AWS Machine Learning Blog

FEBRUARY 27, 2024

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

Machine Learning 134

Machine Learning DevOps Data Scientist Data Quality 134

IBM Journey to AI blog

SEPTEMBER 20, 2023

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps. IBM watsonx consists of the following: IBM watsonx.ai

Large Language Models 240

Large Language Models DevOps AI Modeling Automation 240

Pickl AI

JUNE 29, 2023

Furthermore, The platform’s versatility extends beyond data analysis. Advantages of Using Splunk Real-time Visibility One of the significant advantages of Splunk is its ability to provide real-time data visibility. Thus, it lets users gain insights from vast data in real time.

Big Data 52

Big Data DevOps Data Analysis Machine Learning 52

ODSC - Open Data Science

JULY 19, 2023

Data Estate: This element represents the organizational data estate, potential data sources, and targets for a data science project. Data Engineers would be the primary owners of this element of the MLOps v2 lifecycle. The Azure data platforms in this diagram are neither exhaustive nor prescriptive.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

The MLOps Blog

MARCH 21, 2023

” — Isaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 Monitoring Monitoring is an essential DevOps practice, and MLOps should be no different. Checking at intervals to make sure that model performance isn’t degrading in production is a good MLOps practice for both teams and platforms.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69