Top 20 MLOps Tools to Learn in 2025 | K21Academy

As machine learning (ML) and artificial intelligence (AI) technologies continue to rise, IT industries are embracing these innovations to maintain a competitive edge. MLOps (Machine Learning Operations) has become essential in this evolution, helping businesses optimize the management of the ML lifecycle. By leveraging top MLOps tools, companies can efficiently build, deploy, and manage scalable machine learning models, improving operational efficiency and staying ahead of the competition.

In this post, we are going to learn about the best MLOps tools for model development, deployment, and monitoring to standardize, simplify, and streamline the machine learning ecosystem.

What is MLOps?

Top MLOps Tools in 2025

MLOps tools can be categorized into following domains:

• Large Language Model (LLM) Framework
• Experiment Tracking and Model Metadata Management Tools
• Orchestration and Workflow Pipelines MLOps Tools
• Data and Pipeline Versioning Tools
• Feature Stores
• Model Testing
• Model Deployment and Serving Tools
• Model Monitoring in Production ML Ops Tools
• End-to-End MLOps Platforms

Related Readings: Machine Learning Algorithms & Use Cases

Let’s look at each of them in detail.

Large Language Model (LLM) Framework

With the launch of GPT-4 and the upcoming GPT-4o, the competition is on to develop large language models and unlock the full capabilities of modern AI. To build intelligent AI applications, LLMs need vector databases and integration frameworks. MLOps tools in this category are:

Qdrant is an open-source vector similarity search engine and database that offers a production-ready service with an easy-to-use API, enabling you to store, search, and manage vector embeddings efficiently.

It offers several key features that make it a powerful tool for vector search.

• Its user-friendly API, available in Python and multiple other programming languages, allows for easy integration.
• The engine uses a custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search, ensuring fast and accurate results.
• It supports a wide range of data types and query conditions, including string matching, numerical ranges, and geo-locations, making it versatile for various use cases.
• Being cloud-native, Qdrant can scale horizontally, ensuring optimal resource usage regardless of the data size.
• Additionally, developed in Rust, it prioritizes both performance and resource efficiency, making it a robust choice for production environments.

2) LangChain

LangChain is an open-source framework that helps developers build applications using large language models (LLMs). It is available as both Python and JavaScript libraries and provides tools and APIs to make creating LLM-based applications, such as chatbots and virtual assistants, easier. It supports various use cases for LLMs and natural language processing (NLP), including chatbots, intelligent search, question-answering, summarization services, and virtual agents capable of automating tasks.

Related Readings: Understanding RAG with LangChain

Orchestration and Workflow Pipelines MLOps Tools

These MLOps tools help you create data science projects and manage machine learning workflows:

5) Prefect

Prefect is a modern data stack designed for monitoring, coordinating, and orchestrating workflows across applications. It’s an open-source, lightweight tool built specifically for end-to-end machine learning pipelines. You can use either Prefect Orion UI or Prefect Cloud to manage your workflows.

Prefect Orion UI is a locally hosted orchestration engine and API server, offering insights into your local Prefect Orion instance and its workflows. On the other hand, Prefect Cloud is a hosted service that allows you to visualize flows, track flow runs, and manage deployments, along with handling account settings, workspaces, and team collaboration.

6) Metaflow

Metaflow is a robust, battle-tested workflow management tool designed for data science and machine learning projects. Built with data scientists in mind, it lets them focus on model development without the need to worry about MLOps engineering.

Key features include:

• With Metaflow, you can design workflows, scale them, and deploy models into production.
• It automatically tracks and versions machine learning experiments and data, and you can visualize the results directly in the notebook.
• It is compatible with multiple cloud platforms (including AWS, GCP, and Azure) and integrates with various machine learning Python libraries (such as Scikit-learn and TensorFlow). Additionally, its API is also available for R.

7) Kedro

Kedro is a Python-based workflow orchestration tool designed to help create reproducible, manageable, and modular data science projects. By integrating software engineering principles such as modularity, separation of responsibilities, and versioning, Kedro brings structure to machine learning workflows.

Key features include:

• With Kedro, teams can set up dependencies and configurations, create, visualize, and execute pipelines, log and track experiments, and deploy on one or more machines.
• It also ensures that data science code is maintainable, encourages the development of modular and reusable code, and facilitates collaboration among team members on projects.

Data and Pipeline Versioning Tools

With these MLOps tools, you can manage tasks around data and pipeline versioning:

8) Pachyderm

Pachyderm is a popular MLOps tool widely used across various industries to optimize data processing, manage ML lifecycles, and streamline MLOps workflows. It provides an efficient software platform designed to integrate seamlessly with multiple cloud providers.

Key features of Pachyderm include:

• robust data lineage and automatic data versioning, which helps track and manage the evolution of datasets throughout the ML pipeline.
• The platform can be deployed both on cloud and on-premise environments, offering flexibility based on organizational needs.
• Additionally, Pachyderm is built for easy integration with various cloud providers, making it a versatile solution for teams working in diverse cloud ecosystems.

9) Data Version Control (DVC)

Data Version Control (DVC) is a widely-used open-source tool designed for machine learning projects. It integrates smoothly with Git to provide versioning for code, data, models, metadata, and pipelines.

