How Machine Learning and natural language processing works

Machine Learning is one of the most exciting technological developments in recent years. At its simplest, ML allows users to build and “train” applications with given and incoming data, in order to improve the performance of these applications over time.  

The potential uses for these applications are so various that they could span several articles, but to name a few:

  • A ML model that learns the visual defects normally seen in a given product, and automatically disposes of faulty products.
  • An application that suggests food or entertainment options based upon user activity history. 
  • Diagnosing potential diseases through an ML model that utilizes data related to those diseases, and makes accurate predictions about how and when they could emerge. 

Machine Learning has significant application potential for any business or organization that handles large amounts of data, and that wants to use that data to provide a higher quality product offering to their customers. 

As part of an AWS well-architected framework, these machine learning models can be easily incorporated to improve customer service, make accurate predictions about important trends, and even spotting potential points of performance or security failure. 

In this article, we will cover some of the major AWS services that relate to machine learning, services that Ibexlabs uses every day to help our clients to achieve their business goals. 

Amazon SageMaker

AWS SageMaker is a comprehensive ML service in AWS that handles every major aspect of machine learning implementation, including preparing data for the models, building the models themselves, training and fine-tuning them, and deploying them. 

Preparation

Using a number of services, SageMaker makes it easy to prepare data for the ML models, even if sourced from disparate data sources, or in a variety of formats. 

With SageMaker Ground Truth, it’s easy to label data, including video, images and text that are automatically processed into usable data. With auto-segmentation and a suite of tools, GroundWork can process and combine this data to build a single data label usable for ML models. Along with SageMaker Data Wrangler, and SageMaker Processing, AWS takes a data preparation process that could normally take weeks or months into one that only takes a few days, even just a few hours. 

Build

SageMaker centralizes everything related to your ML models in the form of SageMaker Studio Notebooks, which can be easily shared along with their related data. Through SageMaker JumpStart, you can pick from a number of built-in, open-source algorithms to begin processing your data, or create custom parameters for your machine learning model.  

Once you’ve decided on a model, SageMaker automatically begins processing data, and provides a clear, easy-to-use interface to understand the progress of your model and ongoing results. 

Training

Using a built-in training algorithm, or one of your own design, SageMaker gives a number of tools to train your model from the data that you have prepared, and even detect potential errors through an included debugger. 

The results of the training job are stored in an Amazon S3 bucket, where it can be readily visualized through other AWS services, like AWS Quicksight. 

Deployment

Having powerful machine learning models would not be very useful if they could not be readily deployed to your hosting infrastructure. Luckily, SageMaker makes it as simple as a single click to deploy your machine learning models to your existing services and applications. 

After deployment, SageMaker allows for real-time data processing and prediction. This has powerful implications for a number of fields, including finances and health. For example, businesses working in the stock market can make up-to-the-minute financial predictions of a given stock, and make more profitable investments by zero-ing in on the perfect time to buy. 

Incorporation with Amazon Comprehend, allows for natural language processing, transforming human speech into usable data to train better models, or provide a chatbot to customers through Amazon Lex.

In conclusion…

Machine Learning is no longer a fringe scientific curiosity, but instead occupies a central role in the decision making processes of thousands of businesses across the globe. With nearly limitless applications, and easy incorporation into the AWS framework, there has never been a better time to begin your Machine Learning journey. 

Contact Ibexlabs today for a free consultation, and learn how machine learning can help your business run more efficiently and provide better value to your customers. 

Santosh Peddada

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