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

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. Monitoring setup (model, data drift).

ML 108

ML Machine Learning Data Science Data Drift 108

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 119

Data Drift Auto-complete ML Automation 119

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). The data scientist can review and approve the new version of the model independently.

Data Scientist 76

Data Scientist Data Quality Python ML 76

The MLOps Blog

AUGUST 3, 2023

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the data platform in his previous company and is also co-creator of open-source framework, Hamilton. To a junior data scientist, it doesn’t matter if you’re using Airflow, Prefect , Dexter.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

MARCH 28, 2023

The DevOps and Automation Ops departments are under the infrastructure team. Brainly’s journey toward MLOps Since the early days of ML at Brainly, infrastructure, and engineering teams have encouraged data scientists and machine learning engineers working on projects to use best practices for structuring their projects and code bases.

Machine Learning 59

Machine Learning Automation Data Scientist ML 59

The MLOps Blog

MAY 9, 2023

The platform typically includes components for the ML ecosystem like data management, feature stores, experiment trackers, a model registry, a testing environment, model serving, and model management. It checks the data for quality issues and detects outliers and anomalies. The staging environment is for integration testing.

ML 98

ML Machine Learning Metadata Data Science 98