article thumbnail

Enhancing customer care through deep machine learning at Travelers

CIO

And we recognized as a company that we needed to start thinking about how we leverage advancements in technology and tremendous amounts of data across our ecosystem, and tie it with machine learning technology and other things advancing the field of analytics. But we have to bring in the right talent.

article thumbnail

NJ Transit creates ‘data engine’ to fuel transformation

CIO

Data engine on wheels’. To mine more data out of a dated infrastructure, Fazal first had to modernize NJ Transit’s stack from the ground up to be geared for business benefit. Today, NJ Transit is a “data engine on wheels,” says the CIDO. We have shown out value,” Fazal says of the transformation. “We

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Integrate VSCode With Databricks To Build and Run Data Engineering Pipelines and Models

Dzone - DevOps

Databricks is a cloud-based platform designed to simplify the process of building data engineering pipelines and developing machine learning models.

article thumbnail

What is Machine Learning Engineer: Responsibilities, Skills, and Value Brought

Altexsoft

In a world fueled by disruptive technologies, no wonder businesses heavily rely on machine learning. Google, in turn, uses the Google Neural Machine Translation (GNMT) system, powered by ML, reducing error rates by up to 60 percent. The role of a machine learning engineer in the data science team.

article thumbnail

Of Muffins and Machine Learning Models

Cloudera

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI.

article thumbnail

MLOps: Methods and Tools of DevOps for Machine Learning

Altexsoft

When speaking of machine learning, we typically discuss data preparation or model building. Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. MLOps lies at the confluence of ML, data engineering, and DevOps. More time for development of new models.

article thumbnail

Here’s where MLOps is accelerating enterprise AI adoption

TechCrunch

But with time, enterprises overcame their skepticism and moved critical applications to the cloud. DevOps fueled this shift to the cloud, as it gave decision-makers a sense of control over business-critical applications hosted outside their own data centers.