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Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.
In parallel to using data quality drift checks as a proxy for monitoring model degradation, the system also monitors feature attribution drift using the normalized discounted cumulative gain (NDCG) score. Pavel Maslov is a Senior DevOps and ML engineer in the Analytic Platforms team.
Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps.
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.
This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Some popular data quality monitoring and management MLOps tools available for data science and ML teams in 2023 Great Expectations Great Expectations is an open-source library for data quality validation and monitoring.
Data science – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation. MLOps engineering – Focuses on automating the DevOps pipelines for operationalizing the ML use case. Monitoring setup (model, datadrift). Deploy to production (inference endpoint).
These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally. All models built within DataRobot MLOps support ethical AI through configurable bias monitoring and are fully explainable and transparent.
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). He is passionate about Statistics, NLP and Model Explainability in AI/ML.
Data validation This step collects the transformed data as input and, through a series of tests and validators, ensures that it meets the criteria for the next component. It checks the data for quality issues and detects outliers and anomalies. Is it a black-box model, or can the decisions be explained?
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