AWS Machine Learning Blog

FEBRUARY 21, 2025

Steep learning curve for data scientists: Many of Rockets data scientists did not have experience with Spark, which had a more nuanced programming model compared to other popular ML solutions like scikit-learn. This created a challenge for data scientists to become productive.

Data Science 72

Data Science Data Scientist Data Ingestion DevOps 72

IBM Journey to AI blog

SEPTEMBER 20, 2023

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps.

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

AWS Machine Learning Blog

FEBRUARY 27, 2024

Each product translates into an AWS CloudFormation template, which is deployed when a data scientist creates a new SageMaker project with our MLOps blueprint as the foundation. These are essential for monitoring data and model quality, as well as feature attributions.

Machine Learning 136

Machine Learning DevOps Data Scientist Data Quality 136

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

APRIL 26, 2023

The first is by using low-code or no-code ML services such as Amazon SageMaker Canvas , Amazon SageMaker Data Wrangler , Amazon SageMaker Autopilot , and Amazon SageMaker JumpStart to help data analysts prepare data, build models, and generate predictions. We recognize that customers have different starting points.

ML 114

ML Machine Learning Data Science Data Drift 114

AWS Machine Learning Blog

FEBRUARY 7, 2025

In this post, we assign the functions in terms of the ML lifecycle to each role as follows: Lead data scientist Provision accounts for ML development teams, govern access to the accounts and resources, and promote standardized model development and approval process to eliminate repeated engineering effort.

ML 64

ML Data Scientist ML Engineer Data Science 64

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52