IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 130

Data Quality Metadata Big Data ETL 130

Pickl AI

APRIL 14, 2023

Falling into the wrong hands can lead to the illicit use of this data. Hence, adopting a Data Platform that assures complete data security and governance for an organization becomes paramount. In this blog, we are going to discuss more on What are Data platforms & Data Governance.

Data Platform 52

Data Platform Data Integration Data Ingestion Automation 52

AWS Machine Learning Blog

NOVEMBER 15, 2023

The teams built a new data ingestion mechanism, allowing the CTR files to be jointly delivered with the audio file to an S3 bucket. Principal and AWS collaborated on a new AWS Lambda function that was added to the Step Functions workflow.

Data Ingestion 118

Data Ingestion Metadata NLP Data Scientist 118

The MLOps Blog

JANUARY 26, 2024

A feature store is a data platform that supports the creation and use of feature data throughout the lifecycle of an ML model, from creating features that can be reused across many models to model training to model inference (making predictions). It can also transform incoming data on the fly. What is a feature store?

Machine Learning 72

Machine Learning Metadata ML Python 72

Mlearning.ai

FEBRUARY 16, 2023

Arranging Efficient Data Streams Modern companies typically receive data from multiple sources. Therefore, quick data ingestion for instant use can be challenging. Furthermore, a shared-data approach stems from this efficient combination. Superior data protection.

Business Intelligence 52

Business Intelligence Data Ingestion Metadata Machine Learning 52

The MLOps Blog

MARCH 12, 2024

Tools range from data platforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. Model management Teams typically manage their models, including versioning and metadata.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Large Language Models LLM 52

The MLOps Blog

MARCH 21, 2023

To make that possible, your data scientists would need to store enough details about the environment the model was created in and the related metadata so that the model could be recreated with the same or similar outcomes. You need to build your ML platform with experimentation and general workflow reproducibility in mind.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69