AWS Machine Learning Blog

FEBRUARY 27, 2024

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

Machine Learning 137

Machine Learning DevOps Data Scientist Data Quality 137

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 115

ML Machine Learning Data Science Data Drift 115

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

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 Data Scientist 52

The MLOps Blog

MAY 9, 2023

The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Data ingestion (extraction and versioning). Data validation (writing tests to check for data quality). Data preprocessing. It checks the data for quality issues and detects outliers and anomalies.

ML 98

ML Machine Learning Metadata Data Science 98

ODSC - Open Data Science

FEBRUARY 24, 2025

While traditional models rely solely on broad training datasets, RAG ensures responses are grounded in the most current and specific data available. This data undergoes processing into machine-readable formats and is stored as vector embeddings in an index using tools like Pinecone, Weaviate, orMilvus.

Data Drift 52

Data Drift Data Ingestion Natural Language Processing LLM 52