MLOps aims to make machine learning workflows more flexible, scalable, and manageable. The tools highlighted in this article are some of the best managed tools available in the MLOps market to help achieve this in your workflow.
MLOps, machine learning operations, is an ML model development methodology that follows DevOps and agile development practices. MLOps introduces DevOps practices such as automation, continuous integration, and continuous deployment to machine learning workflows. This creates an operational pipeline for tracking, monitoring, and deploying machine learning models into production.
The MLOps methodology is quickly gaining adoption, and different companies and open-source platforms have introduced tools to enable the practice in ML workflows. This article focuses on some of the best MLOps tools and platforms offered as managed software to help data scientists and machine learning engineers concentrate on their core task, model development.
Algoritmia is a production-level MLOps platform that provides fast and secure end-to-end management of all ML operational processes. It is an easily deployed platform that provides flexibility in tooling and enables seamless collaboration between ML and Ops engineers. It implements CI/CD best practices for ML models and has a version management feature that allows you to test multiple versions. It offers advanced security features and supports GPU, which makes eve suitable for compute-intensive use cases like deep learning.
Cnvrg.io is a technology-agnostic end-to-end platform for building, deploying, managing, and automating ML and data science workflows. It is a platform that utilizes cloud resources to make ML pipelines readily deployed on Kubernetes, thereby accelerate model development and delivery. It is designed to manage every data science and machine learning model stage, including research, data sourcing, information collection, code writing, and model optimization. It has a simple UI and can easily integrate with many MLOps tools, libraries, and frameworks.
Dataiku presents itself as "the platform democratizing access to data and enabling enterprises to build their own path to AI in a human-centric way." It allows you to deploy, monitor, and manage machine learning projects in production. IT integrates with standard DevOps tools like Jenkins and GitLabCI to provide an automated and fully operational CI/CD pipeline for machine learning projects. Some of its features include monitoring, drift detection, and automatic retraining and updating of ML models.
Dataiku also allows you to build, share, and reuse ML-based applications.
DataRobot is an enterprise MLOPS platform that automates and accelerates every process involved in data-driven software development. It provides a central UI where you can deploy, monitor, manages, and govern ML models while managing risk and ensuring regulatory compliance in production. It gives the ability to quickly build simple and complex machine learning algorithms for medium and large teams.
Iguazio offers an end-to-end ML pipeline that ingests and transform data from any source, train and evaluates models at scale, deploys to an operational pipeline, monitor, and detects data drift in real-time. It helps you automate and accelerate da science and ML model development workflow from development to deployment. These exciting features enable ML engineers to deliver better data science and machine learning solutions without worrying about infrastructure bottlenecks. It is easily deployable in seconds and integrates with popular ML frameworks and libraries to further extend its impact.
Pachyderm implements data-driven automation, petabyte scalability, and end-to-end reproducibility for your machine learning workflow. It allows you easily handle structured and unstructured data, meet regulatory compliance, enable reproducibility, and accelerate the development of machine learning models at scale. It offers various features that prompt the building and tracking of machine learning models.
Valohai is an enterprise-scale deep learning management platform that enables data scientists to manage their machine learning orchestration, version control, data pipelines, and deep learning infrastructure. It implements MOps principles such as reproducibility, version control, and reproducibility of the end-to-end lifecycle of ML models. Some of its features include parallel hypermeter sweeps, custom scripts, training sessions visualization, data exploration, Jupyter Notebook extension, deployment, and production monitoring. It enables teamwork and collaboration and supports GPUs for on-premise and cloud environments, making it suitable for running heavy deep learning workloads.
Polyaxon is a platform that provides MLOps tools that allows you to automate, reproduce, and scale production-grade data science and machine learning workflows. It enables you to build, validate, deliver, and monitor ML models from a central, easy-to-use interface. It integrates with various ML frameworks like TensorFlow, allowing you to build modern applications that can run on the cloud or on-premise infrastructures and leverages modern technologies like Docker and Kubernetes.