Training machine learning model for production use is a hectic and time-consuming process. With MLOps, this narrative is changing. MLOps, a descendent of DevOps, provides the automation and scalability required to develop, train, and continuously deliver modern agile machine learning applications.
Machine learning lifecycle management tools are important to implement DevOps practices in your machine learning environment. Using popular, efficient open-source tools such as those mentioned in this article is an excellent start to your machine learning MLOps journey.
Suppose you want to maximize the business potential of your production machine learning models and create new products. In that case, it’s important to equip your technical teams with the best tools and practices for an efficient and agile machine learning workflow.
Data scientists, machine learning experts, and developers are supposed to be spending time and resources researching, building, and innovating intuitive, modern business solutions. One of the concepts that bring about this result in machine learning is MLOps.
MLOps, meaning machine learning operations, is basically DevOps for machine learning models. By applying DevOps principles such as versioning, automation, and monitoring, MLOps speeds up and manages the process of developing and delivering machine learning models for client use.
MLOps offers many benefits, including faster time to market, consistency, and lower failure rate through continuous integration and delivery, monitoring, and testing of ML models. MLOps helps experts concentrate on their field of specialization to drive business benefits, rather than spending a long time building a single solution due to ineffective production practices.
Various tools, both managed and open source, are available to actualize all these benefits in your real-time machine learning workflows. To consider an MLOps solution viable, it should look at the minimum offer Model tracking, versioning, and deployment.
Some of the most popular open-source MLOps tools are listed in this article, offering solutions to specific aspects. Some provide end-to-end solutions throughout the machine learning pipeline.
Built by Databricks, MLflow is a popular open-source MLOps platform for managing the machine learning lifecycle. It is designed with four components; MLflow Tracking, Projects, Models, and Model Registry, which all help manage ML lifecycle from experimentation, reproducibility to deployment.
MLflow is a library-agnostic platform that works with various machine learning libraries and programming languages.
MLflow Tracking, machine learning experiment parameters, attributes, code, and datasets can all be tracked, queried, and logged quickly.
MLflow Models component also has a Big Data capability that packages machine learning models that can be used with Apache Spark tools.
Facebook, Microsft, Wix, Databricks, and Toyota are a few organizations using and contributing to the MLflow open-source platform.
MLflow is generally a well-structured MLops solution that easily tracks, package, deploy, and manage the end-to-end lifecycle of machine learning models in a diverse environment.
Described as a framework for real-life data science, Metaflow is a code-based MLOps platform that supports Python and R programming languages for managing data science projects. Netflix originally developed the open-source MLOps platform to assist data scientists in addressing data management needs. AWS also contributed largely to the advancement of Metaflow by providing built-in storage, compute, and machine learning integration with AWS cloud.
Metaflow is focused on the production pipeline and is designed to deploy and run at scale. It integrates with Amazon Sagemaker, Big Data platforms, and Python-based ML and deep learning libraries to train, deploy and manage machine learning models.
The platform has over 4k stars and more than 30 contributors on GitHub, making regular updates to the tool.
Kubeflow is a full-featured MLOps platform that manages the deployment of machine learning workflows on Kubernetes. It offers a simple, scalable, and portable solution for running machine learning pipelines on Kubernetes.
Kubeflow started as a platform for running TensorFlow tasks via Kuberentes but has since evolved into a full-fledged data pipeline experimentation platform that operates on multiple platforms.
Kubeflow has an impressive 10k plus stars and over 200 contributors on GitHub, making it one of the most popular open-source MLOPs platforms.
The platform offers various services, including Notebooks, TensorFlow model training, and Pipeline for creating and managing Jupyter notebooks, custom TensorFlow ML model training, and end-to-end pipeline deployment and management, respectively.
Flyte is a scalable open source MLOps platform for maintaining machine learning and data processing workflows. It tracks different versions for changes in the model and containerizes it alongside its dependencies to make every step of the execution reproducible.
Flyte is written in GO programming language and is designed o scale across multiple ML workflows written in Python, Java, and Scala.
Spotify, Lyft, and Freenome are some of the companies that use Flyte to handle thousands of production ML model training by leveraging the scalability and reliability it provides.
Iguazio is an enterprise-level MLOps platform tats automates the entire machine learning and data science workflow. The platform offers both managed and free open source versions of its feature-rich MLOps solution. It is used by dominant tech organizations such as Samsung, AWS, Microsoft, and Intel.
Iguazio is focused on accelerating the development, deployment, and management of your machine learning applications by the end-to-end automation of your ML pipelines with MLOps.
It makes the data science operational pipeline easy by enabling automation right from ingesting data from its source to training, deploying, and monitoring the machine learning model.
Cnvrg.io is a technology-agnostic ML platform for building and deploying machine learning models at scale. It allows management of end-to-end data science workflow in a single, simple and intuitive interface.
Cnvrg accelerates the building of machine learning pipelines that are readily deployed in Kubernetes by leveraging available cloud resources. It offers a managed and free open source community version of the platform, which helps data scientists make the most ut their time and resources.
NVIDIA, NetApp, Lightricks, and LogMeIn are some of the companies that use the Cnvrg.io data science platform.
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