TechCrunch
APRIL 7, 2021
As a company founded by data scientists, Streamlit may be in a unique position to develop tooling to help companies build machine learning applications. Data scientists can download the open-source project and build a machine learning application, but it requires a certain level of technical aptitude to make all the parts work.
CIO
JUNE 11, 2024
The company is offering eight free courses , leading up to this certification, including Fundamentals of Machine Learning and Artificial Intelligence, Exploring Artificial Intelligence Use Cases and Application, and Essentials of Prompt Engineering. Registration for the beta exams for the two certifications opens August 13.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Altexsoft
JULY 23, 2020
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. MLOps vs DevOps.
DevOps.com
APRIL 21, 2023
With the rapid advancement of artificial intelligence (AI) and machine learning (ML), companies need to understand and evaluate their readiness to adopt these technologies and drive material business outcomes.
Advertiser: Data Robot
Demand for data scientists is surging. With the number of available data science roles increasing by a staggering 650% since 2012, organizations are clearly looking for professionals who have the right combination of computer science, modeling, mathematics, and business skills. Collecting and accessing data from outside sources.
DevOps.com
AUGUST 10, 2022
Machine learning operations, or MLOps for short, is a key aspect of machine learning (ML) engineering that focuses on simplifying and accelerating the process of delivering ML models to production and maintaining and monitoring them. The post MLOps Vs. DevOps: What’s the Difference?
Dzone - DevOps
JANUARY 5, 2024
Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized the way we approach problem-solving and data analysis. These technologies are powering a wide range of applications, from recommendation systems and autonomous vehicles to healthcare diagnostics and fraud detection.
Expert insights. Personalized for you.
338 
Let's personalize your content