Learn MLOps with these 10 Comprehensive Courses

Machine Learning Operations (MLOps) is an emerging field focused on streamlining the process of taking machine learning models to production reliably and efficiently. As companies increasingly invest in ML, demand for skilled MLOps engineers is booming.

Getting proper MLOps training can set you up for an exciting and lucrative career at the cutting edge of machine learning deployment. This guide explores 10 of the best MLOps courses to take your skills to the next level.

Why Learn MLOps?

While interest in machine learning (ML) continues to skyrocket, taking models from prototype to production brings daunting challenges like:

  • No established software engineering practices for ML
  • Models deteriorating in unpredictable ways
  • Difficulty integrating models with business goals
  • Lack of monitoring for model performance

MLOps introduces DevOps-style best practices to address the end-to-end ML lifecycle – think CI/CD for machine learning.

This emerging capability promises great things:

  • Accelerate time-to-value by rapidly deploying ML innovations
  • Minimize model drift through rigorous monitoring procedures
  • Improve model governance with greater visibility and control
  • Enable reuse of work products to drive efficency

As more companies work to productize ML, MLOps skills are in hot demand. LinkedIn lists it as an emerging job with over 25% annual growth.

Top technology firms like Google, Microsoft, Amazon and IBM are investing heavily in internal MLOps capabilities. They also offer various MLOps products and services to customers struggling with ML deployments.

Whether you want to pursue an MLOps career or effectively integrate ML in your company, getting the right training is key.

Comparing 10 Top MLOps Courses

Here‘s an overview of 10 recommended MLOps courses for every skill level and need:

Course Duration Format Use Case Level
MLOps Fundamentals: Udemy 2 hours Self-paced videos ML engineers new to MLOps Beginner
MLOps Specialization: Coursera 3 months Project-based, cohort model Transition into MLOps Engineer role Intermediate
MLOps on AWS: AWS Training 2 days Live instructor-led Apply MLOps in AWS environment Intermediate
MLOps Engineering: edX 12 weeks Self-paced videos Productionize models on Azure Advanced
View all courses compared
Course Duration Format Use Case Level
MLOps Fundamentals: Udemy 2 hours Self-paced videos ML engineers new to MLOps Beginner
Model Deployment: Udemy 5.5 hours Self-paced videos Take models into production Beginner
MLOps Fundamentals: Coursera 4 weeks Self-paced videos Work in GCP ML infrastructure Beginner
MLOps Specialization: Coursera 3 months Project-based, cohort model Transition into MLOps Engineer role Intermediate
MLOps Certification: CloudxLab 40 hours Virtual live instructor-led End-to-end ML pipeline development Intermediate
Certified MLOps: 360DigiTMG not specified Coding exercises & projects ML model deployment at scale Intermediate
MLOps on AWS: AWS Training 2 days Live instructor-led Apply MLOps in AWS environment Intermediate
Professional Certificate – MLOps: edX 12 weeks Self-paced videos Productionize models on Azure Advanced
MLOps Course: igmGuru 40 hours Self-paced videos AI engineers & leaders Advanced
MLOps Fundamentals: PluralSight 5 hours 40 mins Self-paced videos Onboard to MLOps concepts Beginner

Now let‘s explore what each of these courses offers:

MLOps Fundamentals: Udemy

MLOps Course: Udemy

Best for

ML engineers and data scientists looking for a quick introduction to MLOps concepts.

Course Details

  • 2 hour video course
  • Certificate of completion
  • Access for lifetime
  • $12.99

This Udemy course packs MLOps fundamentals like continuous delivery, deployment and integration pipelines into a tight package.

You‘ll come away with a high-level overview of:

  • MLOps principles
  • Maturity models
  • Platform comparisons
  • Toolchain

While not hands-on, it serves as good prep material if you‘re new to MLOps.

Model Deployment: Udemy

Machine Learning Model Deployment: Udemy

Best for

Early career data scientists.

Course Details

  • 5.5 hours on-demand video
  • 2 downloadable resources
  • Certificate of completion
  • Access for lifetime
  • $13.99 during sale (Normally $109.99)

This deployment-focused course helps you take ML projects from proof-of-concept to production. Instructor Julio Cesar offers lots of practical guidance including:

  • Converting notebooks to production code
  • Building ML packages
  • Containerization with Docker
  • Publishing APIs
  • Infrastructure management

While not a dedicated MLOps course, it complements such content nicely.

