Deployment of Machine Learning Models

Highly recommended Great place to get started if you are a Data Scientist who needs to better understand the full pipeline which takes your awesome model and deploys it in production.

This course should be a staple for Data Scientists worldwide.

I had a vague idea on deploying ML models to production and this course gave me such good insight into the details behind it.

the best course i have seen still today for deploying ml models thanks to the instructors for their great effort, hoping much much greater courses from you , thanks for your great effort Its really heap for us to improve our knowledge Lots of important detail but moves at a good pace, really enjoying so far very clear overview, A complex approach was used to explain the flask section.

I highly recommend it.

This is a very hard to find course and I highly recommend it to any one working in the deployment of ML systems.

This is not a big problem as soon as one can reproduce all the steps with the other dataset and gain practical experience This is great course on deploying machine learning pipelines in production.

Being an aspiring Data Scientist, this course immensely helped me on the processes or steps involved in deploying machine learning models in a production environment.

I think course did a great job giving me an overview of how entire ML pipeline works in real life.

This is the first of many ML courses I have taken which used real data with many varied data types very close to real life Very interested in learning everything covered here.

exactly what I wanted to get, the aws focus in the end is kind of frustrating, but is probably unavoidable .. this course will be my reference for my first few ml-api-deployments in the new workplace :) The lecturer is well-versed in this area.

This course is truly an amazing and a unique course in that it teaches exactly what is needed to transform from Scientist to Engineer.

Very often code does not follow common best practices.

Much of the course coveys ideas through actual code and is downright practical in addressing issues when dealing with data sources, refreshing models, best practices in structuring projects and a lot more.

You two have explained the material so we can utilize it directly in the "real world".

Moving from academia to real world enterprise.

Gives a clear understanding of the pipeline steps as well as how as a data analysts you should perform the data analysis...I hope in some other videos we should be able to see how to handle Cardinality of the categorical data Great topics covered.

The data analysis and cleaning and feature engineering steps were extremely insightful.

I'm a student and I learned about lot many things.

I learned so many things that pertain to deployment and reproducibility.

Also, I felt many steps of how files and folders should be structured should have been explained much better even in the starting sections.

Likewise, it would have been a much better course, had it focused on the how and why of creating the files in the Github repo rather than just walking through them.