AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

Data Drift 118

Data Drift Auto-complete ML Automation 118

ODSC - Open Data Science

JULY 19, 2023

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. For the customer, this helps them reduce the time it takes to bootstrap a new data science project and get it to production.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

AWS Machine Learning Blog

APRIL 26, 2023

Data engineering – Identifies the data sources, sets up data ingestion and pipelines, and prepares data using Data Wrangler. Data science – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation. Monitoring setup (model, data drift).

ML 110

ML Machine Learning Data Science Data Drift 110

DataRobot Blog

FEBRUARY 11, 2022

These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally. And DataRobot’s MLOps accelerates getting Machine Learning into production, thereby reducing spending on infrastructure and sparing data science talent from mundane tasks.

Data Drift 52

Data Drift DevOps Machine Learning Data Scientist 52

The MLOps Blog

JUNE 27, 2023

With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

Metadata 134

Metadata Machine Learning Data Scientist Data Quality 134

The MLOps Blog

AUGUST 3, 2023

As you’ve been running the ML data platform team, how do you do that? How do you know whether the platform we are building, the tools we are providing to data science teams, or data teams are bringing value? If you can be data-driven, that is the best. The data scientists are here with software engineers.

ML 52

ML Data Scientist Software Engineer Python 52

AWS Machine Learning Blog

AUGUST 29, 2023

This architecture design represents a multi-account strategy where ML models are built, trained, and registered in a central model registry within a data science development account (which has more controls than a typical application development account). He’s a pet lover and is passionate about snowboarding and traveling.

Data Scientist 91

Data Scientist Data Quality Python ML 91

The MLOps Blog

MAY 9, 2023

Data validation This step collects the transformed data as input and, through a series of tests and validators, ensures that it meets the criteria for the next component. It checks the data for quality issues and detects outliers and anomalies. Kedro Kedro is a Python library for building modular data science pipelines.

ML 98

ML Machine Learning Metadata Automation 98