Are you looking for the best MLOps courses on Coursera to take your machine learning projects to the next level?
This post will break down the top Coursera courses that cover essential MLOps concepts and practices to help you streamline your ML workflows.
Read on to discover which courses made our list of the best MLOps training available on Coursera and start upskilling today.
Machine Learning Engineering for Production (MLOps) Specialization
Provider: DeepLearning.AI
This hands-on program equips you with the skills to build, manage, and deploy AI applications effectively.
Start with “Introduction to Machine Learning in Production,” where you’ll learn to navigate the ML lifecycle, scope projects, and set model baselines.
You’ll tackle concept drift and design ML systems from the ground up, preparing you to handle real-world deployment challenges.
Move on to “Machine Learning Data Lifecycle in Production,” where building robust data pipelines is key.
You’ll master data cleaning, validation, and feature engineering with TensorFlow Extended.
This course ensures you understand how to maintain high data quality throughout your AI system’s lifecycle.
In “Machine Learning Modeling Pipelines in Production,” you’ll create models for various serving environments and manage resources for efficient inference.
Dive into model fairness and explainability, ensuring your AI solutions are responsible and understandable.
Finally, “Deploying Machine Learning Models in Production” teaches you to launch models for user interaction.
You’ll construct scalable infrastructures, automate workflows, and monitor systems to ensure reliability and compliance with standards like GDPR.
By completing this specialization, you’ll gain expertise in MLOps, TensorFlow, data transformation, and model monitoring.
MLOps | Machine Learning Operations Specialization
Provider: Duke University
This is a comprehensive program that equips you with the skills to blend machine learning with operational know-how.
Kick off with “Python Essentials for MLOps” to master Python, the programming language at the heart of many MLOps tasks.
You’ll tackle data with Pandas and NumPy, and automate operations with Python scripts.
Practical exercises will have you writing and debugging code like a pro.
In “DevOps, DataOps, MLOps,” you’ll explore how these methodologies apply to real-world AI challenges.
You’ll gain experience with web frameworks and command-line tools, and even delve into Rust for tasks requiring extra speed.
By the course’s end, you’ll confidently build and deploy ML models—a skill set that’s increasingly sought after.
For cloud enthusiasts, “MLOps Platforms: Amazon SageMaker and Azure ML” is a treasure trove.
It’s hands-on with AWS and Azure, guiding you through deploying machine learning solutions in production settings.
This course is also a stepping stone if you’re aiming for AWS or Azure machine learning certifications.
Finally, “MLOps Tools: MLflow and Hugging Face” introduces you to essential open-source platforms.
You’ll manage and deploy models, create APIs, and master cloud deployment.
This course is ideal if you’re eager to enhance your programming skills within the MLOps framework.
Throughout these courses, you’ll dive into Python programming, cloud computing, GitHub, Docker, and more.
You’ll learn about data management, continuous integration, and deploying machine learning models.
Real-world tools and projects ensure you’re job-ready.
So, if you’re looking to merge DevOps with machine learning, this specialization is tailored for you.
It’s practical, detailed, and taught by experts, setting you up for success in the evolving field of MLOps.
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
Provider: Google Cloud
Created by Google Cloud, this program equips you with the skills to excel in machine learning and prepare for the Google Cloud Machine Learning Engineer certification exam.
Dive into “Google Cloud Big Data and Machine Learning Fundamentals” to understand the journey from data to AI.
You’ll learn to craft data pipelines and create machine learning models using Vertex AI and AutoML, giving you a practical start.
In “How Google does Machine Learning,” you’ll discover Google’s approach to ML, including best practices and responsible AI.
This course emphasizes the importance of ethical AI solutions.
“Launching into Machine Learning” focuses on data quality and analysis, teaching you to build and train models with BigQuery ML.
You’ll also master model optimization, ensuring your AI performs well.
“TensorFlow on Google Cloud” is where you’ll get technical, creating models with TensorFlow and Keras and learning to scale them with Vertex AI.
This course is essential for applying your models effectively.
With “Feature Engineering,” you’ll enhance your models by selecting impactful data features using tools like BigQuery ML and TensorFlow.
You’ll also get familiar with preprocessing and exploring features for better predictions.
“Machine Learning in the Enterprise” applies your skills to real-world business scenarios, exploring data management and when to use different modeling approaches.
It’s about making AI work in a corporate setting.
“Production Machine Learning Systems” prepares you to deploy robust ML systems.
You’ll tackle model dependencies and distributed training, ensuring your systems are ready for any challenge.
“MLOps: Getting Started” introduces you to deploying, monitoring, and operating ML systems.
You’ll adopt continuous improvement practices to keep your AI at its best.
Finally, “ML Pipelines on Google Cloud” deepens your expertise in ML pipelines with TensorFlow Extended (TFX) and MLflow.
You’ll learn to automate and manage the ML lifecycle, making your workflow efficient and scalable.
This professional certificate is a comprehensive path to becoming a skilled Machine Learning Engineer.
You’ll gain hands-on experience with Google Cloud tools, TensorFlow, and Keras, and understand the full machine learning workflow from data management to model deployment.