Machine Learning Engineering for Production (MLOps)

Share it with your friends Like

Thanks! Share it with your friends!

Close

Welcome to our event celebrating the launch of Machine Learning Engineering for Production (MLOps) Specialization featuring AI leaders in MLOps.

Topics we plan to cover:
-To what extent does the role of Data Scientist or MLE involve MLOps?
-How is MLOps actually implemented in an industry setting? Is there some kind of a framework people use?
-Is MLOps suitable for early-stage startups or only teams with enough resources as the big tech companies do?
-The latest trends on MLOps and how will the future of it evolve.
-What do you see as the biggest challenges for MLOps adoption?
-Apart from taking courses, what are some of the other resources or activities might recommend to learners interested in gaining practical experience with MLOps?

Speakers:
-Andrew Ng, Founder, DeepLearning.AI
-Robert Crowe, TensorFlow Developer Engineer, Google
-Laurence Moroney, AI Advocate, Google
-Chip Huyen, Adjunct Lecturer, Stanford University
-Rajat Monga, co-founder, Stealth Startup; Former lead of TensorFlow, Google
-Event moderator: Ryan Keenan, Director of Product, DeepLearning.AI

Let us know what you think of the event by filling out a quick survey here: https://bit.ly/3janNgZ

To learn more about DeepLearning.AI and sign up for future events: https://www.deeplearning.ai/events/
To sign up for Machine Learning Engineering for Production (MLOps), https://bit.ly/3j1DEhB

Comments

DeepLearningAI says:

3:2010:45 speaker introduction and their thoughts on MLOps
10:5520:41 Machine Learning Operations or “MLOps” for short, is really a nascent field. People might have seen some of the articles that are out there comparing and contrasting ML and DevOps and suggesting that MLOps is sort of a mashup of the two. Is that the right way to think about it? And to what extent do the roles of Data Scientist or ML Engineer involve MLOps?
20:5424:55 How is MLOps actually implemented in an industry setting? Is there some kind of a framework people use?
25:0429:57 Is MLOps suitable for early-stage startups, or only for teams with enough resources to essentially do what the big tech companies do? Do you invent your own tools?
30:0334:54 Chip, you've written a post that captured all the tools out there, and what are some of the trends. I'd like to get your perspective on this.
34:5644:21 Apart from taking courses, what are some of the other resources or activities that you might recommend to learners interested in gaining practical experience with MLOps?
44:4748:12 A question about "Experiment Tracking": “I understood that the MLOps pipeline needs to be data-centric, not model-centric, but when it comes to the experiments tracking (models that might not have been deployed) do we really need an application or framework? Don't you think that we need to address this requirement in an MLOps infrastructure”?
48:15– In the introduction to machine learning in production, we talk about the compromise between an ML Engineer and a business owner regarding the goals of an ML deployment (test set accuracy vs revenue). However, how should we educate the industrial management regarding the machine learning cycle? How to tell them the resources they are very motivated to invest in might not give them the result?
52:1554:56 MLE is much more than algorithms/models. What other tech skills should one have to be considered as a good MLE? say some backend engineer skill sets?
55:0156:44 Almost all job profiles require many years of work experience to become an MLOps engineer, so where should future MLOps engineer should start in his/her career?
56:501:03:12 ML Ops tools come and go. What are some important principles of ML ops that will remain relevant 5 years down the road that are tool agnostic?
1:03:551:09:18 Course 3 demo by Robert Crowe

YOU COMPUTING says:

Nice job…👍

serious coder says:

Gathered all the data rock stars.

The AI Epiphany says:

Nice, it's about time that we start having conversations on this topic. Too many researchers think that ML starts and ends with an idea followed by a paper being submitted to a conference. There is a lot of software engineering and engineering in general that needs to be done to get ML to make our lives better otherwise no one cares.

AmjD S says:

Ai is all monopolized by some folks who are continously taking benefits from it. Whatever they propose becomes standards and years down the line we realize that it wasn't a right decision and let's try something else

Batman says:

Thanks to Andrew Ng for creating a free course on Coursera!

DistortedV12 says:

Thanks for pulling this together! Really great set of guests. If could recommend, I wish questions were a little better at teasing out each person's expertise and think this would've been more interesting as a slide presentation format and then final panel with curated questions.

Balu Motukuru says:

Can we please post the article that chip has written on the various MLOps tools out there currently ?

Kim Ingay says:

Greetings from Philippines 🇵🇭

Pankaj Kumar says:

Thanks for this

ABUBAKAR MUHAMMAD SADIQ says:

Hi greetings from Nigeria

hansalas says:

When is the 4th course due ?

Write a comment

*

Area 51
Ringing

Answer