Picture this: You’re finally diving into the world of Machine Learning and Data Science, excited to make sense of all the data trends and patterns to improve business strategies and decision-making. You quickly realize that ML models are just the first step, and MLOps is the golden ticket to streamlining processes and building a sustainable pipeline. But where to start? With so many resources available, it can be overwhelming to find the perfect online course to walk you through the essentials of MLOps. Look no further! This blog post is here to ease your worries and provide you with the top MLOps online courses to help you master your ML deployment game.
In this well-curated list, we’ve gathered the best online courses to guide you step by step into the world of MLOps. Ranging from beginner-friendly courses that offer foundational knowledge to more advanced options that will take your skills to the next level, we’ve got you covered! Beyond just course recommendations, we’ll give you a sneak peek into what you can expect from each offering, so you can make an informed decision and kick-start your MLOps journey with confidence. Ready to transform your ML models into smooth-running pipelines? Keep on reading!
Mlops Courses – Table of Contents
- Complete MLOps Bootcamp | From Zero to Hero in Python 2022
- Azure Machine Learning & MLOps : Beginner to Advance
- MLOps: ML Model Deployment + AWS Sagemaker, GCP, Apple Cases
- Deployment of Machine Learning Models
- Machine Learning Deep Learning model deployment
- MLOps Fundamentals: CI/CD/CT Pipelines of ML with Azure Demo
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Complete MLOps Bootcamp | From Zero to Hero in Python 2022
Platform:
Udemy
Rating:
4.3 out of 5
Are you on the hunt for a comprehensive and hands-on guide to MLOps (Machine Learning Operations)? Well, you’re in luck! This course is here to teach you everything related to MLOps, covering model development to cloud deployment, and even web applications development. With MLOps estimated to be valued at $126 billion by 2025, learning about this subject matter can greatly enhance your professional opportunities.
This course features visual training, downloadable study guides, hands-on exercises, and real-world labs, ensuring that you’ll be able to implement an end-to-end MLOps project. Topics covered include MLOps fundamentals, model versioning with MLFlow, Auto-ML and low-code MLOps, containerized ML workflows with Docker, deploying ML in production through APIs, and MLOps in Azure Cloud. By the end of the course, you’ll not only gain valuable knowledge and confidence, but you’ll also have an entire MLOps project developed from the ground up. Start enhancing your MLOps skills today and unlock a world of potential job opportunities!
Skills you’ll learn in this course:
- Master MLOps fundamentals and address traditional ML model management challenges.
- Utilize MLOps tools to implement end-to-end projects.
- Implement model versioning and registration using MLFlow.
- Automate ML model development with Auto-ML and Low-code libraries like Pycaret.
- Achieve model interpretability, explainability, auditability, and data drift management.
- Containerize ML workflows with Docker for efficient distribution.
- Deploy ML models in production using APIs, FastAPI, Flask, and Azure Cloud.
- Develop and deploy ML web applications with Gradio, Flask, HTML, Docker, and Azure.
Azure Machine Learning & MLOps : Beginner to Advance
Platform:
Udemy
Rating:
4.5 out of 5
Are you on the hunt for an engaging, comprehensive, and fun course to master Azure Machine Learning and MLOps? Look no further! This unique course, instructed by a dynamic duo, will guide you through both essential and advanced industry-required topics on Azure Machine Learning. Plus, it provides a hands-on end-to-end implementation of Machine Learning Operations (MLOps), which is a fast-growing area with high demand in the industry.
The Azure Machine Learning cloud service accelerates and manages machine learning project lifecycles, making it the perfect tool for data scientists and engineers to incorporate into their workflows. In this course, you’ll find thorough coverage of best practices and key features, preparing you for an exciting career in Machine Learning and MLOps. The course is kept up to date with recent additions, and it also includes some extra materials related to Azure Machine Learning and Databricks (Apache Spark). Join this fantastic learning experience and take the next step in your career!
Skills you’ll learn in this course:
- Master Azure Machine Learning concepts and techniques
- Gain hands-on experience with end-to-end MLOps implementation
- Understand and apply Machine Learning Operations (MLOps) principles and guidelines
- Enhance your workflow by using Azure Machine Learning and its tools
- Train, deploy, and manage models with Azure ML, Pytorch, TensorFlow, and scikit-learn
- Monitor, retrain, and redeploy models using MLOps tools
- Implement Explainable AI and other best practices in Azure ML
- Explore Azure Machine Learning’s integration with Databricks (Apache Spark)
MLOps: ML Model Deployment + AWS Sagemaker, GCP, Apple Cases
Platform:
Udemy
Rating:
4.7 out of 5
If you’re eager to learn best practices of Automation and ML models Deployment, then this is the course for you! With a strong focus on building a solid theoretical foundation and putting newly learned concepts into practice, this course covers essential skills in MLOps, Cloud Computing, and Business acumen that have proven successful over the past six years in the IT, Food and Travel industries.
By the end of this comprehensive course, you’ll be able to set up CI & CD pipelines, package ML models into Docker, run AutoML locally and in the cloud, train ML models for Apple devices, monitor and log ML experiments with MLFlow framework, set up and manage MLOps pipelines in AWS SageMaker, operate Model Registry and Endpoints in GCP VertexAI, and ultimately boost your career and MLOps studying efficiency. For those interested in deploying interactive analytical apps, check out the Streamlit course as well. Happy learning!
