Unite.AI

NOVEMBER 13, 2024

My journey to founding Acceldata in 2018 began nearly 20 years ago as a software engineer, where I was driven to identify and solve problems with software. As organizations increasingly rely on AI to drive business decisions, the need for trustworthy, high-quality data becomes even more critical.

Machine Learning 147

Machine Learning Large Language Models Data Quality Automation 147

IBM Journey to AI blog

JULY 30, 2024

AI Developer / Software engineers: Provide user-interface, front-end application and scalability support. Organizations in which AI developers or software engineers are involved in the stage of developing AI use cases are much more likely to reach mature levels of AI implementation.

Generative AI 266

Generative AI Data Scientist Software Engineer AI 266

Unite.AI

DECEMBER 13, 2024

Prior to Yanolja, Junyoung had a distinguished career at Google, where he worked for nearly two decades in various roles, including Software Engineer, Engineering Manager, and Engineering Director. Travel involves dreaming, planning, booking, and sharingprocesses that generate immense amounts of data.

Software Engineer 100

Software Engineer Data Quality Data Platform Automation 100

ODSC - Open Data Science

OCTOBER 20, 2023

HPCC Systems — The Kit and Kaboodle for Big Data and Data Science Bob Foreman | Software Engineering Lead | LexisNexis/HPCC Join this session to learn how ECL can help you create powerful data queries through a comprehensive and dedicated data lake platform.

Data Science 52

Data Science NLP Machine Learning Data Analysis 52

The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. As you’ve been running the ML data platform team, how do you do that?

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

MARCH 21, 2023

Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases. It would make sure that all development and deployment workflows use good software engineering practices. My Story DevOps Engineers Who they are?

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69