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Axfood has a structure with multiple decentralized datascience teams with different areas of responsibility. Together with a central data platform team, the datascience teams bring innovation and digital transformation through AI and ML solutions to the organization.
Data engineering – Identifies the data sources, sets up dataingestion and pipelines, and prepares data using Data Wrangler. Datascience – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation. Monitoring setup (model, datadrift).
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 dataingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.
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 datascience project and get it to production.
The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Dataingestion (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.
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.
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