Due to the growing importance of MLOPs as a specialization, an aspiring ML engineer has more opportunities than he can typically expect
MLOPs has evolved as an independent approach in machine learning, which applies to the entire life cycle from data gathering to model deployment. MLOPs enable the channels of communication between data scientists and operations professionals. A recent study by NewVantage Partners estimated that around 15% of companies have deployed artificial intelligence capabilities into widespread production. It is always easy to deploy models through MLOps than in conventional methods and leaves little scope for an ML project to fail in its last stage. An MLOps engineer is responsible for model deployment which happens after the machine learning model is built. And due to the growing importance of MLOPs as a specialization, an aspiring ML engineer has more opportunities than he can typically expect to pursue an MLOps course without having an iota of doubt. Here is the list of top 10 MLOps courses to help kickstart an MLOps career.
1. Machine Learning Engineer for Microsoft Azure [Udacity]:
It is a 3-month certificate program, that teaches the concepts like using Azure Machine Learning including optimizing an ML pipeline in Azure spending just 5 – 10 hours every week. Students should have prior experience in Python, machine learning, and statistics to draw maximum benefit from the course.
2. MLOPs (Machine Learning Operations Fundamentals) [Coursera]:
A Google cloud training program is part of the Machine Learning Engineer Professional Certificate offered by Google. The course curriculum includes MLOPs concepts, building AI platform pipelines, Kubeflow pipelines for AI platforms, etc. By the end of this course, the student would be able to use core technologies for effective MLOps implementation, adopt CI/CD practices in AI platforms, and implement reliable training and inference workflows.
3. Applied Machine Learning: Foundations [LinkdIn]:
It is a popular course in MLOps offered by Derek Jedamski, an author and creator of applied machine learning. Unlike other courses which focus on choosing the right ML algorithm, he teaches to choose the right ML tools to solve almost any kind of ML problem. In this course, you will get to learn the concepts of machine learning ranging from exploratory data analysis to evaluating a model not seen before examples.
4. Demystifying Machine Operations [Pluralsight.com]:
This course basically addresses the concerns and issues faced particularly after deploying the ML model. In this course, you will learn about implementing machine learning operations starting with exploring ways in implementing machine learning models and then moving on to learning machine learning operations and their applications for model development.
5. Certified MLOps Course Training [360digitmg.com]:
A certificate course in MLOps engineering which comes with complimentary classes for beginners in DevOps, Kubernetes, and Python programming. The important course modules include teaching deploying models into production environments, and using open-source frameworks like Tensorflow Extended, Apache Beam, Apache Airflow, Kubernetes, and Kubeflow.
6. Machine Learning Engineering for Production (MLOps) Specialization [Deeplearning.AI]:
A course offered by Deeplearning.AI, it lasts for 4 months and is taught by AI experts like Andrew Ng, Cristian Bartolome, Robert Crowe, and Laurence Moroney. By the end of the course, you will be able to design an ML production system end-to-end, establish a model baseline, and prototype, and make improvements along with building pipelines, and best practices in delivering the model.
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7. Machine Learning Operations (MLOps) Certification [cloudxlab.com]:
This course teaches you end-to-end machine learning system designing with more than 17 projects for the hands-on learning experience. Through this course you will gain capabilities in training, deploying, scaling, and monitoring the ML model in action. Apart from that, you gain expertise in building pipelines, using Spark MLlib, and gain practical knowledge in TensorFlow, Keras, Linux, Git, Python, Docker, Kubernetes, Graffana, Prometheus, and Jenkins.
8. MLOps Zoomcamp [KDNuggets.com]:
A free course offered by popular tech platform KDNuggets focuses on practical aspects of model deployment. This course is meant for people with experience in concepts like Python development, Docker, command line, and a deep understanding of machine learning project development. The applicants are required to have a minimum of one year of programming experience.
9. MLOps Certification Training Course (MLOps Certified Professional) [devopsschool.com]:
In this course, you will learn to deploy machine learning models in production and at scale. With their best MLOps tools, techniques, and practices, you would get hands-on experience with developing machine learning models and implementing them through their life cycle.
10. Advanced Certification in Machine Learning and Cloud [IIT Madras]:
It is an advanced certification program in Machine Learning and Cloud that comes with more than 20 case studies and projects, practical hands-on workshops, and industry mentorship sessions. It is considered India’s most advanced cloud program.
Source: analyticsinsight.net
