Last answered:

10 Jul 2023

Posted on:

27 May 2023

0

Machine Learning Operations

Hi,


1. Thank you for the course. It is very good course with good coding example. However it would be uniqe if the learnig process could be equiped with or applied on a real end-to-end project with coding exampe from inception to production. 


2. Does this course covers the same information and process steps as Machine Learning Operations MLOs/MLOps. If Not what is the difference between Machine Learning Process and Machine Learning Operations.


Thank you


3 answers ( 0 marked as helpful)
Instructor
Posted on:

31 May 2023

0

Hi!

Good to hear from you!

1. Please consider this course:

https://learn.365datascience.com/courses/machine-learning-deep-dive-business-applications-and-coding-walkthroughs/

Inside you will find complete coding walkthroughs.

2. MlOps and ML Process are two different areas. The ML Process course describes all the considerations and prep work you need to when developing an ML model as a data scientist. MlOps, on the other hand, is a set of activity that involves deploying ML models in production. Working on MlOps requires computer science skills that data scientists not always have.

Best,

Ned

Posted on:

31 May 2023

0

Thank you Ned for you cleare and concise answer.


1. I meant if each course is covered with a real-life project that covers different aspect of experience needed in real-life production area, Your courses would be superb. 


2. I hope you consider a making a course on Machine Learning Operations with a real-life project if that is possible. Because after you learn to crunch data you need the knowledge required to apply them in production. Am i right?!?!

Instructor
Posted on:

10 Jul 2023

1

Hi Khalid - I have a list of relevant projects on my YouTube that may be in line with what you're looking for. They are meant to completment the course: https://www.youtube.com/playlist?list=PL2zq7klxX5AReJn7nZfqOKLZ3IpKj7fwc


In particular, the project from scratch series may be very helpful!

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