The speed at which machine learning (ML) is evolving within the cloud industry is exponentially growing, and public cloud providers such as AWS are releasing more and more services and feature updates to run in parallel with the trend and demand of this technology within organizations today. Within this article, I will briefly describe the seven AWS machine learning services announced at re:Invent 2018.
If you want to dive deeper into each of these services, take a look at our AWS Machine Learning Services courses to understand how each of these new services is used and the benefit that they can bring to your organization.
What exactly is machine learning?
Wikipedia defines ML as:
The scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead.
Sounds easy, right……?
Clearly, ML is a specialized field of focus and traditionally requires a specific set of skills, largely centered around programming languages, mathematical algorithms, analytical and statistical skills, and data science. Unless you have these targeted set of skills, understanding and learning ML to become a practitioner in this area can be a little daunting. However, AWS is trying to change this perception.
As AWS releases more and more services covering ML, they are helping to bridge the gap between having the traditional skill set of a ML engineer to those looking to venture into the ML arena for the first time. This is allowing people to become skilled with using ML technology without having to be an expert in the traditional skillset.
From a technology perspective, there is a wide range of services, frameworks, and tools that all fall under the ML umbrella. From an AWS perspective, these include:
Image source: https://aws.amazon.com/machine-learning/
As new technology is developed, such as enhanced CPU processing power, along with data ingestion and storage performance at a lower cost point, it makes it easier for AWS to develop managed ML services. This opens the door for engineers to design, develop, and train models for business application.
AWS Machine Learning Services
As a part of this ML growth, AWS announced a number of ML services heading our way, these include:
Amazon SageMaker Ground Truth: Amazon SageMaker Ground Truth is a labeling service which provides both automatic and human workforce labeling features. With Ground Truth, you simply upload your unlabeled datasets into an S3 bucket, create a manifest file with pointers to each of the images, and place the manifest file within the same S3 bucket.
Watch this short video on Amazon SageMaker Ground Truth taken from Cloud Academy’s AWS Machine Learning Services 2019 – Re:invent Reminders Course:
Amazon Forecast: Amazon Forecast is a managed service, which provides you with a time series forecasting capability, i.e. being able to predict the future, and therefore gain and execute an advantage over your competitors who can’t.
Amazon Comprehend Medical: Natural Language Processing, or NLP, is the process in which meaning is extracted from human language. This enables a machine to interpret and understand human language. Leveraging this capability within the medical field can help and complement many things, none more so than the overall patients well being. For example, of where NLP is used to identify potential health risk problems for a patient by examining their historical clinical records. To support this, AWS provides a specialized version of their Amazon Comprehend service called Amazon Comprehend Medical.
Amazon Textract: Although OCR (Optical Character Recognition) is a proven technology, traditional OCR does have limitations. Amazon Textract, on the other hand, is OCR on steroids. Built and provided by Amazon, Amazon Textract under the hood leverages ML to provide a level of service that surpasses many existing OCR solutions. Textract excels in scanning documents that contain tabulated information, tables of figures, etc.
Amazon Personalize: With Amazon Personalize, you can leverage ML tools to generate highly targeted and personalized product listings that help maintain end-user engagement and ultimately increase product turn over. Amazon Personalize draws on the many years of knowledge and experience that Amazon has acquired running the amazon.com e-commerce site.
Amazon SageMaker RL: Reinforcement learning is a ML technique that uses a reward-based approach. Reinforcement learning involves an agent that takes particular actions within an environment in such a way as to always maximize a goal.
AWS DeepRacer: To get the next wave of ML enthusiasts on board and familiar with AWS ML technologies, AWS has released AWS DeepRacer. AWS DeepRacer is a new global racing league for autonomous handheld-sized racing cars. The idea is quite simple: Build and train a reinforcement learning model that can be uploaded into an autonomous handheld-sized car. The reinforcement learning model is then used in conjunction with the onboard camera, gyroscope, and accelerometer sensors to guide it around a racing track as quickly as possible. If your time is the fastest, you’ll be at the top of the leaderboard.
To view our entire library of AWS Machine Learning content, containing learning path, courses and hands-on labs, take a look here: https://cloudacademy.com/library/machine-learning/