ML Ops
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What is ML Ops?
MLOps (Machine Learning Operations) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. It combines machine learning model development (Dev) and machine learning model deployment (Ops) to standardize and automate the ML lifecycle. MLOps helps businesses streamline the end-to-end process from model building to deployment, monitoring, and governance.
What other technologies are related to ML Ops?
ML Ops Complementary Technologies
L
LLMs
Large Language Models benefit from MLOps practices for deployment, monitoring, and management.
Docker
Docker is a containerization platform used to package ML models for deployment in MLOps pipelines.
d
deep learning
Deep learning models require robust MLOps pipelines for training, validation, and deployment.
Number of organizations that mention technology
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Which job functions commonly mention ML Ops?
AI Engineer
0.2% of all AI Engineer jobs mention ML Ops
View 419
jobs on Sumble
Machine Learning
0.1% of all Machine Learning jobs mention ML Ops
View 198
jobs on Sumble
Data Scientist
0.1% of all Data Scientist jobs mention ML Ops
View 351
jobs on Sumble
Data, Analytics & Machine Learning
0.1% of all Data, Analytics & Machine Learning jobs mention ML Ops
View 1.0k
jobs on Sumble
Engineering & R&D
1611 Engineering & R&D jobs mention ML Ops
View 1.6k
jobs on Sumble
Which organizations are mentioning ML Ops?
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