Machine learning has become a core component of modern data science. Machine learning is already being applied to solve real problems in industries such as healthcare, finance, marketing, manufacturing, etc. The rise of big data and AI has led to an increase in the demand for machine learning professionals.
As the field continues to evolve, so too should the tools needed to manage machine learning projects. This means that new tools will continue to emerge over the next decade. Machine Learning Operations, MLOps, tools will allow organizations to automate their machine learning processes, improve efficiency, and reduce costs.
What is MLOps?
The term MLOps refers to the combination of machine learning and operations. MLOps is an approach to managing machine learning projects. It can be thought of as a discipline that encompasses all the tasks related to creating and maintaining production-ready machine learning models. MLOps bridges the gap between data scientists and operation teams and helps to ensure that models are reliable and can be easily deployed. Cognilytica predicts that the MLOps market is expected to reach nearly $4 billion by 2025.
The goal of Machine Learning Operations (MLOps) is to manage and orchestrate the end-to-end machine learning lifecycle.
The main objectives of MLOps are to ensure that models are always accessible, reproducible, and scalable.
Additionally, MLOps aims to automate the deployment and monitoring of machine learning pipelines, as well as optimize the overall model development process.
How is MLOps different from DevOps?
DevOps is a term that came about a few years ago as a way to describe the collaboration between developers and operations staff in order to improve the flow of communication and cooperation between the two groups. MLOps, or machine learning operations, is a relatively new field that refers to the application of machine learning techniques within an organization's IT infrastructure. So, what is the difference between DevOps and MLOps?
The main difference between DevOps and MLOps is that DevOps focuses on collaboration between developers and operations staff, while MLOps focuses on the application of machine learning techniques within an organization's IT infrastructure. While both fields are concerned with improving communication and cooperation between different groups within an organization, MLOps is specifically focused on the use of machine learning.
So, what does that mean for organizations? MLOps can help organizations to automate tasks and improve efficiency by using machine learning algorithms to perform tasks such as monitoring systems and identifying issues. In short, MLOps can help organizations to make better use of their data and resources.
Why should you care about MLOps?
The main reason why you should care about MLOps is that it helps you to automate repetitive tasks, reduce operational costs, and increase the quality of models produced by your team.
Benefits of MLOps
According to Deloitte's study, with MLOps, organizations have been able to automate the preparation of data, train models, and evaluate them, while tracking model versions, monitoring model performance, and making models reusable.
Some of the business benefits of MLOps include increased efficiency, improved communications, enhanced user experience, higher quality, and accuracy of model predictions and outcomes, effective time utilization of data scientists i.e. more focus on creating new, better models rather than focussing on a routine deployment.
By automating repetitive tasks, MLOps allows you to focus on higher-value activities such as developing better models and scaling production systems.
10 MLOps tool to manage the ML Lifecycle
Several MLOps tools can help in managing the machine learning lifecycle. In this article, we will discuss the 10 popular MLOps tools.
MLflow is a platform to share, deploy and manage machine learning models in production on a variety of clouds platforms. It includes tools to track your experiments, package them as reproducible runs, and share and deploy them. MLflow also provides lightweight APIs that can be integrated with any existing machine learning libraries or applications. MLflow provides four components to help manage the ML workflow: MLflow Tracking, MLflow Projects, MLflow Models, and MLflow Registry.
MLflow's GitHub repository: 2.5k+ forks, 11.3k+ stars, 350+ contributors.
Kubeflow is an open-source platform for deploying and managing AI workloads on Kubernetes. It makes it easy to deploy machine learning (ML) models and data pipelines to Kubernetes clusters and to manage and monitor the lifecycle of your AI workloads. Kubeflow also includes a library of pre-built AI models and data pipelines, so you can get started quickly.
Kubeflow's GitHub repository: 1.9k+ forks, 11.2k+ stars, 240+ contributors.
Kedro is an open-source Python framework for creating reproducible and maintainable data science code. It uses concepts from software engineering best practices and applies them to machine learning code. Applied concepts include modularity, separating concerns, and versioning. It makes it easier to set up a data pipeline and makes machine learning projects more efficient.
Kedro's GitHub repository: 600+ forks, 6.5k+ stars, 130+ contributors.
Pachyderm is a Kubernetes-based platform for building, deploying, managing, and monitoring machine learning models. It includes a model registry, a model management system, and a CLI toolkit.
Pachyderm provides the data foundation that allows developers to automate and scale their machine learning lifecycle while ensuring reproducibility. It helps customers get their data science projects to market faster, reduces data processing and storage costs, and supports strict data governance regulations.
Pachyderm's GitHub repository: 500+ forks, 5.4k+ stars, 140+ contributors.
Metaflow is an open-source project that allows scientists and engineers to build and manage real-world data science projects. It was initially developed at Netflix to help data scientists with data management and model training. Metaflow provides an API for the underlying infrastructure stack that is required for running data science projects, from prototypes to production.
Metaflow's GitHub repository: 450+ forks, 5.3k+ stars, 50+ contributors.
Data Version Control (DVC)
Data version control (DVC) is an open-source tool for data science and machine learning projects. It's created to make machine learning models shareable and reproducible. It is designed to manage large files, data sets, and machine learning models as well as code. Some of the key features of DVC include a simple command-line Git-like experience; management and versioning of datasets and machine learning models; making projects reproducible and shareable, helping to answer questions about how a model was built.
DVC's GitHub repository: 900+ forks, 9.3k+ stars, 200+ contributors
ZenML is an open-source MLOps framework to create ML pipelines. It is cloud- and tool agnostic, integrates natively with the most popular ML tooling, and has interfaces/abstractions that are catered toward ML workflows. ZenML pipelines execute ML-specific workflow from sourcing data to splitting/preprocessing, training, all of the ways to evaluating results and serving.
ZenML's GitHub repository: 100+ forks, 1.7k+ stars, 20+ contributors.
MLRun is an open-source machine learning platform that provides an integrative approach to managing your machine-learning pipelines from early development to model deployment in production. MLRun provides a comprehensive abstraction layer that enables data science engineers and data analysts to define the feature and model.
MLRun's GitHub repository: 100+ forks, 550+ stars, 45+ contributors.
Seldon Core is an open-source framework, that enables data scientists to build, deploy, and manage machine learning models and experiments at scale on Kubernetes. It integrates with Kubeflow and RedHat's OpenShift and supports toolkits such as TensorFlow, scikit-learn, Spark, R, Java, and H2O. Seldon core converts ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices.
Seldon Core's GitHub repository: 630+ forks, 2.9k+ stars, 135+ contributors.
Algorithmia is an enterprise MLOps platform. it automates the ML lifecycle within existing operations processes. Algorithmia automates ML deployment, optimizes collaboration between operations and development, integrates with best-of-breed tools, and provides advanced security and governance. Algorithmia has been used by over 130,000 engineers and developers, including large and mid-size enterprises, Fortune 500 companies and non-government organizations, and government intelligence services.
Algorithmia, Python's GitHub repository: 40+ forks, 140 stars, 15+ contributors.
MLOps can help improve machine learning processes. By using MLOps, one can speed up the deployment process, make data collection and debugging easier, and improve collaboration between data scientists and developers. In this article, 10 popular MLOps tools were discussed, with most of them open-source, you can choose the one that best suits your needs.
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