, loadable artifact.

Serve a Model with FastAPI

A24 lessonsPRO

Wrap any model in a real HTTP prediction API.

Dockerize Your ML Service

A24 lessonsPRO

Build a reproducible container that runs your model anywhere.

Your First End-to-End ML Flow

A24 lessonsPRO

Connect train, register, serve, and predict into one working pipeline.

CI/CD for Machine Learning

B14 lessonsPRO

Automate testing and deployment of models with GitHub Actions.

Automated Training Pipelines

B14 lessonsPRO

Turn ad-hoc scripts into a reproducible, staged training DAG.

Build a Feature Store

B14 lessonsPRO

Serve consistent features to training and inference with Feast.

Validate and Test ML Models

B14 lessonsPRO

Catch bad models and bad data before they reach users.

Batch vs Online Inference

B14 lessonsPRO

Choose and build the right prediction pattern for your use case.

Serve Models with BentoML

B14 lessonsPRO

Package and serve models with batching and adaptive scaling built in.

Monitoring Basics: Metrics & Logs

B14 lessonsPRO

Instrument a model service so you can see what it is doing live.

Detect Data and Concept Drift

B14 lessonsPRO

Measure when inputs or relationships shift away from training.

A/B Test Models in Production

B14 lessonsPRO

Roll out a new model to a slice of traffic and measure the lift.

Deploy Your Model to the Cloud

B14 lessonsPRO

Take a containerized model from laptop to a managed cloud runtime.

Kubernetes for ML Workloads

B24 lessonsPRO

Run training and serving on Kubernetes with the right primitives.

Scalable Serving with KServe

B24 lessonsPRO

Deploy models as autoscaling inference services on Kubernetes.

GPU Serving and Dynamic Batching

B24 lessonsPRO

Squeeze maximum throughput from GPUs with Triton Inference Server.

Canary and Shadow Deployments

B24 lessonsPRO

Release new models safely by testing them on real traffic first.

Automate Model Retraining

B24 lessonsPRO

Close the loop so models refresh themselves on schedule or on drift.

Drift Detection in Depth

B24 lessonsPRO

Build robust drift monitoring that survives noisy, delayed-label reality.

ML Observability at Scale

B24 lessonsPRO

See model behavior end to end across data, predictions, and outcomes.

Lineage and Model Governance

B24 lessonsPRO

Prove how any model was built and keep it audit-ready.

Optimize ML Serving Costs

B24 lessonsPRO

Cut inference spend without sacrificing latency or accuracy.

LLMOps for LLM Applications

B24 lessonsPRO

Operate, evaluate, and monitor LLM-powered apps in production.

FAQ

Frequently Asked Questions

Is the MLOps Academy course free?

Yes. You can start the MLOps Academy course for free and complete its interactive lessons at no cost. An optional PRO subscription unlocks advanced AI tools and a shareable certificate.

Do I need prior experience to learn PYTHON?

No. The course begins with the fundamentals and gradually moves to more advanced topics, so you can start even with no prior PYTHON experience.

How will I learn PYTHON on CoddyKit?

You learn by doing. Short interactive lessons pair a clear explanation with a hands-on coding exercise that runs in real time, and a 24/7 AI tutor gives personalized help whenever you get stuck.

Do I get a certificate for completing MLOps Academy?

Yes. PRO learners can take an exam and earn a shareable certificate of completion with a verifiable code for the MLOps Academy course.

Can I learn PYTHON on my phone?

Yes. CoddyKit is available on the web and as native iOS and Android apps, so you can learn PYTHON on any device and your progress syncs across them.

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