MLOps Zero to Hero (Udemy.com)

The course was very well explained, with clear examples and hands-on demos. I gained a deep understanding of various MLOps concepts and found the practical approach very helpful. Thank you, Abhishek, for curating such a great course and explaining the concepts so effectively. I would definitely recommend this course to anyone looking to gain a strong understanding of MLOps.

I actually help me how in production deployed ML Model securing with high availabilty and zero downtime ,also understanding model deployment in VM and containerizing ML model through docker and KServe for deploying in Kubernetes Workload with scaling and self healing,load balancing,MLflow for experiment tracking ,DVC (Data Version Control) to versioning datasets,understing sagemaker to deploy ml application while managing domains ,profile,IAM permissions for production security best preatices,Orchastrating pipeline through kubeflow,how to design CI CD through github actions

Excellent course by Abhishek Veeramalla! The explanations are clear, practical, and easy to understand. I especially liked the hands-on approach and real-world examples around MLOps, Kubernetes, Docker, MLflow, AWS, and model deployment. As someone coming from a DevOps background, this course is helping me understand how DevOps practices are applied to the ML lifecycle. The content is well structured and very useful for building practical MLOps skills. Thank you, Abhishek, for creating such a valuable course. Highly recommended for anyone who wants to learn MLOps in a practical way!

I really liked the 'Blackboard' sections where he organized the topics before going into the technology. This is something I haven't seen many other instructors do, they just dive into the IDE or Console which causes new folks to get lost very quickly in syntax without understanding how everything fits together.

The Big Picture This course takes a highly practical, infrastructure-first approach to deploying machine learning models into production. It does not just teach theoretical concepts; it forces you to understand the actual orchestration layer behind modern AI platforms—taking you from absolute containerization basics all the way to cloud-native model serving.

The cource and content is good. but its not seems like zero to hero cource. if we have some backgound in devops and python we will understand what u say else it is haeded. insted of blackboard and handwritten explanation better use ppts with good fonts. and include maximum labs. overall this is a good one

It was really a good hands on course as i was looking for MLOps learning for long time. I am an SME in DevOps It was way easier to understand the Contents. to Implement it was a cake walk for me as i have strong hold on the DevOps engineering. I look forward to show case my skills in this area in real time.

The course is very well structured and provides a clear understanding of deployment concepts in a precise and practical manner. I would like to extend my appreciation to Abhishek Veeramalla for designing the course so effectively—it covers most of the deployment standards currently followed in the industry while keeping the duration concise, enabling learners to quickly grasp and implement these practices in their own projects. One suggestion for improvement would be to include topics on observability and retraining, as these are also critical components of modern deployment workflows.

Abhishek did an exceptional tutoring on MLOPS, this is one of the best course i found... Only thing is, there is no FULL end-to-end MLOPS project using all the tools we have learned in the session... i was expecting to make use of all the tools and create a project..but indeed the current end-to-end project is still good. hence the 4 starts!! :) :) hope you will add the FULL MLOPS project in coming days..

This course provides a solid hands-on introduction to implementing and designing ML pipelines for data ingestion, model versioning/tracking, model serving, and inference within Kubernetes environments. While the content is strong in these areas, I found that it does not sufficiently cover important topics such as model monitoring and performance evaluation, nor does it explore feature engineering and feature stores in depth. Additionally, including an example on LLMOps would greatly enhance the course, as large language models are increasingly relevant in modern ML workflows. Overall, I would recommend this course as an entry-level resource. However, it does not fully reach the “hero level” suggested by its title.