MLOps

Top MLOps Courses Every Data Scientist Must Take for Career Growth

MLOps combines machine learning principles with DevOps practices, simplifying the development, deployment, and management of prototypes in manufacturing environments. By adopting MLOps, data scientists can ensure that their models are reliable, maintainable, and able to meet real-world challenges.

As the field of artificial intelligence (AI) and machine learning (ML) has grown, the demand for scalable, reliable, and efficient machine learning operations (MLOps) has increased dramatically MLOps combines machine learning with DevOps practices and focuses on automating and streamlining the lifecycle of ML from development to deployment.

Mastering MLOPS is important for data scientists looking to advance their careers and work on real-world ML solutions. Here’s a roundup of must-have MLOps courses for every data scientist.

Coursera: MLOps Specialization by DeepLearning.AI

This comprehensive specialization allows you to dive deep into the basics of MLOps, and provides hands-on experience in the lifecycle of machine learning models, from design to implementation.

What you will learn:
  • Model training, tuning, and scaling
  • Automating ML Pipeline
  • Managing the ML infrastructure
  • Maintaining ML models and improving productivity

Why take this course: Designed by industry experts like Andrew Ng, this specialization covers basic and advanced MLOPS-first. Data scientists are using real-world ML.

Udemy: Machine Learning Engineering for Production (MLOps)

Overview: This Udemy course focuses on applying DevOps practices to machine learning projects, from repeatable workflows to prototyping in production.

What you will learn:
  • Model versioning and analysis
  • CI/CD for machine learning
  • Infrastructure (IaC) as a rule for ML
  • For better reliability and efficiency in manufacturing

Why take this course: This course is perfect for data scientists transitioning into machine learning and engineering roles, where building and maintaining production-ready ML models is critical.

DataCamp: MLOps for Data Scientists

Overview: Aimed specifically at data scientists, this DataCamp course teaches how to apply MLOps best practices to real-world projects, including collaboration between data science and engineering teams.

What you will learn:
  • Automated pipelines for model deployment
  • Model monitoring and governance
  • Handling model drift and performance degradation
  • version control and testing

Why take this course: DataCamp’s hands-on approach helps data scientists gain practical experience using MLOps tools like Docker, Kubernetes, and MLflow to ensure their instances are production-ready.

Pluralsight: MLOps – Deploying AI & ML Models in Production

This course provides a comprehensive journey through the use of machine learning models in business, with an emphasis on scalability, monitoring, and performance optimization.

What you will learn:
  • Establishment of scalable ML models
  • Model deployment using Docker and Kubernetes
  • CI/CD pipeline used for ML
  • Monitoring and retraining the model’s performance

Why take this course: Pluralsight’s course is ideal for those looking to sharpen their skills in effectively using AI and ML models in manufacturing environments.

Google Cloud: MLOps: Continuous delivery and automation pipeline in ML

Focusing on Google Cloud’s AI and ML applications, this course teaches you how to build robust, automated ML pipelines using tools like Kubeflow, TensorFlow Extended (TFX), and BigQuery.

What you will learn:
  • Building an end-to-end ML pipeline on Google Cloud
  • Designing business processes using TFX
  • Deal with models and use them at scale
  • Continuous monitoring and development of ML models

Why take this course: Google Cloud’s MLOps course is more suitable for data scientists working in cloud-based environments.

AWS Certified Machine Learning – Specialty

Although this certification is mostly focused on machine learning, it does offer a comprehensive section on MLOps, especially when using AWS tools and services

What you will learn:
  • End-to-end ML workflows on AWS
  • Model deployment using SageMaker
  • ML models and automatic scaling
  • Ensuring adequate security and compliance

Why take this course: For data scientists working in the AWS environment, this certification provides the skills needed to manage ML instances from development through cloud deployment.

Microsoft Azure: MLOps with Azure Machine Learning

This course covers the development of the MLOps product for the Azure ecosystem, which focuses on using Azure Machine Learning for end-to-end ML deployments.

What you will learn:
  • Create, train, and deploy models on Azure
  • Implementing a CI/CD pipeline for Azure ML
  • Automating ML business processes with Azure DevOps
  • Maintenance and maintenance of models through to production

Why take this course: This course is essential for data scientists working in a Microsoft Azure environment to learn how to manage ML models and pipelines on Azure’s cloud infrastructure.

Conclusion:

A background in MLOps is not an option for data scientists who want to work on large, real-world machine learning projects. The courses listed above offer a blend of theoretical and practical skills that will enhance your understanding of MLOps and give you the hands-on skills necessary to deal with machine learning models that use and optimize scale.