MLOps Online Courses & Certifications

MLOps or Machine Learning Operations is a set of practices that automates and simplifies the machine learning workflow, from building the model to deploying it in the real world. It essentially bridges the gap between creating a model and using it to solve real problems.

In simple terms, MLOps combines Machine Learning, DevOps, and Data Engineering to deploy, manage, and monitor machine learning models in production environments. It aims to streamline the end-to-end machine learning lifecycle, ensuring that ML models can be deployed, maintained, and monitored effectively and efficiently.

Key Components and Practices Involved in MLOps:

Collaboration:

Encourages collaboration between data scientists, ML engineers, and operations teams to streamline the model development and deployment process.

Continuous Integration/Continuous Deployment (CI/CD):

Applies CI/CD princi

Key Components and Practices Involved in MLOps:

Collaboration:

Encourages collaboration between data scientists, ML engineers, and operations teams to streamline the model development and deployment process.

Continuous Integration/Continuous Deployment (CI/CD):

Applies CI/CD principles to ML models, ensuring automated testing, validation, and deployment of models in production.
Facilitates rapid iteration and deployment of new models or updates to existing models.

Version Control:

Manages versions of data, models, and code to ensure reproducibility and traceability.
Enables tracking of changes and rollback to previous versions if needed.

Automated Monitoring:

Implements monitoring systems to track model performance and detect issues like data drift, model degradation, or anomalies.
Ensures that models continue to perform well in production environments.

Model Training and Re-training:

Automates the process of training and re-training models using updated data.
Ensures models remain accurate and relevant over time.

Scalability:

Designs infrastructure that can scale to handle large volumes of data and complex ML models.
Uses cloud platforms and containerization to achieve scalability and flexibility.

Security and Compliance:

Ensures that data and models comply with regulatory requirements and are secure from threats.
Implements access controls and audit trails.

Why Learn MLOps in 2024?

Increased Demand for Reliable AI: Machine learning (ML) is being used in more and more applications, but simply building a model isn't enough. Businesses need to deploy these models effectively and ensure they continue to perform well over time. MLOps provides the tools and practices to achieve this.
Bridge the Gap Between Data Science and Engineering: MLOps fosters collaboration between data scientists and engineers. Data scientists can focus on model development while engineers ensure those models are implemented and monitored correctly. This combined expertise is crucial for successful AI projects.
Streamlined ML Workflow: MLOps automates many of the tedious tasks involved in the ML lifecycle, freeing up valuable time for data scientists and engineers. This allows them to focus on higher-level activities like model development and innovation.
Improved Model Performance: MLOps practices like monitoring and data governance help to identify and address issues that can affect a model's performance over time, such as data drift or bias. This helps to ensure that models deliver consistent and accurate results.
Future-Proofing Your Career: As the field of AI continues to grow, MLOps skills will be increasingly in demand. By learning MLOps, you can position yourself for exciting career opportunities in this rapidly evolving field.

MLOps Course Curriculum

Week	Module	Topics Covered
1	Introduction to MLOps	Overview of MLOps Importance of MLOps in ML lifecycle Key MLOps concepts and principles
2	Version Control for ML	Versioning data, models, and code Tools like Git and DVC Ensuring reproducibility and traceability
3	Data Management	Data collection, cleaning, and labeling Data validation techniques Strategies for data versioning
4	Model Development	Best practices in model development Feature engineering Model selection techniques
5	Continuous Integration (CI)	Implementing CI for ML Automated testing and validation of models Tools and frameworks for CI
6	Continuous Deployment (CD)	Continuous deployment strategies Automated deployment pipelines Rollback and monitoring in CD
7	Model Deployment	Deployment of ML models in production Containerization with Docker Orchestration with Kubernetes
8	Monitoring and Maintenance	Monitoring model performance Detecting data drift and model degradation Maintenance strategies and automated retraining
9	Security and Compliance	Ensuring data security in ML workflows Compliance with regulatory requirements Implementing access controls and audit trails
10	Scalability in MLOps	Scaling ML models and workflows Cloud platforms for MLOps (e.g., AWS, Azure, GCP) Handling large-scale data and model training
11	MLOps Tools and Platforms	Overview of popular MLOps tools Practical use of platforms like Databricks, MLflow, Kubeflow Integration of various tools into the MLOps pipeline
12	Case Studies and Applications	Real-world MLOps case studies Applications in different industries (e.g., healthcare, finance, retail) Lessons learned from successful MLOps implementations

