AWS Machine Learning Blog

SEPTEMBER 12, 2023

Email classification project diagram The workflow consists of the following components: Model experimentation – Data scientists use Amazon SageMaker Studio to carry out the first steps in the data science lifecycle: exploratory data analysis (EDA), data cleaning and preparation, and building prototype models.

Data Science 133

Data Science Data Scientist AWS ML 133

AWS Machine Learning Blog

JULY 31, 2023

Data preparation, feature engineering, and feature impact analysis are techniques that are essential to model building. These activities play a crucial role in extracting meaningful insights from raw data and improving model performance, leading to more robust and insightful results.

ML 98

ML ML Data Preparation Machine Learning 98

Dataconomy

AUGUST 16, 2023

A model builder: Data scientists create models that simulate real-world processes. These models can predict future events, classify data into categories, or uncover relationships between variables, enabling better decision-making.

Data Scientist 172

Data Scientist Data Science Machine Learning Machine Learning 172

AWS Machine Learning Blog

JUNE 5, 2025

Legacy workflow: On-premises ML development and deployment When the data science team needed to build a new fraud detection model, the development process typically took 24 weeks. In the event of deployment issues, automated rollback mechanisms revert to a stable model version, minimizing disruptions and maintaining business continuity.

Machine Learning 60

Machine Learning Machine Learning AWS ML 60

Iguazio

DECEMBER 14, 2023

There are 6 high-level steps in every MLOps project The 6 steps are: Initial data gathering (for exploration). Exploratory data analysis (EDA) and modeling. Data and model pipeline development (data preparation, training, evaluation, and so on).

ML 52

ML ML Data Science Data Preparation 52

Heartbeat

MAY 29, 2023

In this article, we will explore the essential steps involved in training LLMs, including data preparation, model selection, hyperparameter tuning, and fine-tuning. We will also discuss best practices for training LLMs, such as using transfer learning, data augmentation, and ensembling methods.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Kaggle

JULY 29, 2020

NeuML was working on a real-time sports event tracking application, neuspo but sports along with everything else was being shut down and there were no sports to track. The early days of the effort were spent on EDA and exchanging ideas with other members of the community. Sports analytics is how I got started in data science.

ETL 71

ETL Data Scientist Data Science Machine Learning 71