The goal of this post is to introduce you to machine learning – and specifically Amazon Machine Learning – and help you understand how the cloud can greatly simplify the implementation of a complex machine learning algorithm.
What is Machine Learning?
We humans learn a lot from everything going on around us. Over time, these day-to-day experiences “invisibly” become our knowledge base. Our growing ability to predict outcomes based on past experience can make a huge difference in our lives.
So if people can build out intelligence based on past experiences, why not computers?
Machine Learning, in computer science terms, is a tool for developing useful artificial intelligence. If enough data points can be processed and exposed to software that’s designed to identify patterns, then computers can appear to think for themselves without the need for explicit programming. They can, in other words, teach themselves.
Why Machine Learning is such a big deal these days
Today, most businesses are data driven. The truly historic volumes of data that are now available allow businesses to analyze the past and predict the future. In fact, failing to capitalize on available data could easily make the difference between success and complete failure. Working effectively with machine learning is a significant new IT competency, no less than Android app development.
It’s not unreasonable to divide the larger goals of data-driven development into three categories:
- Retrospective analysis and reporting
Applications generate a lot of data. These data are drawn from the way users act with, say, an application or web service. The data can be very useful for gaining insight into user experience, from which developers can better understand the way their application performs in the real world and refine it, improving user experience.
- Real time processing
By intelligently processing huge amounts of streaming data to a dashboard interface, users can visualize, and then understand changing trends or business states. With greater understanding, you’re much more likely to make smart decisions.
The latest machine learning trend is to use live data to predict users’ behavior and present them with helpful suggestions based on past activities.
Machine learning can help you build smarter applications. Here are some such apps you’ve probably used yourself:
- The Amazon.com site will recommend items based on your past browsing and buying patterns. The predictions can improve your shopping experience, but Amazon just loves their growing online retail revenues.
- Netflix identifies patterns in the movies you watch, and uses their insights to recommend more titles.
- Email spam filtering is one of the best and most common examples of predictive analysis.
Besides those very well known examples, machine learning is now commonly used for:
- Fraud Detection.
- Demand forecasting.
- Predictive customer support.
- Document classification.
- Content personalization.
Amazon Machine Learning
Successful machine learning runs require as much data as possible for input. The more data, the more accurate your analysis will be and the higher quality of your predictions.
But managing such huge data stores in a traditional environment has always been a challenge. Moreover, older storage solutions were designed for more highly structured data than you’re likely to use for machine learning – as the kind of data generated by mobile devices is by nature unstructured. Storing and processing such high volumes of unstructured data requires integrated compute technologies and scalable storage solutions.
So, who do you think of first when you’re looking for “integrated compute technologies and scalable storage solutions? AWS. And, in particular, their EC2, S3, Redshift, and RDS services.
Your machine learning infrastructure is ready for you now, sir.
But infrastructure is not the only requirement for machine learning implementations. You’ll also need to be well schooled in a wide range of subjects like linear algebra, probability theory, calculus of variations, graph theory, and optimization methods (Lagrange multipliers). And besides, closing the gap between Data Models and applications is time consuming and expensive.
Give up? No need. Amazon’s Machine Learning service provides visualization tools and wizards to guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. You will only need to provide the data set that needs to be analyzed, and Amazon Machine Learning will pretty much do the rest.
Amazon Machine Learning vs manual implementation:
What does Amazon Machine Learning offer?
- Amazon ML is robust and fully managed.
- It easily integrates with AWS ecosystem
- It’s easy and quick to deploy.
- Creating Amazon ML models can be done without prior experience in data analytics.
Cloud Academy has got you completely covered for Amazon Machine Learning: if you want to get a jump start, check out our machine learning course, Amazon Machine Learning for Human Activity Recognition hands-on lab and this terrific blog post guiding you through a real-world example using Python.
AWS Machine Learning Labs and Certification Preparation
Are you trying to dig deep into AWS Machine Learning but don't know where to start? Let's talk about how you can do that with Cloud Academy. Cloud technology democratizes so many things, not the least of which is the opportunity to experiment and learn. Take Machine Learning (ML), fo...
AWS Machine Learning Services
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 t...
How to Develop Machine Learning Models in TensorFlow
Predictive analytics and automation—through AI and machine learning—are increasingly being integrated into enterprise applications to support decision making and address critical issues such as security and business intelligence. Public cloud platforms like AWS offer dedicated services ...
Analyze CPU vs. GPU Performance for AWS Machine Learning
For teams training complex machine learning models, time and cost are important considerations. In the cloud, different instance types can be employed to reduce the time required to process data and train models. Graphics Processing Units (GPUs) offer a lot of advantages over CPUs wh...
New on Cloud Academy, January ’18: Security, Machine Learning, Containers, and more
LEARNING PATHS Introduction to Kubernetes Kubernetes allows you to deploy and manage containers at scale. Created by Google, and now supported by Azure, AWS, and Docker, Kubernetes is the container orchestration platform of choice for many deployments. For teams deploying containeri...
AWS Global Infrastructure: Availability Zones, Regions, Edge Locations, Regional Edge Caches
Amazon Web Services is a global public cloud provider, and as such, it has to have a global network of infrastructure to run and manage its many growing cloud services that support customers around the world. In this post, we'll take a look at the components that make up the AWS Global...
AWS re:Invent 2015: Real-World Smart Applications With Amazon Machine Learning
How to apply Machine Learning to social media to make your customers happy At his AWS re:Invent presentation, Alex Ingerman - technical product manager at AWS - went through the design and implementation of a real-world end-to-end application to transform a high-volume social stream in...