Career Opportunities with an MLOps Certification

Job Role	Description	Key Responsibilities	Skills Required
MLOps Engineer	Integrates ML models into production environments, ensuring scalability and reliability.	Implement CI/CD for ML models Monitor and maintain ML pipelines Automate model deployments	Proficiency in Python/R Experience with CI/CD tools Knowledge of cloud platforms (AWS, Azure, GCP) Strong understanding of ML lifecycle
Machine Learning Engineer	Designs, develops, and deploys ML models. Works closely with data scientists and MLOps engineers.	Develop ML models Optimize and scale models for production Collaborate on deployment strategies	Proficiency in Python/R Strong ML and data science knowledge Experience with TensorFlow/PyTorch Understanding of MLOps practices
Data Engineer	Manages and organizes data pipelines, ensuring data is clean, accessible, and usable for ML models.	Design and build data pipelines Ensure data quality and integrity Collaborate with data scientists	Proficiency in SQL and NoSQL databases Experience with ETL tools Knowledge of big data technologies (Hadoop, Spark) Programming skills (Python, Java, Scala)
DevOps Engineer	Implements CI/CD pipelines for software and ML models, automating and monitoring the deployment process.	Develop CI/CD pipelines Automate infrastructure provisioning Monitor and troubleshoot deployments	Proficiency in scripting languages (Python, Bash) Experience with CI/CD tools (Jenkins, GitLab CI) Knowledge of cloud platforms (AWS, Azure, GCP) Containerization (Docker, Kubernetes)
AI/ML Architect	Designs and oversees the architecture for AI and ML systems, ensuring they meet business and technical requirements.	Define ML architecture and frameworks Ensure scalability and performance Guide development teams	Strong ML and AI knowledge Experience in system design and architecture Proficiency in cloud platforms Excellent problem-solving skills
Cloud Engineer	Manages cloud infrastructure for deploying ML models, focusing on scalability, security, and cost efficiency.	Design and manage cloud infrastructure Implement security best practices Optimize cloud resources	Proficiency in cloud platforms (AWS, Azure, GCP) Experience with cloud automation tools (Terraform, Ansible) Knowledge of networking and security
Data Scientist	Analyzes data and develops ML models. Works with MLOps engineers to deploy models in production.	Conduct data analysis Develop and validate ML models Collaborate on model deployment	Strong statistical and ML skills Proficiency in Python/R Experience with data visualization tools (Tableau, Power BI) Knowledge of MLOps practices
Automation Engineer	Develops automation scripts and tools to streamline ML workflows, from data processing to model deployment.	Create automation scripts Develop tools to enhance workflows Monitor automated processes	Proficiency in scripting languages (Python, Bash) Experience with automation tools Knowledge of CI/CD and ML workflows
ML Research Scientist	Conducts research to advance the field of machine learning, focusing on developing new models and algorithms.	Conduct research on ML algorithms Publish research papers Collaborate with academic and industry partners	Strong background in ML and statistics Experience with research methodologies Proficiency in programming languages (Python, C++) Analytical and problem-solving skills
Technical Product Manager	Manages the lifecycle of ML products, from conception to deployment, ensuring they meet customer needs and technical requirements.	Define product requirements Oversee development and deployment Ensure alignment with business goals	Strong understanding of ML and AI Excellent project management skills Proficiency in communication and stakeholder management Experience with product lifecycle management

