Machine Learning is rapidly growing field. We get news of every day new development, new evolution in this field. After Developing ML models we deploy in the production. Deploying models into production efficiently and reliably is job of expert professionals.
Machine Learning Operations (MLOPs) provides CI/CD(Continuous Integration/Continuous Development) platforms that bridges the gap between data science and Information Technology operations.
MLOPs ensures easy integration, deployment, and monitoring of Machine Learning models.
As we step into 2025, several MLOps tools have emerged as important assets for machine learning engineers. This blog post explains 11 such frequently used MLOps tools that every Machine Learning Engineers or Data Science practitioners should know.
1. MLflow
It is an open-source platform designed to manage the complete machine learning lifecycle, including experimentation, reproducibility, and deployment. It offers four primary components: Tracking, Projects, Models, and Registry. These components facilitate logging experiments, packaging code, managing models, and deploying them across various platforms. MLflow’s flexibility allows integration with any Machine Learning library and supports multiple programming languages, making it a versatile choice for diverse ML projects.
To read more about MLFlow and Practicle explanation with help of python click here:
Mastering MLFlow: How to track, reproduce and deploy AI models
2. Kubeflow
Kubeflow is a Kubernetes-native platform tailored for deploying, monitoring, and managing ML models in various environments. It simplifies the orchestration of complex ML workflows, enabling scalable and portable deployments. With components such as Pipelines, Katib for hyperparameter tuning, and KFServing for model serving, Kubeflow provides a comprehensive suite for end-to-end machine learning (ML) lifecycle management.
To learn more about Kubeflow visit Official Website
3. Metaflow
Metaflow is developed by Netflix. Metaflow is a human-centric framework that simplifies the process of building and managing real-life data science projects. It provides a high-level API for defining and executing workflows. It is handling data versioning, and scaling computations to the cloud. Metaflow’s emphasis on user experience and productivity makes it a favorite among data scientists aiming for rapid prototyping and deployment.
Visit Offcial site of Metaflow
4. Comet ML
Comet ML is a meta machine learning platform that allows data scientists and ML engineers to track, compare, explain, and optimize experiments and models.
It offers real-time metrics, visualizations, and collaborative tools, enhancing transparency and reproducibility in ML projects. Comet ML’s integration capabilities with popular frameworks and tools make it a seamless addition to existing workflows.
5. AWS SageMaker
AWS SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly. It offers a broad array of built-in algorithms, support for custom models, and seamless integration with other AWS services. Features like SageMaker Studio and SageMaker Autopilot further enhance the development experience by providing an integrated development environment and automated model building capabilities, respectively.
6. Google Cloud Vertex AI
Vertex AI is Google’s unified ML platform that brings together the Google Cloud services for building ML under one roof. It simplifies the process of deploying and maintaining ML models, offering tools for data labeling, model training, evaluation, and prediction. Vertex AI’s integration with other Google Cloud services ensures scalability and efficiency in handling large-scale ML projects.
7. Azure Machine Learning
Azure Machine Learning is Microsoft’s cloud-based platform for building, training, and deploying ML models. It supports a wide range of open-source tools and frameworks, providing flexibility and control over the ML lifecycle. Features like automated ML, drag-and-drop designer, and MLOps capabilities make it a comprehensive solution for enterprises aiming to operationalize their ML workflows.
8. DataRobot
DataRobot is an enterprise AI platform that accelerates and democratizes data science by automating the end-to-end journey from data to value. It offers tools for data preparation, model building, deployment, and monitoring, all within a user-friendly interface. DataRobot’s emphasis on automation and ease of use makes it accessible to both technical and non-technical users, fostering collaboration across teams.
9. Neptune.ai
Neptune.ai is a metadata store for MLOps, focusing on experiment tracking and model registry. It allows teams to log, organize, and compare experiments, facilitating better collaboration and reproducibility. Neptune.ai’s lightweight integration with various ML frameworks and its intuitive UI make it a valuable tool for managing the iterative nature of ML development.
10. DVC (Data Version Control)
DVC is an open-source version control system for machine learning projects. It extends Git capabilities to handle large files, data sets, machine learning models, and metrics. By enabling data and model versioning, DVC ensures reproducibility and efficient collaboration among teams. Its compatibility with existing Git workflows makes it an easy addition to the ML development process.
Click Here to read more about DVC
11. Seldon Core
Seldon Core is an open-source platform that helps data science teams deploy, scale, and manage thousands of machine learning models on Kubernetes. It supports various ML frameworks and provides advanced features like A/B testing, canary deployments, and outlier detection. Seldon Core’s focus on model serving and monitoring makes it a critical component in the MLOps ecosystem.
Conclusion
As machine learning continues to permeate various industries, the importance of efficient, scalable, and reliable deployment of models cannot be overstated. The MLOps tools highlighted above offer diverse functionalities, catering to different stages of the ML lifecycle. By integrating these tools into their workflows, machine learning engineers can enhance collaboration, ensure reproducibility, and accelerate the journey from model development to production. Staying abreast of these tools and understanding their capabilities is essential for any ML professional aiming to thrive in the dynamic landscape of machine learning operations.
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