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
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
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
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.
