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. And everybody agrees that in production, this should be automated.” Yet this leads into another important point.

Data Ingestion 241

Data Ingestion Big Data Explainability ETL 241

AWS Machine Learning Blog

FEBRUARY 21, 2025

Responsibility for maintenance and troubleshooting: Rockets DevOps/Technology team was responsible for all upgrades, scaling, and troubleshooting of the Hadoop cluster, which was installed on bare EC2 instances. This created a challenge for data scientists to become productive. Analytic data is stored in Amazon Redshift.

Data Science 70

Data Science Data Scientist Data Ingestion DevOps 70

AWS Machine Learning Blog

JANUARY 5, 2024

Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. AWS also helps data science and DevOps teams to collaborate and streamlines the overall model lifecycle process. MLOps – Model monitoring and ongoing governance wasn’t tightly integrated and automated with the ML models.

Data Science 131

Data Science Data Drift DevOps Auto-complete 131

AWS Machine Learning Blog

MARCH 5, 2025

Scaling ground truth generation with a pipeline To automate ground truth generation, we provide a serverless batch pipeline architecture, shown in the following figure. The serverless batch pipeline architecture we presented offers a scalable solution for automating this process across large enterprise knowledge bases. 201% $12.2B

Generative AI 76

Generative AI LLM AI AI 76

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

Generative AI 124

Generative AI Prompt Engineering Prompt Engineer ML 124

Marktechpost

JULY 5, 2024

The curriculum also includes classical search, automated planning, and probabilistic graphical models for comprehensive AI training. It covers advanced topics, including scikit-learn for machine learning, statistical modeling, software engineering practices, and data engineering with ETL and NLP pipelines.

Data Science 62

Data Science Machine Learning Data Analysis Software Engineer 62

Marktechpost

JULY 17, 2023

We can automate the procedure to deliver forecasts based on new data continuously fed throughout time. A few automated and enhanced features for feature engineering, model selection and parameter tuning, natural language processing, and semantic analysis are noteworthy.

Machine Learning 98

Machine Learning Data Mining Data Scientist Data Science 98