Introduction
Machine Learning has emerged to be one of the most sought after technologies in recent times. Amazon SageMaker is a fully managed machine learning platform in the cloud. It is an AWS service that can help you to analyze data and then and build, train, and deploy machine learning models in the cloud. SAP HANA is a business data platform. It is an in-memory relational database. SAP HANA's Python library contains a data frame which can be used to read data from a HANA instance and then examine the data inside a SageMaker notebook.
Potential Use Cases
Assume that you have millions of records in the database. You now need to predict or forecast based on the data you have. Here's exactly where you can leverage Amazon SageMaker to do the analysis and forecasting for you. SageMaker can be used in predictive analysis, medical image analysis, predictions in sports, marketing, climate, etc. You can also take advantage of Amazon SageMaker for detecting frauds in banking as well.
Sentiment analysis
You can take advantage of sentimen analysis to predict the behavior patterns of an individual. As an example, data from the Facebook can be used for sentimental analysis to understand the mood, emotions of individuals and then send out alerts if need be. A piece of writing of an individual can contain negetive thoughts and AI can be used here to predict the future moods of that individual. There can be an individual who might develop suicidal thoughts - data from social media like Facebook can be used to understand the behavioral patterns and then send out alerts to that person's contacts. After reading the data, you'll have to train a model and then use an algorithm to predict the future possible moods of the individual.
Sentiment analysis using Amazon SageMaker
In this section, we'll examine how we can take advantage of Amazon SageMaker for sentiment analysis. Sentiment analysis is a technique that uses the emotional tone used in words to understand the attitude, emotions expressed. This can be very helpful in many scenerios. The ability of Amazon SageMaker to easily build, train, and deploy machine learning models at any scale can be very helpful to build an application that has these capabilities.
Summary
Amazon SageMaker can help you build machine learning models with ease. SageMaker can be a great choice for pattern detection, forecasting, etc.
Joydip Kanjilal is a Microsoft Most Valuable Professional (MVP) in ASP.Net (2007 - 2012) for six consecutive years, as well as a speaker and the author of several books and articles. Here's the link to his MVP public profile: https://mvp.microsoft.com/en-us/PublicProfile/38659
He has around 25 years of experience in IT, with more than 18 years in Microsoft .Net and related technologies. He has been selected as MSDN Featured Developer of the Fortnight and as Community Credit Winner several times.
Joydip has authored nine books, reviewed about a dozen books and written more than 500 articles for MSDN, CodeMagazine, InfoWorld, SSWUG, Simple-Talk, Visual Studio Magazine, TechBeacon, TechTarget and several other sites of repute. Many of his articles have been featured at Microsoft's Official Site on ASP.Net.
He was a speaker at the Spark IT 2010 event and at the Dr. Dobb’s Conference 2014 in Bangalore. He has also worked as a judge for the Jolt Awards at Dr. Dobb's Journal. He is a regular speaker at the SSWUG Virtual Conference, which is held twice each year.
Joydip blogs regularly at https://www.infoworld.com/blog/microsoft-coder
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