The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. Flyte Flyte is a platform for orchestrating ML pipelines at scale.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

MAY 10, 2023

Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication. Standard data science practices could also be contributing to this issue. One might say that tabular data modeling is the original data-centric AI!

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

Kaggle

JULY 29, 2020

David: My technical background is in ETL, data extraction, data engineering and data analytics. I spent over a decade of my career developing large-scale data pipelines to transform both structured and unstructured data into formats that can be utilized in downstream systems.

ETL 71

ETL Data Scientist Machine Learning Machine Learning 71

Iguazio

FEBRUARY 8, 2024

Continuous monitoring of resources, data, and metrics. Data Pipeline - Manages and processes various data sources. ML Pipeline - Focuses on training, validation and deployment. Application Pipeline - Manages requests and data/model validations. Collecting feedback for further tuning.

ML 52

ML ML Data Scientist AI 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Socio-political events have also caused delays and issues, such as a COVID backlog, and with inert gases for manufacturing coming from Russia. For a given LOB, some events might be applicable to individual price levels independently. An important part of the data pipeline is the production of features, both online and offline.

AWS 113

AWS ML ML Clustering 113

Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. In the data pipeline phase—I’m just going to call out things that I think are more important than the obvious. So the basic ones: you collect and validate and prepare data.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. In the data pipeline phase—I’m just going to call out things that I think are more important than the obvious. So the basic ones: you collect and validate and prepare data.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Pickl AI

SEPTEMBER 5, 2024

Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling. Data Scientists can use Azure Data Factory to prepare data for analysis by creating data pipelines that ingest data from multiple sources, clean and transform it, and load it into Azure data stores.

Azure 52

Azure Data Scientist Machine Learning Machine Learning 52

AWS Machine Learning Blog

OCTOBER 18, 2023

The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. Tayo Olajide is a seasoned Cloud Data Engineering generalist with over a decade of experience in architecting and implementing data solutions in cloud environments.

AWS 130

AWS ML ML Machine Learning 130

DataRobot

SEPTEMBER 14, 2021

DataRobot now delivers both visual and code-centric data preparation and data pipelines, along with automated machine learning that is composable, and can be driven by hosted notebooks or a graphical user experience. Virtual Event. Learn More About DataRobot’s Vision and Roadmap for AI Cloud. September 23.

AI 52

AI AI Data Pipeline Data Preparation 52

DataRobot Blog

OCTOBER 3, 2017

Enterprise data architects, data engineers, and business leaders from around the globe gathered in New York last week for the 3-day Strata Data Conference , which featured new technologies, innovations, and many collaborative ideas. We look at data as an asset, regardless of whether the use case is AML/fraud or new revenue.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52