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

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