AWS Machine Learning Blog

FEBRUARY 27, 2024

Automation of building new projects based on the template is streamlined through AWS Service Catalog , where a portfolio is created, serving as an abstraction for multiple products. The model will be approved by designated data scientists to deploy the model for use in production. Alerts are raised whenever anomalies are detected.

Machine Learning 112

Machine Learning DevOps Data Quality Data Scientist 112

AWS Machine Learning Blog

FEBRUARY 23, 2023

Automation levels The SAE International (formerly called as Society of Automotive Engineers) J3016 standard defines six levels of driving automation, and is the most cited source for driving automation. This ranges from Level 0 (no automation) to Level 5 (full driving automation), as shown in the following table.

Automation 91

Automation Neural Network Machine Learning Data Scientist 91

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. The typical score.py

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

AWS Machine Learning Blog

APRIL 26, 2023

This includes AWS Identity and Access Management (IAM) or single sign-on (SSO) access, security guardrails, Amazon SageMaker Studio provisioning, automated stop/start to save costs, and Amazon Simple Storage Service (Amazon S3) set up. MLOps engineering – Focuses on automating the DevOps pipelines for operationalizing the ML use case.

ML 88

ML Machine Learning Data Science Data Drift 88

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 Quality Data Scientist 134

The MLOps Blog

MARCH 12, 2024

Monitoring Monitor model performance for data drift and model degradation, often using automated monitoring tools. Feedback loops: Use automated and human feedback to improve prompt design continuously. Develop the text preprocessing pipeline Data ingestion: Use Unstructured.io

Prompt Engineer 52

Prompt Engineer Prompt Engineering LLM Large Language Models 52

The MLOps Blog

MAY 9, 2023

The pipelines let you orchestrate the steps of your ML workflow that can be automated. The orchestration here implies that the dependencies and data flow between the workflow steps must be completed in the proper order. Reduce the time it takes for data and models to move from the experimentation phase to the production phase.

ML 98

ML Machine Learning Metadata Automation 98