However, DVC is more than just a tool for tracking and versioning data. It offers a range of features, including

• experiment tracking (for model metrics, parameters, and versioning), the ability to create, visualize, and run machine learning pipelines, and workflows for deployment and collaboration.
• It also supports reproducibility, data and model registries, and continuous integration and deployment (CI/CD) for machine learning through its integration with CML.

10) LakeFS

LakeFS is an open-source, scalable data version control tool that offers a Git-like interface for managing object storage, allowing users to treat their data lakes just like their code. With LakeFS, users can version control data at exabyte scale, making it an ideal solution for managing large data lakes.

Additional features include

• the ability to perform Git operations such as branching, committing, and merging across any storage service.
• It accelerates development through zero-copy branching, enabling seamless experimentation and collaboration.
• LakeFS also integrates pre-commit and merge hooks for CI/CD workflows, ensuring clean processes.
• Furthermore, its resilient platform allows for quick recovery from data issues with its revert capability.

Related Readings: How to create CI CD Pipeline Jenkins Step by Step Guide

Feature Stores

Feature stores are centralized repositories for storing, versioning, managing, and serving features (processed data attributes used for training machine learning models) for machine learning models in production as well as for training purposes.

11) Feast

Feast is an open-source feature store designed to help machine learning teams productionize real-time models and build a collaborative feature platform that bridges the gap between engineers and data scientists.

• It enables the management of an offline store, a low-latency online store, and a feature server, ensuring consistent feature availability for both training and serving.
• Feast also helps prevent data leakage by creating accurate point-in-time feature sets, relieving data scientists from the complexities of error-prone dataset joins.
• Additionally, it decouples machine learning from data infrastructure by providing a unified access layer.

12) Featureform

Featureform is a virtual feature store that empowers data scientists to define, manage, and serve features for their ML models. It helps data science teams improve collaboration, streamline experimentation, facilitate deployment, boost reliability, and maintain compliance.

Key features include

• enhanced collaboration by allowing teams to share, reuse, and better understand features.
• When a feature is ready for deployment, Featureform orchestrates the data infrastructure to prepare it for production.
• The system also ensures that features, labels, and training sets remain unmodified, enhancing reliability.
• With built-in role-based access control, audit logs, and dynamic serving rules, Featureform enforces compliance logic directly within the platform.

Model Testing

With these MLOps tools, you can test model quality and ensure machine learning models’ reliability, robustness, and accuracy:

13) SHAP

SHAP is a tool that explains the output of machine learning models using a game-theoretic approach. It calculates an importance value for each feature, reflecting its contribution to the model’s prediction. This approach enhances the transparency and interpretability of complex models, making their decision-making process easier to understand.

Key features include

• explainability through Shapley values, which use concepts from cooperative game theory to attribute each feature’s contribution to a model’s prediction.
• SHAP is model-agnostic, meaning it works with any machine learning model, offering a consistent method for interpreting predictions.
• Additionally, it provides various visualizations and plots to help users better understand the impact of different features on the model’s output.

14) DeepChecks

Deepchecks is an open-source solution designed to cover all your ML validation needs, ensuring that both your data and models are rigorously tested from research through to production. It provides a comprehensive approach to validating your data and models with its range of integrated components.

Model Deployment & Serving Tools

When it comes to deploying models, these MLOps tools can be very helpful:

15) Kubeflow

Kubeflow simplifies machine learning model deployment on Kubernetes by making it portable, scalable, and easy to manage. It supports the entire machine learning lifecycle, including data preparation, model training, optimization, prediction serving, and performance monitoring in production. Whether you’re deploying locally, on-premises, or in the cloud, Kubeflow streamlines the process, making Kubernetes more accessible for data science teams.

Key features include:

• centralized dashboard with an interactive UI, machine learning pipelines for reproducibility and efficiency, and native support for tools like JupyterLab, RStudio, and Visual Studio Code.
• It also offers hyperparameter tuning, neural architecture search, and supports training jobs for frameworks such as TensorFlow, PyTorch, PaddlePaddle, MXNet, and XGBoost.
• Kubeflow enables job scheduling, multi-user isolation for administrators, and compatibility with all major cloud providers.

16) Hugging Face Inference Endpoints

Hugging Face Inference Endpoints is a cloud-based service provided by Hugging Face, an all-in-one machine learning platform that allows users to train, host, and share models, datasets, and demos. These endpoints are designed to make it easy for users to deploy their trained machine learning models for inference, eliminating the need to manage the underlying infrastructure.

Key features include

• cost-effective pricing starting at $0.06 per CPU core per hour and $0.60 per GPU hour, depending on your requirements.
• The service is quick to deploy, fully managed, and auto-scaling, ensuring seamless performance.
• As part of the Hugging Face ecosystem, it offers enterprise-level security, making it a reliable choice for businesses and developers alike.

Related Readings: Hugging Face: Revolutionizing NLP and Beyond

Model Monitoring in Production MLOps Tools

Whether your ML model is in development, validation, or deployed to production, these tools can help you monitor a range of factors:

17) Prometheus

Prometheus is an open-source monitoring system designed to collect and store metrics, which are numerical representations of performance, from a variety of sources such as servers and applications. This MLOps tool operates on a pull-based model, meaning that metric sources periodically push data to Prometheus for collection.

Key features of Prometheus include

• federated monitoring, which allows for scaling by distributing metrics across multiple Prometheus servers.
• It also supports multi-dimensional data, enabling users to attach labels (key-value pairs) to metrics for more detailed analysis.
• