Tredence & Iguazio present "Automated Model Management for CPG Trade Effectiveness" May 4th 10am PDT
Machine learning operations (MLOps), also called Operations for ML, or AI Infrastructure and ML Operations, is considered to be the backend supporting ML applications in business. Machine learning operations management is responsible for provisioning development environments, deploying models, and managing them in production.
On this page, you will learn:
MLOps began as a way of improving communications between the data scientists who develop ML models and DevOps, the engineers who operate them in production. Soon, workflows and processes evolved into open-source MLOps solutions, like MLflow and Kubeflow.
Today, machine learning operations management is vital for companies to smoothly deploy and operate ML models at scale.
Developing a model, bringing it to deployment, and ensuring that it keeps working optimally is a long, involved process that requires a number of different teams. Machine learning IT operations supports every team so they can focus on their specialized tasks.
Here are some of the responsibilities of MLOps solutions:
Data scientists need to concentrate on model development, but they also need a suitable development environment.
MLOps takes care of infrastructure provisioning so that data scientists can do their job.
Data scientists develop, train, and test ML models.
An MLOps platform helps deploy and scale them correctly.
Engineers are responsible for models in a production environment, but they use different tools and processes from data scientists. Often, they struggle to understand the model given to them by the data science team, while the data science team isn’t sure how to explain it.
MLOps bridges the gap to explain models to the engineers.
Once the models are in use in a production environment,
MLOps track metrics and parameters to monitor model accuracy and performance.
Data science teams need to continue improving model inference once it’s in production, but without affecting performance.
MLOps establish an automated machine learning pipeline with critical feedback loops for the DS team.
Every enterprise that wants to take advantage of ML predictions needs MLOps. It’s crucial for every vertical, including telecommunications, healthcare, education, financial services, retail, manufacturing, entertainment, and more.
Data scientists, engineers, and IT operations teams all rely on an MLOps platform.
Machine learning orchestration delivers value for a number of business use cases:
By supporting more robust ML lifecycle management, machine learning orchestration enables data scientists, analysts, and engineers to innovate faster and deliver accurate, advanced ML models more swiftly and easily.
Every organization department, from R&D to marketing to customer support, wants ML predictions in order to better understand opportunities and challenges. This places more strain on the ML infrastructure. Machine learning IT operations shoulders that strain to ensure that the production environment doesn’t collapse and that the enterprise can grow and expand.
MLOps help enterprises to meet governance requirements by tracking version history and model origin, and enforces security and data privacy compliance policies, so auditing is quick and painless. By enhancing model transparency and fairness, data science teams can identify the most important features and create even better models with minimal bias.
MLOps keeps the enterprise’s ML framework operating smoothly and reliably to power the predictions that stakeholders need to drive faster, better decision-making in critical use cases across every department of the business.