Amazon Internet Providers has partnered with training expertise firm DeepLearning.AI to supply a brand new specialization to assist knowledge professionals shortly grasp the necessities of machine studying and effectively deploy knowledge science tasks at scale within the AWS cloud.
The three-course Sensible Knowledge Science Specialization with Amazon SageMaker, AWS’ totally managed machine studying (ML) service, is accessible by means of Coursera’s training platform.
The brand new, huge open on-line course (MOOC) addresses a crucial issue to success with ML: rising the expertise pool and serving to extra folks turn into ML practitioners, in keeping with Bratin Saha, vp of machine studying providers for AWS.
“At Amazon, our purpose is to coach each developer we rent on machine studying,” stated Saha, who introduced the brand new specialization through the opening keynote tackle for in the present day’s digital AWS Machine Studying Summit. “In actual fact, machine studying programs are actually obligatory for any engineer becoming a member of Amazon, and we wish to make coaching accessible to much more builders.”
A Coursera specialization is a sequence of programs that assist contributors grasp a talent, and contributors earn a certificates after they full the work. The brand new MOOC is right for individuals who are able to virtually implement ML fashions of their organizations, in keeping with Saha.
Contributors ought to have a working data of ML algorithms and ideas, proficiency in Python programming at an intermediate degree and familiarity with Jupyter notebooks and statistics. Completion of Coursera’s Deep Studying Specialization or an equal program is advisable. Contributors additionally also needs to be accustomed to the basics of AWS and cloud computing, and completion of Coursera’s AWS Cloud Technical Necessities or an identical program is taken into account the prerequisite data base.
The ten-week curriculum consists of complete labs developed particularly for the specialization to supply hands-on expertise with a wide range of ML ideas and abilities. The instructors are from AWS: Antje Barth, senior developer advocate for AI and ML; Shelbee Eigenbrode and Sireesha Muppala, principal options architects for AI and ML; and Chris Fregly, principal developer advocate for AI and ML.
The primary course, Analyze Datasets and Practice ML Fashions Utilizing AutoML, covers foundational ideas for exploratory knowledge evaluation, automated machine studying (AutoML) and textual content classification algorithms. Within the Construct, Practice and Deploy ML Pipelines Utilizing BERT course, contributors will be taught to automate a pure language processing process by constructing an end-to-end ML pipeline utilizing Hugging Face’s extremely optimized implementation of the BERT algorithm with Amazon SageMaker Pipelines. The third course, Optimize ML Fashions and Deploy Human-in-the-Loop Pipelines, covers a sequence of performance-improvement and cost-reduction methods to routinely tune mannequin accuracy, examine prediction efficiency and generate new coaching knowledge with human intelligence.
“The sphere of knowledge science is continually evolving with new instruments, applied sciences and strategies,” stated Betty Vandenbosch, chief content material officer at Coursera. “By way of hands-on studying, cutting-edge expertise and professional instruction, this new content material will assist learners purchase the newest job-relevant knowledge science abilities.”