Deploying AI & Machine Learning Models for Business | Python (Udemy.com)

As I am taking this course in 2024, I did not expect to have all coding activities running right, so I had to search for the workarounds to make the solutions work on the newer versions of the tools and libraries used by the instructor. At the same time, I learned quite a lot because I had to figure out how to solve these issues in order to have the containerized applications running accordingly. In my opinion, the positive aspect of the course is to give an overview end-to-end of all processes involved in deploying a machine learning solution and expose it through an API. Of course, it came at a cost, since some lessons are more superficial so that inexperienced students might struggle to grasp the contents and may find difficult to deploy the solutions successfully. As a whole, I learned quite a lot from this course and I thank you the instructor for making this it available.

brief but on-point. everything is clear and well explained. even though it covers very basic stuff related to deployment of a model, I was able to understand things I didn't have any prior knowledge of. I gave 4.5 because i'll have to learn the advanced topics of each module myself but the course gave me a good direction I need to follow. This is exactly what I needed today.

This is a solid introductory course, but doesn't go into the backend of how things work at any level of detail. We don't need a full breakdown of Random Forests or Keras but it would be helpful to go into more detail on how Flask and Docker work. The content of this class is basically an aggregation of blogs and docs I've read over the last few months. I was under the assumption we would learn Docker and Flask at a pretty strong level.

Thank you for providing this course! I was looking for a quick way to get into using Docker and priding my Flask apps to end users. Not only did I learn how to do this, but I learned some other cool tricks along the way! I do feel more confident in deploying Docker applications now and cannot wait to introduce this to my workflow at work! Pronunciation and audio quality were top-notch as well as a fast paced approach to your instructions. I also really appreciate you keeping your errors in the videos. This truly helps us students out in my opinion. Thank you!

Awesome course! To the point and for advanced user who wants to go the the next level. I would expect more examples as suggested by the instructor to try reading and writing on sql, or file etc - which will greatly enhance the course even further.

I love the pace and the way this instructor explained the programming syntax, problem and resolution. My only challenge is he should have expanded the Editor all the way to the right and move the terminal console below or above it. That way, students can see all the codes that was written. I know that I can download the code from the resource file, but watching and seeing everything in one place is a trivial bonus. Other than that, I love the practicality of this course.

The main problem I have with this course is the lack of a git repository. Also, the audio levels are a bit low and the closed captions are not very good. The material is accessible but the first quiz is absurdly difficult and arcane, and completely unrelated to the course content, suggesting it was cribbed from another source. The instructor is also unresponsive to questions. These problems aside, this is a nicely paced and enjoyable introduction to Docker.

The instructor has put together a very handy and practical guide that combines modeling and dev-ops in one succinct course. It is ideal for a working professional who wants to get the low-down at a rapid but hands on pace. Excellent material!

Good show; instruction;, showing errors and fixing them. Slow buildup of the code, good for zooming in, otherwise skipping to the end, Started on Windows10 trouble with python modules installing. worked in the end and learned. Docker windows Kills Virtualbox, had i known, wouldn't have bothered with windows, so started and ubuntu18 w, virtual machine, this worked very well so far. NO docker course, its enough, to get a (ML) api in a container working, just to get a start with it / taste of it.

There are literally 100's of ML courses out there but very few explain real world scenario of how to deploy trained models in real business requirements, other then whole ecosystem of TF-Serve or AWS Sagemaker, the most common way to serve the trained models to business apps is the one which is explained in this course. This will fix you up to right start, and to the point of deploying and serving your trained models in minutes from dev environment to (almost) production ready setup.

Exactly what I needed. Very concise, no-nonsense introduction to the subject. I like that the typical errors are shown in the videos, so the user is prepared to handle their own mistakes. Love the real life examples for deploying on production using production-grade tools.

The course definitely helped bridge the gap between making a ML model and deploying it using docker container. Only reason for 4 start rating is that its still missing the deployment aspects over cloud environment in real world scenarios, and how people would actually access the micro-services that is built over cloud and connecting to that.

I changed my review because of very quick and fast response. Thanks for considering the request and appreciate your help. Thanks