DagsHub

AUGUST 2, 2023

This is how we came up with the Data Engine - an end-to-end solution for creating training-ready datasets and fast experimentation. Let’s explain how the Data Engine helps teams do just that. Preparing and organizing data into a format suitable for training models presents significant challenges for ML teams.

ML 52

ML ML Data Engineering Data Engineer 52

ODSC - Open Data Science

AUGUST 9, 2023

Our focus will be hands-on, with an emphasis on the practical application and understanding of essential machine learning concepts. Attendees will be introduced to a variety of machine learning algorithms, placing a spotlight on logistic regression, a potent supervised learning technique for solving binary classification problems.

Machine Learning 52

Machine Learning Machine Learning AWS Big Data 52

IBM Journey to AI blog

AUGUST 11, 2023

Because the machine learning lifecycle has many complex components that reach across multiple teams, it requires close-knit collaboration to ensure that hand-offs occur efficiently, from data preparation and model training to model deployment and monitoring. Foundation models aim to solve this problem.

Data Science 68

Data Science Machine Learning Machine Learning ML 68

Kaggle

JULY 29, 2020

In August 2019, Data Works was acquired and Dave worked to ensure a successful transition. David: My technical background is in ETL, data extraction, data engineering and data analytics. What supervised learning methods did you use? Sports analytics is how I got started in data science.

ETL 71

ETL Data Scientist Data Science Machine Learning 71

Dataconomy

APRIL 18, 2025

What is machine learning operations (MLOps)? MLOps refers to a collection of practices designed to unify machine learning model development and operational processes. Key concepts of MLOps MLOps incorporates several foundational concepts that bridge the gap between machine learning and traditional software deployment practices.

Machine Learning 103

Machine Learning Machine Learning ML ML 103