AWS, DeepLearning.AI Partner On Data Science Specialization

The new three-course specialization is designed to help data professionals master the essentials of machine learning and efficiently deploy data science projects at scale in the AWS cloud.

Amazon Web Services has partnered with education technology company DeepLearning.AI to offer a new specialization to help data professionals quickly master the essentials of machine learning and efficiently deploy data science projects at scale in the AWS cloud.

The three-course Practical Data Science Specialization with Amazon SageMaker, AWS’ fully managed machine learning (ML) service, is available through Coursera’s education platform.

The new, massive open online course (MOOC) addresses a critical factor to success with ML: growing the talent pool and helping more people become ML practitioners, according to Bratin Saha, vice president of machine learning services for AWS.

“At Amazon, our goal is to train every developer we hire on machine learning,” said Saha, who announced the new specialization during the opening keynote address for today’s virtual AWS Machine Learning Summit. “In fact, machine learning courses are now mandatory for any engineer joining Amazon, and we want to make training accessible to even more developers.”

A Coursera specialization is a series of courses that help participants master a skill, and participants earn a certificate when they complete the work. The new MOOC is ideal for those who are ready to practically implement ML models in their organizations, according to Saha.

Participants should have a working knowledge of ML algorithms and principles, proficiency in Python programming at an intermediate level and familiarity with Jupyter notebooks and statistics. Completion of Coursera’s Deep Learning Specialization or an equivalent program is recommended. Participants also should also be familiar with the fundamentals of AWS and cloud computing, and completion of Coursera’s AWS Cloud Technical Essentials or a similar program is considered the prerequisite knowledge base.

The 10-week curriculum includes comprehensive labs developed specifically for the specialization to provide hands-on experience with a variety of ML concepts and skills. The instructors are from AWS: Antje Barth, senior developer advocate for AI and ML; Shelbee Eigenbrode and Sireesha Muppala, principal solutions architects for AI and ML; and Chris Fregly, principal developer advocate for AI and ML.

The first course, Analyze Datasets and Train ML Models Using AutoML, covers foundational concepts for exploratory data analysis, automated machine learning (AutoML) and text classification algorithms. In the Build, Train and Deploy ML Pipelines Using BERT course, participants will learn to automate a natural language processing task by building an end-to-end ML pipeline using Hugging Face’s highly optimized implementation of the BERT algorithm with Amazon SageMaker Pipelines. The third course, Optimize ML Models and Deploy Human-in-the-Loop Pipelines, covers a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance and generate new training data with human intelligence.

“The field of data science is constantly evolving with new tools, technologies and methods,” said Betty Vandenbosch, chief content officer at Coursera. “Through hands-on learning, cutting-edge technology and expert instruction, this new content will help learners acquire the latest job-relevant data science skills.”