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Data engineering – Identifies the data sources, sets up dataingestion 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, datadrift).
Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.
Monitoring Monitor model performance for datadrift and model degradation, often using automated monitoring tools. Optimization: Use database optimizations like approximate nearest neighbor ( ANN ) search algorithms to balance speed and accuracy in retrieval tasks.
Elements of a machine learning pipeline Some pipelines will provide high-level abstractions for these components through three elements: Transformer : an algorithm able to transform one dataset into another. Estimator : an algorithm trained on a dataset to produce a transformer. Data preprocessing. CSV, Parquet, etc.)
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