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MLOps platforms are a category of tools, services, and infrastructural architectures designed to streamline the development, deployment, and management of machine learning (ML) models in production environments, based on MLOps principles. MLOps, short for Machine Learning Operations, is inspired by the DevOps principles—automation, collaboration, and continuous delivery—into the machine learning lifecycle, allowing data scientists, data engineers, machine learning engineers, and DevOps teams to work together seamlessly.
MLOps platforms generally provide a collaborative environment where those stakeholders can work together efficiently, as well as facilitate the management, maintenance and sharing of features, models., ML assets and data across teams through the integration of tools like feature stores, orchestrations tools, compute management, experiment tracking, model deployment and model registries.
Model Training and Experimentation: Tools and frameworks to run and track machine learning experiments, manage hyperparameters, and compare model performance across multiple versions.
Model Versioning: Provides automated versioning of datasets and models to maintain a clear history of model evolution.
Model Deployment: Simplifies the deployment of models in production, whether as batch processes or real-time APIs, while ensuring high availability and scalability.
An MLOps platform generally supports continuous integration of new data or model changes and facilitates seamless continuous deployment (CI/CD) in production, ensuring that models are quickly updated when new data or improvements are available.
Model monitoring: An MLOps platform tracks the performance of deployed models, monitors for model degradation, and can alert teams or trigger automated retraining when models become stale due to data shift or other factors which improves feedback loops.
Feature monitoring: An ideal MLOps platform should be able to continuously monitor the input general data and its distribution. For the features used in deployed models in order to either improve model performances or alert stakeholders.
Modern MLOps platforms often provide tools or frameworks for feature management such as a feature store and feature engineering capabilities. Features are the input used by models to make predictions, and their quality directly affects the model's performance. A feature store acts as a single source of truth for all features used in ML models, ensuring consistency and reusability. By centralizing the storage and cataloging of features, it helps teams avoid redundant work, allowing data scientists and engineers to access and reuse pre-engineered features across different projects and teams.
Automates various stages of the ML pipeline, from data preprocessing and feature engineering to training, deployment, and monitoring. In an operational or production setup, workflow orchestration for large-scale ML pipelines become necessary to ensure reproducibility and efficient resource management.
An MLOps platform aims to offer numerous benefits to organizations looking to operationalize machine learning workflows and scale AI efforts efficiently. Here are the potential key advantages:
MLOps platforms bridge the gap between data scientists, data engineers, ML engineers, IT/DevOps teams, and often stakeholders; allowing them to cross-collaborate more effectively. MLOps platforms offer a unified environment aimed at removing the friction caused by different teams working with disjointed tools, leading to more cohesive development, maintenance and deployment processes
Some MLOps platforms do enable automatic logging of all ML experiments, including parameters, data versions, and outcomes, although in recent years this has been generally done on dedicated toolsets and platforms. This allows data scientists to easily compare results and share insights with teammates. For engineers and operations teams, this means better visibility into how models were trained, which improves reproducibility when moving models to production.
Data and compute management allows for better allocation of resources and usually provides more fine-grained monitoring and management of the infrastructures used in production. Emerging toolsets, platforms and abstractions such as feature platform, stores, lakehouses and AI lakehouses also aim to facilitate automation and efficiency and are often considered part of or categories of MLOps platforms.
MLOps platforms typically include capabilities to optimize the use of compute resources by dynamically scaling up or down infrastructures, reducing unnecessary computation during low-demand periods, and avoiding costly manual management of resources. By also providing automation and standardization capabilities, MLOps platforms usually contribute to faster deployment of models, allowing businesses to gain a competitive edge and faster time-to-market.
Hopsworks: An end to end MLOps platform based on a state of the art real-time AI lakehouse architecture. Integrated in the platform are feature store, model registry, deployment, compute and orchestration, and capabilities for managing AI pipelines.
Databricks: Combines data engineering and ML tools, providing integration between ML development and production environments.
Kubeflow: An open-source MLOps platform for Kubernetes, designed to make deploying and managing ML workflows on Kubernetes simple, portable, and scalable.
AWS SageMaker: A fully managed MLOps platform that provides necessary tools for model training, deployment, and monitoring.