Machine Learning Operations (MLOps)
Automate Model Training, Deployment, and Retirement with MLOps
MLOps, short for Machine Learning Operations, represents a set of practices and methodologies that bridge the gap between data science and operations, enabling the integration and alignment of AI with business strategies.
Data science teams often focus on developing accurate and high-performing machine learning models. However, without proper integration into operational processes, these models may not effectively contribute to business objectives. MLOps addresses this challenge by establishing a framework that brings together data science and operations teams, fostering collaboration and ensuring that AI initiatives align with business strategies.
Avoid Human
Error
Manually deploying models is prone to human mistake. Prior to deployment, MLOps automates model testing.
Consistency and
Redundancy (Backup)
Thorough testing ensures that a new model will not fail or perform poorly once it is put into production. If this happens, MLOps makes it easy and quick to recall the new model and re-deploy the old until the assessment is finished.
Effective and at
Less Cost
Unless you automate the entire ML process, you’ll have to train and deploy your model manually every time you wish to update it. This simply entails going through the MLOps procedure over and again. This greatly raises the cost, which is precisely what MLOps was created to avoid.