Summary
MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.
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It is a collaborative function, often comprising data scientists, devops engineers, and IT
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, and is based on the use of a data warehouse, data warehouse, and data warehouse.
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MLOps is becoming increasingly important for companies looking to streamline their ML operations, as it helps them to align models with business needs and regulatory requirements.
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It also advocates for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management.
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According to
Summary
With Machine Learning Model Operationalization Management (MLOps), we want to provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.
ML Ops: Machine Learning Operations
ml-ops.org
Summary
MLOps or ML Ops is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous development practice of DevOps in the software field
MLOps - Wikipedia
wikipedia.org
Summary
MLOps stands for Machine Learning Operations. MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them. MLOps is a collaborative function, often comprising data scientists, devops engineers, and IT.
What is MLOps?
databricks.com
Summary
MLOps is a set of practices for collaboration and communication between data scientists and operations professionals, which is a similar concept to DevOps but more experimental in nature. It is a hybrid team composition, data collection, analysis, transformation/preparation, model training and development, model validation, model serving, model monitoring, and re-training, and is based on the use of a data warehouse, data warehouse, and data warehouse. MLOps is becoming increasingly important for companies looking to streamline their ML operations, as it helps them to align models with business needs and regulatory requirements.
MLOps: What It Is, Why It Matters, and How to Implement It - neptune.ai
neptune.ai
What is MLOps? Machine learning operations are best practices to run AI successfully. Learn about DLOps, DataOps, AIOps and ModelOps, too.
What is MLOps? | NVIDIA Blog
nvidia.com
DataRobot MLOps is helping to increase AI value by automating the deployment, optimization, and governance of machine learning applications.
MLOps | DataRobot
datarobot.com
