.png){kind=logo}
MLOps (Machine Learning Operations) is receiving huge traction lately as more and more organizations are utilizing AI-driven systems. MLOps fills the gap between data science, IT, and operations by taking care of the workflow of machine learning models for development, deployment, and management in an efficient, scalable, and reliable production manner. While some proprietary platforms have been offered in the market, open-source MLOps tools have also risen as cost-effective and flexible solutions for businesses and developers.
This article delves into some of the best open-source MLOps tools for 2024, offering their features and capabilities and, where relevant, empowering organizations to get hold of efficient AI/ML workflows.
Kubeflow makes all machine-learning operations simple, portable, and scalable on Kubernetes. Kubeflow is a cloud-native framework that lets you create machine learning pipelines, train the model, and then deploy that model into production.
Kubeflow could be supported on cloud services such as AWS, GCP, and Azure; self-hosted services are supported, too. Machine learning engineers can integrate all sorts of AI frameworks into it for training, fine-tuning, scheduling, and deploying the models.
Furthermore, it provided a single look of the dashboard to monitor and manage the pipelines and allowed editing code via Jupyter Notebook, experiment tracking, model registry, and artifact storage.
The mlflow is generally used for experiment tracking and logging. However, over time it has evolved into an end-to-end MLOps tool for all kinds of machine learning models including LLMs.
The MLFlow has six core components:
a. Tracking: Version and store parameters, code, metrics, and output files. It also comes with interactive metric and parametric visualizations.
b. Projects: Package data science source code for reusability and reproducibility.
c. Models: Store machine learning models along with metadata in a standard format that could be later used by the downstream tools. It also provides options for model serving and deployment.
d. Model Registry: Model registry is a centralized model store that manages the life cycle of MLflow Models. It provides versioning, model lineage, model aliasing, model tagging, and annotations.
e. Recipes (Pipelines): Pipelines that allow you to train high-quality models fast and deploy them to production.
f. LLMs: Support evaluation, prompt engineering, tracking, and deployment of LLMs.
You can manage each component in your machine learning ecosystem with CLI, Python, R, Java, and REST API.
The metaflow lets data scientists and machine learning engineers create and manage any type of machine learning or AI project quickly.
It originated from Netflix to increase the efficiency of data scientists and is now open-sourced so others can use it too.
Metaflow provides a unified API for data management, versioning, orchestration mode training and deployment, and computing. It aligns well with major cloud providers and machine learning frameworks.
It is another popular open-source MLOps tool, that enables packaging, training, deployment, and monitoring of thousands of machine learning models in production.
a. Deploy models locally with Docker or to a Kubernetes cluster.
b. Tracks model and system metrics.
c. Deploy drift and outlier detectors alongside models.
d. Supports most machine learning frameworks such as TensorFlow, PyTorch, Scikit-Learn, and ONNX.
e. Data-centric MLOPs approach.
f. Workflow management and other tasks of inference and debugging are done via the command line interface.
g. Transparency in deploying a lot of models will save costs.
Seldon Core transforms your machine learning models into REST/GRPC microservices.
MLRun framework provides an easy way to build and manage machine learning applications in production. It streamlines the ingestion of production data, machine learning pipelines, and online applications in one place, greatly reducing engineering efforts, time for production, and computation resources.
a. Project Management: A central hub that manages different project artifacts, including data, functions, jobs, workflows, secrets, and more.
b. Data and Artifacts: Provides integration of different data sources, metadata management, cataloging, and versioning artifacts.
c. Feature Store: Stores, prepares, catalogs, and serves model features for training and deployment.
d. Batch Runs and Workflows: Run one or more functions, and collect, track, and compare all their results and artifacts.
e. Real-Time Serving Pipeline: Fast deployment of scalable data and machine learning pipelines.
f. Real-Time Monitoring: It monitors data, models, resources, and production components.
In today's world, with organizations increasingly using AI-driven systems, MLOps solutions have become leaner, faster, and more scalable. MLOps bridges the gap between data science, IT, and operations by helping to develop, deploy, and manage machine learning models reliably and effectively. Though open-source tools stand nowhere compared to efficient proprietary platforms, they turn out to be time-and-money-saving, something that finds a reasonably effective way for most organizations.
Kubeflow is the most mature Kubernetes-native platform for scaling machine learning operations without a hitch while integrating with cloud services and providing a set of management and monitoring features. In its current form, MLflow has become a versatile experiment tracking, model versioning, and model deployment tool that supports a wide variety of machine learning models, including large language models. Metaflow came from Netflix to make data scientists more productive; unifying an API and cloud compatibility would make handling machine learning projects easier.
Seldon Core V2 handles the packaging, deployment, and monitoring of several models, integrates machine learning frameworks, and provides cost-effective deployment. MLRun provides support for developing and operating machine learning applications by allowing them to be managed in one place, including project management, handling data, and real-time serving.
These tools represent the capability and possibility of open-source MLOps solutions in 2024, each solving different aspects of the machine learning lifecycle. With these tools driving them, organizations can realize better AI/ML workflows, speed up innovation, and stay competitive in this fast-evolving machine-learning landscape.
1. What is MLOps and why is it important?
A: MLOps, or Machine Learning Operations, is a set of practices and tools designed to streamline the development, deployment, and management of machine learning models. It integrates data science, IT, and operations to ensure that machine learning models are efficiently delivered and maintained in production environments. MLOps is important because it helps automate workflows, improve model reliability, and scale machine learning solutions.
2. What are some top open-source MLOps tools for 2024?
A: Some of the top open-source MLOps tools for 2024 include Kubeflow, MLflow, Metaflow, Seldon Core V2, and MLRun. These tools offer comprehensive solutions for managing various aspects of the machine learning lifecycle, from experimentation to deployment.
3. How does Kubeflow benefit machine learning operations?
A: Kubeflow provides a Kubernetes-native platform that simplifies and scales machine learning workflows. It supports creating, training, and deploying machine learning models in a cloud-native environment. Kubeflow’s integration with Kubernetes allows for efficient orchestration, monitoring, and management of ML pipelines.
4. What are the key features of MLflow?
A: MLflow features include experiment tracking, model packaging, model registry, and pipeline management. It supports the versioning of parameters, code, and metrics, and integrates with various machine-learning frameworks. MLflow also offers tools for model serving and deployment.
5. How does Metaflow improve the efficiency of data science projects?
A: Metaflow provides a unified API for managing data, versioning, and orchestrating machine learning workflows. It simplifies the creation and management of ML projects by offering an intuitive, Python-native interface and seamless integration with cloud providers.
