A technique for creating policies, norms, and best practices for machine learning models is known as “machine learning operations” or “MLOps.” MLOps aims to guarantee the whole lifecycle of ML development — from conception to deployment — is meticulously documented and managed for the best results instead of investing a lot of time and resources in it without a strategy.
MLOps aims to codify best practices to improve the quality and security of ML models while making machine learning development more scalable for ML operators and developers.
MLOps provides developers, data scientists, and operations teams with a framework for cooperating and, as a result, producing the most potent ML models. Some refer to MLOps as “DevOps for machine learning” since it successfully applies DevOps methods to a more specialized field of technological development. Because MLOps and DevOps are both centered on knowledge sharing, cooperation, and best practices across teams and technologies, this perspective on MLOps is beneficial.
The Function of MLOps Tools
MLOps tools may perform a wide range of duties for an ML team. However, they are often split into platform administration and individual component management. While some MLOps products focus only on a single core function, such as data or metadata management, other tools adopt a more all-encompassing strategy and provide an MLOps platform to control several aspects of the ML lifecycle.
Look for MLOps solutions that assist your team in managing these ML development areas, whether you’re looking for a specialist or a more comprehensive tool:
- Managing data
- Design and modeling
- ML model deployment and continuous upkeep
- Lifecycle management from beginning to end, which is often offered by full-service MLOps platforms
- Management of projects and workplace
Top MLOps Tools/ Platforms
Although there are numerous reasons why Amazon SageMaker is one of the top MLOps platforms, teams benefit most from its emphasis on monitoring and drift management. Teams get warnings from the platform about models, algorithms, and data sets that require adjustment over time. Real-time model and idea drift tracking, prediction accuracy monitoring, and bias alerts are a few critical areas of attention for Amazon SageMaker.
The Domino Data Science Platform from Domino Data Lab is a well-liked platform for data management teams, mainly because it emphasizes the creation of centralized storage and visualization areas for MLOps data. Because Domino’s platform provides many learning and template resources, like their Knowledge Center and Workbench, is a solid option for teams looking to lean towards data democratization.
One of the numerous premier machine learning and artificial intelligence tool solutions offered by H2O is H2O MLOps. Due to the adaptability of the platform’s testing and deployment settings, many MLOps teams use this solution. Teams may build a variety of settings for production, testing, and development. Additionally, the platform is adaptable enough to deal with on-premises, cloud, and container infrastructures.
Machine Learning and Shared Data Experience are two subcategories of the technology known as Cloudera Data Platform (SDX). Although the Machine Learning module provides several essential MLOps functionalities, the SDX solution is what makes Cloudera stand out. Increased visibility and guided management for data security, compliance, and other data governance requirements are provided by SDX to users. SDX enables businesses to maintain compliance and safety while developing ML models, mainly when several team members work with fresh and sensitive data.
A complete open source MLOps solution, Kubeflow facilitates the deployment and orchestration of machine learning workflows. For several stages of machine learning, including training, pipeline development, and maintenance of Jupyter notebooks, Kubeflow offers specialized services and integration.
It performs TensorFlow training tasks efficiently and interfaces with several frameworks, including Istio.
MLFlow is an open-source platform that offers several components for tracking experiments, project packaging, model deployment, and registry. TensorFlow and Pytorch are just two machine learning libraries that MLFlow interfaces use to make it easier to train, deploy, and manage machine learning applications.
The open-source MLOps platform Metaflow was created by Netflix. It is a program designed in Python and R that makes managing and building business Data Science projects simple.
Metaflow unifies Python-based Machine Learning, Deep Learning, and Big Data frameworks to train, deploy, and manage ML models.
Another open-source MLOps tool for managing and automating Kubernetes-native Machine Learning operations is called Flyte. Keeping track of model modifications, versioning it, and containerizing the model together with its dependencies guarantees that machine learning model execution is repeatable.
Flyte was created to handle sophisticated machine learning operations in Python, Java, and Scala.
To deploy ML models into employment logically and simply, ZenML incorporates ML tools like Jupyter notebooks into its flexible open source MLOps platform. ZenML is used to build reproducible machine learning pipelines to develop machine learning projects.
An open-source MLOps framework called MLRun makes it easy to manage your machine learning pipeline from the design stage to deployment in the field. Your machine learning pipeline now includes MLRun, which adds tracking, automation, quick deployment, administration, and simple model scaling.
All phases of the ML lifecycle are managed by algorithms inside operational procedures. The platform utilizes current SDLC and CI/CD techniques, automates ML deployment, offers the most tooling flexibility, improves communication between operations and development, and has cutting-edge security and governance capabilities. It produces models in a timely, secure, and economical manner.
Dataiku democratizes data access and empowers businesses to choose their own human-centered AI path. It enables you to develop, distribute, and reuse apps that expand and automate decision-making through data and machine learning. The platform offers a gathering place for data professionals and explorers, a library of best practices, quick ways to implement and manage machine learning and artificial intelligence, and a centralized, regulated setting.
The top end-to-end corporate AI platform, DataRobot, automates and speeds up each stage of your journey from data to value. To make the most of the investments in data science teams and manage risk and regulatory compliance, it serves as a central center for deploying, monitoring, managing, and governance machine learning models in production.
Scalability choices, experiment construction and tracking, and data lineage. Pachyderm is a powerful MLOps solution that enables users to manage a whole machine learning cycle. Due to its quick and precise tracking knowledge and replication skills, it is a straightforward option for data scientists and teams. It supports most languages, frameworks, and libraries, and as we showed in our comparison based on supported libraries, it aids in developing scalable ML/AI pipelines.
Databricks offers a platform for data analytics, machine learning, and artificial intelligence. Incorporating an open lake house design, Databricks Machine Learning enables ML teams to prepare and analyze data while accelerating cross-team communication and standardizing the whole ML lifecycle from exploration to production.
Metadata management and storage is the primary part of the MLOps lifecycle that Neptune.ai focuses on. With the help of this application, users may simply log, arrange, search, categorize, and save various types of information for their ML models. Neptune is a good option for teams who wish to concentrate on research, experimentation, and more complex builds needing deeper data insights because of its strategic focus on in-depth metadata knowledge.
Note: We tried our best to feature the best MLOps platforms and tools, but if we missed anything, then please feel free to reach out at Asif@marktechpost.com
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Prathamesh Ingle is a Mechanical Engineer and works as a Data Analyst. He is also an AI practitioner and certified Data Scientist with an interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real-life applications