AI News

AUGUST 29, 2023

The operationalisation of data projects has been a key factor in helping organisations turn a data deluge into a workable digital transformation strategy, and DataOps carries on from where DevOps started. It’s all data driven,” Faruqui explains. And everybody agrees that in production, this should be automated.”

Data Ingestion 231

Data Ingestion Big Data Explainability ETL 231

Pickl AI

JUNE 29, 2023

It initiates the collection, indexing, and analysis of machine-generated data in real-time. It helps harness the power of big data and turn it into actionable intelligence. Moreover, it allows users to ingest data from different sources. Additionally, Splunk can process and index massive volumes of data.

Big Data 52

Big Data DevOps Data Analysis Machine Learning 52

ODSC - Open Data Science

JULY 19, 2023

Personas associated with this phase may be primarily Infrastructure Team but may also include all of Data Engineers, Machine Learning Engineers, and Data Scientists. Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

FEBRUARY 7, 2025

For options 2, 3, and 4, the SageMaker Projects Portfolio provides project templates to run ML experiment pipelines, steps including data ingestion, model training, and registering the model in the model registry. Alberto Menendez is a DevOps Consultant in Professional Services at AWS.

ML 63

ML Data Scientist ML Engineer Data Science 63

AWS Machine Learning Blog

NOVEMBER 7, 2024

We explored multiple big data processing solutions and decided to use an Amazon SageMaker Processing job for the following reasons: It’s highly configurable, with support of pre-built images, custom cluster requirements, and containers. When inference data is ingested on Amazon S3, EventBridge automatically runs the inference pipeline.

ML 109

ML Machine Learning Automation Data Scientist 109