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

10 most in-demand generative AI skills

CIO

Most relevant roles for making use of NLP include data scientist , machine learning engineer, software engineer, data analyst , and software developer. They’re also seeking skills around APIs, deep learning, machine learning, natural language processing, dialog management, and text preprocessing.

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

AI Chihuahua! Part I: Why Machine Learning is Dogged by Failure and Delays

d2iq

Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail , and for the few models that are ever deployed, it takes many months to do so. As little as 5% of the code of production machine learning systems is the model itself. Adapted from Sculley et al.

article thumbnail

Data collection and data markets in the age of privacy and machine learning

O'Reilly Media - Data

In this short talk, I describe some interesting trends in how data is valued, collected, and shared. Economic value of data. It’s no secret that companies place a lot of value on data and the data pipelines that produce key features. But if data is precious, how do we go about estimating its value?

article thumbnail

Investors flock to fund an AI cornerstone: Feature stores

TechCrunch

Introduced as a concept by Uber in 2017, feature stores provide a unified place to build and share features across different teams in an organization. They serve as the interface between data and [AI] models.”

article thumbnail

Predibase exits stealth with a low-code platform for building AI models

TechCrunch

“The major challenges we see today in the industry are that machine learning projects tend to have elongated time-to-value and very low access across an organization. “Given these challenges, organizations today need to choose between two flawed approaches when it comes to developing machine learning. .

article thumbnail

DBeaver takes $6M seed investment to build on growing popularity

TechCrunch

The open source product proved so popular that he launched a company to support it in 2017, and began building a commercial product for users with enterprise requirements. So actually anyone who needs to work with data can use DBeaver,” she told TechCrunch.