MLOps Fundamentals: Coursera

MLOps Fundamentals: Coursera

Best for

ML practitioners working with Google Cloud infrastructure.

Course Details

  • Taught via video lectures (+ readings & quizzes)
  • Takes ~4 weeks at 4 hours/week
  • Issued by Google Cloud
  • Free to audit, $49 for graded assignments

This Coursera entry covers using MLOps tools on Google Cloud Platform (GCP) specifically. The instructor works on the technical solutions side at Google.

Skills covered include:

  • GCP setup/configuration
  • ML pipelines
  • Model deployment
  • Monitoring, observability
  • CI/CD principles

It‘s a quick introduction oriented around working in a GCP environment.

MLOps Specialization: Coursera

Machine Learning Engineering for Production (MLOps) Coursera Specialization

Best for

Mid-career professionals looking to transition into MLOps roles.

Course Details

  • Project-driven specialization
  • Taught via video lectures (+ hands-on labs & projects)
  • Takes ~3 months at 8-10 hours/week
  • Issued by deeplearning.ai
  • Free to audit, $79/month for graded track

This Coursera specialization helps you master MLOps capabilities through theoretical and practical knowledge.

It‘s taught by highly-rated deeplearning.ai, founded by former Stanford CS faculty Andrew Ng (a machine learning pioneer).

Skills covered include:

  • ML experimentation
  • Model deployment
  • Data management
  • Workflow orchestration
  • Monitoring

The specialization culminates in you building an end-to-end MLOps solution. This can directly showcase your hands-on skills to employers.

MLOps on AWS: AWS Training

MLOps Engineering on AWS

Best for

Engineers and cloud architects working with AWS services.

Course Details

  • 2 day instructor-led training
  • Delivered through classroom or virtual classroom
  • Hands-on labs & exercises
  • Offered globally

For those leveraging Amazon Web Services, this course helps connect ML concepts with practical AWS implementations.

You‘ll learn how to:

  • Architect for MLOps on AWS
  • Enable rapid experimentation
  • Operationalize models
  • Monitor systems
  • Optimize costs
  • Scale deployments

It sets you up to drive MLOps outcomes through AWS offerings like SageMaker, EKS, and CloudWatch.

MLOps Engineering Professional Certificate: edX

Professional Certificate – MLOps Engineering

Best for

Software engineers and architects looking to productionize ML on Azure.

Course Details

  • Self-paced learning
  • Estimated effort: 12 weeks, 8-10 hours/week
  • Free auditor track
  • Verified certificate: $996

This certificate from Microsoft enables you to operationalize machine learning models with GitHub and Azure tools.

Across 9 courses, you will:

  • Deploy models with Azure Web Services
  • Enable CI/CD with GitHub Actions
  • Monitor systems with Azure Monitor
  • Manage metadata/lineage with MLflow
  • Architect model APIs
  • Secure access and data

It provides advanced skills for your ML TooLkit while keeping costs low via Azure credits.

Key Benefits of MLOps Training

Beyond foundational MLOps knowledge, these courses provide collateral benefits:

  • Cutting-edge techniques: Exposure to latest tools, architectures and best practices
  • Cloud platform skills: Operational know-how for AWS, GCP and Azure
  • Hands-on experience: Real projects to apply skills and build portfolio
  • Expert guidance: Learn from ML practitioners and researchers
  • Career advancement: Transition to high-growth MLOps roles
  • Efficient learning: Structure to ensure skill proficiency

MLOps continues gaining momentum across industries. These courses equip you with versatile capabilities to improve ML reliability, security and performance in enterprise settings.

Bringing MLOps Training to Your Organization

While individual learning is accessible and affordable, companies can realize even greater value from MLOps training.

Options like onsite workshops, cohort-based courses and train-the-trainer programs provide:

  • Customization: Tailor content to your tech stack
  • Scale: Train large teams efficiently
  • Context: Incorporate organization-specific use cases
  • Cost savings: Group discounts result in lower price per learner

Disclosure: I offer such corporate training programs on MLOps. Get in touch to discuss your team‘s needs!

Continue Your MLOps Education

With the right approach, MLOps offers immense opportunities:

  • Extract more value from your data science investments
  • Accelerate innovation by rapidly translating ML research into customer value
  • Minimize risks around accountability, reliability and accuracy of models
  • Drive efficiencies through leaner, more modular architectures

I hope this guide sparks your interest in advancing your MLOps skills! Reach out if you have any other questions.