Customers often ask us how to get started when they do not have a deep data science and machine learning (ML) background. At AWS, our goal is to put ML in the hands of every developer and data scientist.

AWS Training and Certification has partnered with edX to help you get started quickly and easily with ML with our interactive course, Amazon SageMaker: Simplifying Machine Learning Application Development. 

Available exclusively on edX, Amazon SageMaker: Simplifying Machine Learning Application Development is an intermediate-level digital course that provides a baseline understanding of ML and how applications can be built, trained, and deployed using Amazon SageMaker. Amazon SageMaker is a fully managed, modular service that covers the entire ML workflow. It helps you label and prepare your data; choose an algorithm; train the model; tune and optimize it for deployment; make predictions; and act.

This course was developed by AWS experts. It explains the following:

• Key problems that ML can address and ultimately help solve
• How to train a model using Amazon SageMaker’s built-in algorithms and a Jupyter Notebook instance
• How to deploy a model using Amazon SageMaker
• How to integrate the published SageMaker endpoint with an application

Amazon SageMaker: Simplifying Machine Learning Application Development is recommended for developers of all skills. If you want to take your models from concept to production quickly and easily, or are looking to level up in ML, this course is for you. Before beginning, we recommend that you have at least one year of software development experience. You should also have a basic understanding of AWS services and the AWS Management Console, either through previous experience or the AWS Developer Professional Series.

The course is divided into four weekly lessons, with an estimated two to four hours per week of study time. It features video-based lectures, demonstrations, and hands-on lab exercises. You can set your own pace and deadlines. Everyone can take the weekly quizzes, which are not graded and allow unlimited retries.

This on-demand, 100%-digital course is available now and is offered on a complimentary basis.

Get started today at Amazon SageMaker: Simplifying Machine Learning Application Development.

About the Author

Jennifer Davis is the Senior Manager, Product Marketing, for AWS Training and Certification.

When Electronic Caregiver’s founder and CEO, Anthony Dohrmann, started the company a decade ago, he was reacting to a difficult situation faced by 100 million Americans and countless individuals globally: the challenge of managing health treatment for chronic diseases. “Patients are often confused about their care instructions and non-adherence with care plans and medications schedules are estimated to cause 50% of all treatment failures,” he explains.

As such, Electronic Caregiver was designed “to improve the patient experience and to positively engage patients in their personal care plans. We improve communication between providers, families, and caregivers, and expedite a more informed response to the need of the aging and ill. We intend to reduce costly complications, improve health outcomes, and extend lifespans.”

Today, Electronic Caregiver’s solution revolves around Addison, a-state-of-the-art, 3D-animated virtual caregiver. She can engage in two-way conversations and is programmed for a user’s personal needs. Similar to a human in-home caregiver, Addison monitors patients’ activity, reminds them to take medications, collects vitals, and conducts real-time health assessments—all from the safety and comfort of a patient’s home. Whereas a patient would otherwise need to make myriad doctor visits or pay an in-home caregiver, Addison brings health solutions to the user wherever they are.

To power that life-changing magic, Electronic Caregiver relies on AWS in multiple ways. For raw computational power to store patient data in a HIPPA-compliant way, the team uses services including AWS Lambda functions. For the patient-facing experience, Electronic Caregiver has developed an augmented reality (AR) character named Addison using Amazon Sumerian. And for the intelligence behind that character including collecting and analyzing data, AWS IoT Core, AWS IoT Greengrass, and Amazon SageMaker are key to the solution. The architecture that the Electronic Caregiver team has developed is shown in the following diagram.

Bryan Chasko, CTO at Electronic Caregiver, comments, “We saw an opportunity to use the latest sensing, artificial intelligence, and other cloud-based technologies to address unmet customer needs with a fuller-featured solution than traditional alert devices.”

Specifically, Electronic Caregiver provides patients with wearable gadgets (such as a wrist pendant) and monitoring devices (such as a contact-free thermometer and a glucose meter) that are connected to the cloud.

To connect device fleets, AWS IoT Core easily and securely connects devices to the cloud via an MQTT lightweight communication protocol specifically designed to tolerate intermittent connections, minimize the code footprint on devices, and reduce network bandwidth requirements.

Whenever a user completes a health reading, such as checking their temperature or completing a physical therapy exercise, that activity generates data that the devices capture. That data can then be queried to check whether a measured value is in the expected range. If the reading is good, the patient will receive a contextually appropriate positive response from Addison.

For example, in cases where a patient is in physical therapy recovering from an injury, Electronic Caregiver monitors improvement to their range of motion. The built-in gamification rewards the patient with points and even sends gifts to their homes to celebrate improvements. This personalized support reinforces their ongoing commitment to their treatment plan and helps these patients execute their treatments properly; it is pivotal to their long-term recovery.

If a reading is atypical, Electronic Caregiver springs into action to get the patient back on track. From a technical perspective, AWS IoT Greengrass Machine Learning Inference pushes a machine learning model built in Amazon SageMaker directly to the edge device in the user’s home. The patient is asked specific questions to help assess the cause of the anomaly, and then they receive from their device a prediction of the likely reason(s) for this result as well as recommended solutions. These questions and solutions are voiced to the patient with Amazon Lex and Amazon Polly, as well as shared with the patient’s selected stakeholders (such as family members and doctors) so everyone on the individual’s care team is immediately aware.

With this set-up, it is as though the patient has a constant caregiver watching out for them, so they receive the quality of care typical of a full-time facility like a nursing home, but possible from their own house. As a further benefit, even individuals who are not co-located with the patient (such as family members on a different continent) can get real-time updates from across the world.

In addition to the connected wearables, Electronic Caregiver has developed a platform to track patients’ activity and ensure that they are conscious and mobile. If activity is not detected, Electronic Caregiver can summon emergency response, coming to the rescue quickly in the event of a fall or other lapse into unconsciousness.

There is a visual analytics monitoring system that also enables personalized monitored medication reminders. Motion is tracked by the visual analytics system and then the pills are identified with Amazon SageMaker-trained machine learning models. This means that Addison can pinpoint when a user has taken their medication and remind them if they’re late.

Amazon Lex also accepts verbal input from the user, so a patient can simply articulate that they’re taking a medication and the system logs it. This feature makes it feel almost like the caregiver is human. Just as someone would articulate to a housemate that they’d completed their medication routine, they can inform Addison.

“Only 3% of the US population can afford live caregiving,” Dohrmann notes. “We are bringing affordable, effective care alternatives to the world through Addison.”

About the Author

Marisa Messina is on the AWS ML marketing team, where her job includes identifying the most innovative AWS-using customers and showcasing their inspiring stories. Prior to AWS, she worked on consumer-facing hardware and then university-facing cloud offerings at Microsoft. Outside of work, she enjoys exploring the Pacific Northwest hiking trails, cooking without recipes, and dancing in the rain.