10 Best Resources To Learn MLOps in 2023

Machine learning operations (MLOps) is a relatively new discipline that is rapidly gaining importance in the world of machine learning (ML) and artificial intelligence (AI). MLOps refers to the process of managing the entire ML lifecycle, from development to deployment and monitoring. In order to become proficient in MLOps, it is essential to have access to the right resources. 

In this article, we will discuss the top 10 resources to learn MLOps.

igmGuru

igmGuru is a popular online learning platform that offers advanced and easy-to-go courses on MLOps. In the last few months, many working professionals have enrolled in the MLOps course of igmGuru to become masters in that technology. Some of the popular courses on MLOps include “MLOps Engineer Nanodegree” and “AI Infrastructure: Deployment and MLOps” by Google Cloud. These courses are designed to provide a hands-on learning experience and cover topics such as infrastructure as code (IaC), model monitoring, and automation.

MLOps.community

MLOps.community is an open-source community that focuses on sharing best practices, tools, and frameworks for MLOps. The community consists of industry experts, academics, and practitioners who share their knowledge and experience on various aspects of MLOps. It is a great resource for learning about the latest trends, technologies, and best practices in MLOps.

Coursera

Coursera is a popular online learning platform that offers a variety of courses on MLOps. Some of the popular courses on MLOps include “Machine Learning Engineering for Production (MLOps) Specialization” by Andrew Ng and “Introduction to Machine Learning Operations (MLOps)” by Google Cloud. These courses are designed to provide a comprehensive understanding of MLOps and cover topics such as model deployment, version control, and continuous integration/continuous deployment (CI/CD) pipelines.

GitHub

GitHub is a popular platform for hosting and collaborating on software projects. It is also a great resource for learning about MLOps. There are several open-source projects on GitHub that focus on MLOps, such as “Kubeflow” and “MLflow”. These projects provide a great opportunity to learn about the best practices, tools, and frameworks for MLOps.

AWS Machine Learning Blog

The AWS Machine Learning Blog is a great resource for learning about MLOps on the AWS platform. The blog covers a wide range of topics, such as model training, deployment, and monitoring. It also provides insights into the latest tools and frameworks for MLOps on AWS, such as SageMaker and TensorFlow.

Google AI Blog

The Google AI Blog is another great resource for learning about MLOps. The blog covers a wide range of topics, such as model serving, deployment, and monitoring. It also provides insights into the latest tools and frameworks for MLOps on the Google Cloud platform, such as Kubeflow and TensorFlow Extended (TFX).

LinkedIn Learning

LinkedIn Learning is an online learning platform that offers a variety of courses on MLOps. Some of the popular courses on MLOps include “MLOps: Continuous Delivery and Automation” and “Applied Machine Learning: Deployment”. These courses are designed to provide a comprehensive understanding of MLOps and cover topics such as model deployment, version control, and CI/CD pipelines.

Medium

Medium is a popular platform for sharing knowledge and insights on various topics. There are several publications on Medium that focus on MLOps, such as “The Startup” and “Towards Data Science”. These publications provide a great opportunity to learn about the latest trends, best practices, and tools for MLOps.

MLOps Academy

MLOps Academy is an online learning platform that focuses on teaching MLOps best practices, tools, and frameworks. The platform offers a variety of courses on MLOps, such as “MLOps Fundamentals” and “MLOps Advanced”. These courses are designed to provide a hands-on learning experience and cover topics such as model serving, version control, and automated testing.

YouTube

YouTube is a popular platform for sharing video content on various topics. There are several channels on YouTube that focus on MLOps, such as “The AI Academy” and “The MLOps Community”. These channels provide a great opportunity to learn about the latest trends, best practices, and tools for MLOps through video content.

Conclusion

MLOps is a rapidly evolving discipline that requires continuous learning and adaptation. There are several resources available that can help beginners learn MLOps. These resources include online learning platforms, open-source projects, blogs, publications, and video channels. By leveraging these resources, beginners can gain a comprehensive understanding of MLOps and develop the necessary skills to manage the entire ML lifecycle, from development to deployment and monitoring.

Follow this mlops tutorial for beginner to learn more about the technology.