An Introduction to MLOps

#datascience #machinelearning #mlops

In this webinar, we will understand the need for MLOps and the components of MLOps. Session coverage includes

Overview of Machine Learning Lifecycle

What and Why of MLOps?

Components of MLOps

Automating ML Workflows

Live Q&A

[sreemantokesh]

This content is precious. I really like the fact that you mentioned that entire MLOPs can never be automated in a single shot.

[manikantadevasish]

This is just amazing content. Looking forward for the hands on session. 👏👏

[ankitshrivastava]

Kudos to you...everything was practical and to the point, what a start to the playlist.
Superb!

[avinashbaburavi]

Im into Automation testing and have no prior knowledge of ML/DS and ofcourse new to ML-ops. This video has striked interest in me to know more about MLops :) thank you 🙏

[amitmodi]

Left with No Words . Simple and best explanantion.

[snehotoshbanerjee]

Thanks for this great lecture. Just to add that the concept of Feature store is towards disconnected architecture. That is, moving out from tightly coupled E2E to the first destination till feature engineering metastore (Feature Store). From FT other teams or any downstream application can take the feature for their model building controlled by RBAC. Thanks.

[chefboyrdee]

I just wanted to say Thank you !!. This video really helped me put it all together. ! It's perfect in it's simplicity and understanding ! <3 Data Science.

[pavanim]

Awesome video.Thanks a lot for ur detailed and nice explanation.Learning more from ur videos.

[Krishalum]

so nice of you to share your experiences for the benefit of lot of aspiring engineers and data scientists... this is god's work

[flaskapp]

amazing sir :) never seen some other youtubers making this kind of videos. :) loved it.

[AIEngineeringLife]

Additional resource on my channel as mentioned in one of Q&A.

[inshamearaj]

Thanks for this contribution very well explained

[Patrickdot]

Thank you for this valuable information!

[ericburns]

Your accent is fine, by the way. Your English is very clear.

[Sefran]

nice channel. cheers. good to see more of the engineering side of ML

[pranabkumarmanna]

Amazing Videos .. Kindly always share your presentation, it most helpful during revisioning.

[jeromeeusebius]

I agree with your view that automating everything is probably not very realistic as managing the whole thing can then become a serious challenge. The comment about TFX is right. While it's a nice tool for end-to-end MLOPs, writing pipelines even for simple cases results in complicated code and steps. It would be nice if something of it can be abstracted to an higher level. I just started learning TFX only a few weeks now, so take this with a grain of salt. I am sure there are probably workflows that do the abstraction I am talking about.

[sndselecta]

Mr. Srivatsan, you teach very well thank you.

[blockchain]

can you please provide some links to implement Deep learning models in MLOps pipeline.

[stepancabelka]

Thank you very much indeed. This was extremely informative and it helped me to get a really good understanding of what MLOPs is. Only one remark, if I may. Can you please talk a little bit more slowly? Sometimes it is difficult to catch what you are saying.