Skills you’ll learn in this course:
- Set up CI and CD pipelines
- Package ML models into Docker
- Run AutoML locally and in the Cloud
- Train ML models for Apple devices
- Monitor and Log ML experiments with MLFlow framework
- Set up and manage MLOps pipelines in AWS SageMaker
- Operate Model Registry and Endpoints in GCP VertexAI
- Boost your Career and MLOps studying efficiency
Deployment of Machine Learning Models
Platform:
Udemy
Rating:
4.4 out of 5
Deployment of Machine Learning Models is an all-inclusive online course that teaches you how to take your machine learning models from the research environment to a fully integrated production environment. This course is perfect for those who have built their first machine learning models and want to learn deployment or for software developers looking to step into deployment of fully integrated machine learning pipelines.
The course covers essential topics such as transforming Jupyter notebooks into production code, serving predictions from an API, creating a Python package, deploying into production environments, and using Docker to control software and model versions. By the end of the course, you’ll have a comprehensive understanding of the entire research, development, and deployment lifecycle of a machine learning model, learn best coding practices, and be well-equipped to deploy models in a way that suits your organization’s needs. Although some advanced topics such as model monitoring and advanced deployment orchestration are not covered, the course still provides foundational knowledge for successfully putting your models into production.
Skills you’ll learn in this course:
- Transforming code from Jupyter notebooks to production-ready code.
- Writing production code with tests, logging, and OOP.
- Deploying machine learning models as an API.
- Creating a Python Package.
- Understanding realistic production environments and deployment.
- Using Docker for version control.
- Implementing Continuous Integration and Continuous Delivery.
- Ensuring model reproduction in deployed environments.
Machine Learning Deep Learning model deployment
Platform:
Udemy
Rating:
4.4 out of 5
If you’ve been wanting to dive into machine learning and deep learning, here’s a course that not only helps you develop models but also teaches you how to make them accessible to different applications! This beginner-friendly course covers a variety of deployment techniques and provides hands-on examples to ensure you get the most out of the learning experience.
In this comprehensive course, you’ll learn how to create classification models using Scikit-learn, save and export the models and standard scalers to different environments, create REST APIs for local and cloud applications, and even work with TensorFlow and PyTorch models. Plus, you’ll explore machine learning model building with Scikit-learn, TensorFlow Keras, and PyTorch for beginners. Just make sure to have a Google Cloud (GCP) free trial account ready for some exciting cloud-based labs! By the time you’ve completed this course, you’ll have a solid understanding of machine learning deployment and be ready to take on exciting new projects in the field!
Skills you’ll learn in this course:
- Deploying Machine Learning and Deep Learning Models using various techniques.
- Creating and implementing REST APIs with Python Flask.
- Building and deploying TensorFlow and Keras models using TensorFlow Serving.
- Deploying PyTorch Models and converting them to TensorFlow format using ONNX.
- Implementing text classifier models for sentiment analysis, like Twitter sentiment analysis.
- Deploying models using TensorFlow.js and JavaScript.
- Tracking model training experiments and deployment with MLFlow.
- Utilizing Google Cloud Platform (GCP) for cloud-based labs and deployment.
MLOps Fundamentals: CI/CD/CT Pipelines of ML with Azure Demo
Platform:
Udemy
Rating:
4.1 out of 5
This online course is designed to teach the fundamentals of MLOps, a culture that emphasizes smooth implementation and productionization of Machine Learning models. MLOps has rapidly grown as a market solution to tackle the challenges faced by traditional machine learning lifecycle management. It involves the deployment of machine learning models into production, which is essential for providing real business value. Note that this course focuses on the MLOps principles, not Azure ML specifically, but an Azure demo section is provided to showcase the workings of an end-to-end MLOps project.
Throughout the course, students will learn the core basics and fundamentals of MLOps, exploring the challenges in traditional machine learning lifecycle management and how MLOps addresses these issues. Other topics covered include the standards and principles of MLOps, Continuous Integration (CI), Continuous Delivery (CD) and Continuous Training (CT) pipelines, MLOps maturity levels, and comparisons of MLOps tools and platforms. Additionally, the course includes a quick crash course on Azure Machine Learning components and an end-to-end CI/CD MLOps pipeline case study using Azure DevOps and Azure Machine Learning.
Skills you’ll learn in this course:
- Understanding MLOps core basics and fundamentals
- Identifying challenges in traditional machine learning lifecycle management
- Exploring how MLOps addresses issues and adds flexibility and automation
- Grasping standards and principles of MLOps
- Learning continuous integration (CI), continuous delivery (CD), and continuous training (CT) pipelines
- Familiarizing with MLOps maturity levels
- Evaluating MLOps tools and platforms, including comparisons
- Gaining hands-on experience with an end-to-end MLOps pipeline using Azure DevOps and Azure Machine learning
In conclusion, skilling up in MLOps through online courses is a game changer for your machine learning career. By mastering the principles and best practices, you’ll set yourself apart in the job market and pave the way for a smooth transition into a highly in-demand field. Don’t forget, continuous learning and development are essential to stay competitive and thrive in an ever-evolving industry like machine learning.
So, choose a course from the list we’ve provided, and dive into the world of MLOps. With numerous options available to suit your needs, there’s no better time to commit to expanding your skill set. Remember, investing in your growth today will reward you with numerous opportunities in this exciting and cutting-edge field. Stay innovative, be curious, and let your MLOps journey begin!