Machine Learning

Make accurate predictions, get deeper insights from your data, reduce operational overhead, and improve customer experience. Let us help you at every stage of your ML adoption journey with the most comprehensive set of artificial intelligence (AI) and ML services, infrastructure, and implementation resources.

Why is Machine Learning Important?

Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

Who's using it?

Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors.

Financial services

Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cybersurveillance to pinpoint warning signs of fraud.

Transportation

Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.

Government

Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.

Applications of Machine Learning

MLOPS and Automation

Machine learning models are often trained to automate back-office tasks that require less specialized human skills or can benefit from machine support. This process, most commonly known as MLOps or AIOps, automates functions like security monitoring, network audits, and network self-healing efforts.

Deep learning

This type of ML includes neural networks that cause it to function like a human brain, which makes it possible for deep learning models to closely copy human behaviors for assigned tasks. Some common examples include chatbots and virtual assistants.

Smarter Data Analytics

The most widely used application of ML is found in data mining and data analytics. Once ML models are trained to comb through big data sets, they can not only move through data at a faster rate than humans, they can also deliver deeper insights and typically avoid user error problems.