New Courses: Machine Learning Engineering for Production

Posted by Robert Crowe and Jocelyn Becker


Have you mastered the art of building and training ML models, and are now ready to use them in a production deployment for a product or service? If so, we have a new set of courses to get you going. Built as a collaboration between the TensorFlow team, Andrew Ng, and, the new set of courses are launching as a specialization on Coursera: The Machine Learning Engineering for Production (MLOps) specialization.

The new specialization builds on the foundational knowledge taught in the popular specialization, DeepLearning.AI TensorFlow Developer Professional Certificate, that teaches how to build machine learning models with TensorFlow. The new MLOps specialization kicks off with an introductory course taught by Andrew Ng, followed by courses taught by Robert Crowe and Laurence Moroney that dive into the details of getting your models out to users.

Every lesson comes with plenty of hands-on exercises that give you practice at preparing your data, and training and deploying models.

By the end of the specialization, you’ll be ready to design and deploy an ML production system end-to-end. You’ll understand project scoping, data needs, modeling strategies, and deployment requirements. You’ll know how to optimize your data, models, and infrastructure to manage costs. You’ll know how to validate the integrity of your data to get it ready for production use, and then prototype, develop, and deploy your machine learning models, monitor the outcomes, and update the datasets and retrain the models continuously.

You’ll learn how to implement feature engineering, transformation, and selection with TFX as well as how to use analytics to address model fairness and explainability issues, and how to mitigate bottlenecks. You’ll also explore different scenarios and case studies of ML in practice, from personalization systems to automated vehicles.

Typical ML pipeline
You’ll learn how processing requirements are different in deployment than in training
Use of Accelerators in Serving Infrastructure
You’ll learn about different tools and platforms for deploying your machine learning systems.
Product recommendations
A common use of ML in production is personalization systems for product recommendations.
Autonomous Driving Systems
A cutting edge use of ML in practice is to guide automated vehicles.

Despite the growing recognition of AI/ML as a crucial pillar of digital transformation, successful ML deployments are a bottleneck for getting value from AI. For example, 72% of a cohort of organizations that began AI pilots before 2019 haven’t deployed even a single application in production. A survey by Algorithmia of the state of enterprise machine learning found that 55% of companies surveyed haven’t deployed an ML model.

Models don’t make it into production and if they do, they break because they fail to adapt to changes in the environment. Deloitte identified lack of talent and integration issues as factors that can stall or derail AI initiatives. This is where ML engineering and MLOps are essential. ML engineering provides a superset of the discipline of software engineering that handles the unique complexities of the practical applications of ML. MLOps is a methodology for ML engineering that unifies ML system development (the ML element) with ML system operations (the Ops element).

Unfortunately, job candidates with ML engineering and MLOps skills are relatively hard to find and expensive to hire. Our new MLOps specialization teaches a broad range of many of the skills necessary to work in this field, and will help prepare developers for current and future workplace challenges. We believe that this is a valuable contribution to the ML community, and we’re excited to make it available.

Enroll today to develop your machine learning engineering skills, and learn how to roll out your ML models to benefit your company and your users.

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