Best AWS SageMaker for Beginners in Machine Learning

AWS Summit London was held on May 10 at the ExCeL conference center in East London. This was my first time at an AWS conference, and I was impressed by the scale. There must have been at least 10,000 participants and all the lectures I attended were close to capacity. I just had a general idea of the conversations I wanted to get involved, but for some reason, I was drawn to the new AWS product, SageMaker. I had never heard of this until I saw the keynote address with Dr. Werner Vogels of Amazon.com. Many of my friends had studied Machine Learning (ML) at their university, but I did not know anyone who had used it in the industry. In the world of monitoring, there are all sorts of interesting ideas about using ML for predictive analytics, so I wanted to know more about how other ML companies use and how SageMaker can be leveraged by companies that want to start ML.

During Dr. Vogels' presentation, he was quick to point out that Amazon.com had used ML for 20 years in its e-commerce business. It's easy to understand why this was important to them when they grew up. On the recommendation side of products, being able to make a smart sale to customers should have a big impact on your sales. But many companies do not operate on the Amazon scale. Can ML have a business impact on a small software company or a traditional retail business?

For what SageMaker was created were the types of companies in which the ML is not critical to the business the way it is on Amazon. The hardest part of ML is the great effort involved in building, training and adjusting your model to provide accurate forecasts for your business. Like many other services that AWS offers, they try to eliminate some of this complexity so you can deploy it more quickly.

Ramp up
The first thing you must do to build your model is to connect it to your data. Obviously, this is much easier if you already have data stored in AWS so it can be a blocker if you have stored them elsewhere. However, it does not take much effort to get some test data in S3 so the model can access them. Once you have your data in place, you should choose your algorithm. It is here that a product like SageMaker really helps the beginner because it already has preloaded algorithms with default settings to get you started. So if we start using SageMaker in Opsview Monitor, we will probably use the DeepAR algorithm, which uses large sets of time series data to generate forecasts and forecasts by analyzing patterns in that data.

Train
Training is what separates ML from a simple input / output algorithm. The more data you process and the more time you spend, the better the forecast model will be. Training is also the most computationally intensive part, so again, that's where AWS tries to reduce the overhead. You only pay for what you use, so you can train your model until you are satisfied with the results. The adjustment and adjustment required to properly configure your algorithm can also be done automatically by SageMaker.

Unfold
This is where SageMaker delivers real business value. Being able to deploy your ML solution at AWS allows companies to quickly get the benefits of their model and see how it works in the real world. With features such as one-click deployment, A / B testing, and hosting, you can be up and running with your first ML application without deployment overhead for your local infrastructure.

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Conclusion
It can be intimidating to start in the ML world, but AWS has tried to reduce the entry barrier with the introduction of SageMaker. Microsoft and Google already have their own ML solutions so it will be interesting to see how these three evolve over time. It is good that AWS has seen the value of ML for small businesses and has integrated it with their other offerings, as it is the largest provider of cloud services. I hope this will allow more companies to use ML in their day-to-day operations and benefit from the information it provides.