During the past few years, the demand for machine learning specialists and engineers has soared. These two roles now rank among the top emerging jobs on LinkedIn. More recently, machine learning is being adopted by a wide range of industries, from medical diagnostic companies to finance firms and more. Udacity created the Intro to Machine Learning Nanodegree program and Machine Learning Engineer Nanodegree program in response to this demand to provide access to this growing tech field to a broader audience.
There is a growing demand for engineers who are able to integrate machine learning models into globally available production applications like voice assistants and recommendation engines. Knowing how to build machine learning models is a great starting point. But, to truly make an impact, a data scientist or developer needs to know how to take a model out of the lab and into the real world so that it can be used to make millions or billions of predictions.
“Industry demand for the latest AI skills is at an all-time high. In collaboration with Amazon, we’ve updated the Udacity Machine Learning Nanodegree program to make it possible to gain the latest machine learning deployment skills anywhere in the world on the AWS platform,” says Sebastian Thrun, Co-Founder, President, Executive Chairman of Udacity.
AWS Educate and Amazon SageMaker collaborated with Udacity to create new deployment content for the Machine Learning Engineer Nanodegree program. AWS Educate provides Udacity students with access to AWS content and AWS promotional credits. These benefits allow students to use Amazon SageMaker for assignments developed in tandem with AWS subject matter experts (SMEs). The course examines a variety of machine learning models as they are applied at-scale to real-world tasks. Students learn how to deploy both unsupervised and supervised algorithms, and apply them to tasks such as feature engineering and time-series forecasting. This content addresses questions such as:
- How do you decide on the correct machine learning model for a given task?
- How can you use cloud deployment tools such as Amazon SageMaker to work with data and improve your machine learning models?
In addition to learning about model deployment, students also learn about model serving and updating. The course now shows how to connect a deployed sentiment analysis model to a website by using an AWS API. After deploying the model, it’s updated to account for changes in the underlying text data – an especially valuable skill in industries that continuously collect data. By the end of this section, students should have the skills needed to train and deploy models to solve tasks of their own design!
ML courses from beginner to advanced
Udacity’s Intro to Machine Learning and Machine Learning Engineer Nanodegree programs are part of Udacity’s School of AI, a set of free courses and Nanodegree programs designed by and for software developers. If you’re new to machine learning, their Intro to Machine Learning Nanodegree program is an entry point to learn foundational machine learning concepts such as data cleaning and supervised models. If you already have machine learning skills, the updated Machine Learning Engineer Nanodegree program, featuring Amazon SageMaker, focuses on teaching you the latest in machine learning deployment technologies.
Enroll today to get practical experience deploying machine learning models at-scale with an AWS Educate membership.
About the Author
Sally Revell is a Senior Manager, Product Marketing for AWS AI. She loves to work on innovative products that have the potential to impact people’s lives in a positive way. In her spare time, she loves to do yoga, horseback riding and being outdoors in the beauty of the Pacific Northwest.