Machine Learning Engineering for Production (MLOps)

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well.

Earlier last month, DeepLearning.AI launched a much anticipated Machine Learning Engineering for Production (MLOps) Specialization. It covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this efficiently, as well as important concepts in the emerging fields of MLOps and data-centric AI.

To celebrate the launch of the new program, we are pleased to invite you to join us on June 30 for our live virtual event where our instructors for the MLEP Specialization are joined with industry speakers to talk about machine learning engineering for production, as well as a sneak peek of the MLEP Specialization.

Speakers:

• Andrew Ng, Founder, DeepLearning.AI
• Robert Crowe, TensorFlow Developer Engineer, Google
• Laurence Moroney, AI Advocacy Lead, 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

Topics:

• 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 that might recommend to learners interested in gaining practical experience with MLOps?

The event will start off with an overview of the Specialization, a panel discussion, and follow with a Q&A session. We’ll be using Slido for Q&A. Please note that only the people who sign up for the Slido ticket will have access to Slido to post and upvote questions.

We will send you the livestream link 3 days before the event.

Can't attend the live YouTube event? Don’t worry. Register now to get the recorded session.

About the speakers:

• Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer both in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world's largest MOOC platform. Dr. Ng now focuses his time primarily on his entrepreneurial ventures, looking for the best ways to accelerate responsible AI practices in the larger global economy.

• Robert Crowe is a data scientist and TensorFlow addict. He has a passion for helping developers quickly learn what they need to be productive. Since the very early days, he’s used TensorFlow and is excited about how rapidly it's evolving to become even better. Before moving to data science, Robert led software engineering teams for large and small companies, focusing on providing clean, elegant solutions for well-defined needs.

• Laurence Moroney leads AI Advocacy at Google with a vision to make AI easy for developers and to widen access to ML careers for everyone. He’s written dozens of programming books, the most recent being ‘AI and ML for Coders’ at O’Reilly. Laurence believes MOOCs are among the greatest ways to learn and is excited to create TensorFlow Specializations with DeepLearning.AI on Coursera. When not working with technology, he’s an active member of the Science Fiction Writers of America and has authored several sci-fi novels and comics books and a produced screenplay. Laurence is based in Washington state, where he drinks way too much coffee.

• Chip Huyen is an engineer who develops best engineering practices for machine learning production. Through her work with Snorkel AI, NVIDIA, Netflix, and Primer AI, she has helped some of the world’s largest organizations develop and deploy machine learning systems. She teaches Machine Learning Systems Design at Stanford. She’s the author of four bestselling Vietnamese books. Her writings have been published in leading newspapers in the USA, France, and Vietnam.

• Rajat Monga is co-founder and former lead of TensorFlow at Google, powering machine learning research and products worldwide. As a founding member of the Google Brain team he was involved in designing and implementing DistBelief and more recently TensorFlow. Prior to this role, he led teams in AdWords, built out the engineering teams and designed web scale crawling and content-matching infrastructure at Attributor, and co-implemented and scaled eBay’s search engine. Rajat received a B.Tech. in Electrical Engineering from Indian Institute of Technology, Delhi. He left Google recently to bring machine intelligence closer to data in the business world.

What is the Machine Learning Engineering for Production (MLOps) Specialization about?

The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.

In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology and solve real-world problems.

Enroll now.