As it might be evident from some of my previous blog posts, at AgilePoint, AI and Natural Language Processing have been at forefront of our product development in last few years. Things like skipping process instances or rolling them back based on certain key decision factors governing the state of that instance were always strong points for the product but with Machine Learning and Natural Language Processing we are trying to take it few notches up.

Previously we had done this with Microsoft Azure Machine Learning Studio, Amazon Machine Learning and Salesforce Prediction Builder and got some great feedback from clients and today I am happy to announce that we have extended this capability to Amazon SageMaker as well.

For those of you who are new to Amazon SageMaker, it helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning. It has replaced Amazon Machine Learning as the preferred machine learning offering from AWS. It provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon ML makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.

In the following video, we are actually going to step by step build one of the prediction models for you to get familiar with how this is done. Since we are going to show how to build, train and deploy model in SageMaker from scratch, we have broken this video in four parts.

Introduction: