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
Factory machines that even the devil doesn’t think of. Intelligent production process.
Data scientists spend a lot of time on data cleaning and munging, so that they can finally start with the fun part of their job: building models. After you have engineered the features and tested different models, you see how the prediction performance improves. However, the job is not done when you have a high performing model. The deployment of your models is a crucial step in the overall workflow and it is the point in time when your models actually become useful to your company. In this session you will learn about various possibilities and best practices to bring machine learning models into production environments. The goal is not only to make live prediction calls or have the models available as REST API, but also what needs to be considered to maintain them. This talk will focus on solutions with Python (flask, Cloud Foundry, Docker, and more) and the well established ML packages such as Spark MLlib, scikit-learn, and xgboost, but the concepts can be easily transferred to other languages and frameworks. Speaker SUMIT GOYAL Software Engineer IBM