Top 10 MLOps Online Courses

Course Name	Platform	Duration	Description
MLOps with TensorFlow on GCP	Coursera	5 weeks	This course covers how to build, train, and deploy machine learning models on Google Cloud Platform. It focuses on MLOps best practices using TensorFlow.
Machine Learning Engineering for Production (MLOps)	Coursera	4 months	Offered by Google Cloud, this specialization covers a wide range of MLOps skills including data pipeline construction, model monitoring, and versioning.
MLOps: CI/CD/CT Pipelines of ML with Azure Demo	Udemy	4.5 hours	This course provides a practical guide to building CI/CD/CT pipelines for machine learning models using Azure.
Azure MLOps Workshop	Pluralsight	4 hours	This workshop dives into the integration of machine learning and DevOps using Azure, covering automation, deployment, and monitoring of ML models.
Become a Machine Learning Engineer for Microsoft Azure Nanodegree	Udacity	3 months	This nanodegree program equips students with the skills to build and deploy ML solutions on Azure, utilizing open-source tools and frameworks.
Operationalizing Machine Learning Models	edX	4 weeks	This course, offered by Microsoft, focuses on deploying, monitoring, and automating ML models.
MLOps: From Model Deployment to Model Management	DataCamp	2 hours	This course covers the lifecycle of machine learning models, from deployment to management, using practical exercises and examples.
MLOps Fundamentals	AWS Training	Self-paced	This course provides an introduction to MLOps practices on AWS, covering automation, deployment, and monitoring of ML models.
Demystifying Machine Learning Operations (MLOps)	Pluralsight	2 hours	This intermediate-level course teaches the main concerns and issues in developing ML models post-deployment.
MLOps Essentials: Model Deployment and Monitoring	LinkedIn Learning	3 hours	This course covers the essentials of MLOps, focusing on model deployment and monitoring using various tools and techniques.

Top 10 MLOps Degree Programs

University/Institution	Degree Program	Mode
Carnegie Mellon University	Master of Science in MLOps	On-Campus/Online
University of California, Berkeley	Master of Data Science with MLOps	Online
University of Toronto	MSc in Applied Computing with MLOps	On-Campus
Georgia Institute of Technology	MS in Computational Science and Engineering with MLOps	Online
University of Illinois Urbana-Champaign	MCS in Data Science with MLOps	Coursera
Stanford University	Graduate Certificate in MLOps	On-Campus/Online
University of Washington	MS in Data Science with MLOps	On-Campus/Online
Johns Hopkins University	MS in Artificial Intelligence with MLOps	Online
Imperial College London	MSc in Artificial Intelligence and Machine Learning Operations	Online
National University of Singapore	MSc in Computer Science with MLOps	On-Campus/Online

Best Free MLOps Courses

Course Name	Platform	Description
Google Cloud’s MLOps Course	Google Cloud	Learn the basics of MLOps on Google Cloud, covering model building, deployment, and management using GCP tools.
MLOps Zoomcamp	DataTalks.Club	A comprehensive course teaching practical aspects of MLOps, including experiment tracking, model deployment, and monitoring.
MLOps Specialization	Coursera (DeepLearning.AI)	Focuses on deploying and scaling ML models in production environments, including CI/CD pipelines and model monitoring.
ML Engineering for Production (MLOps)	Coursera (Google Cloud)	Covers how to manage ML projects, build production ML systems, and operationalize ML models using Google Cloud.
Practical MLOps	Coursera (Andrew Ng)	Provides insights into building production-ready ML systems, focusing on MLOps practices such as CI/CD, model deployment, and monitoring.
Azure MLOps with Python	Microsoft Learn	Learn how to operationalize ML using Azure Machine Learning, including model training, deployment, and monitoring.
Introduction to MLOps	Udacity	Offers a foundational understanding of MLOps, including versioning, model management, and deployment strategies.
Machine Learning Engineering for Production (MLOps)	Coursera (deeplearning.ai)	Explores best practices in MLOps, including model development, deployment pipelines, and continuous delivery.
AWS Machine Learning Operations	AWS Training	Focuses on using AWS services for MLOps, covering model training, deployment, and monitoring with SageMaker.
MLOps with TensorFlow	Coursera (deeplearning.ai)	Teaches how to implement MLOps pipelines with TensorFlow, covering model management, deployment, and monitoring strategies.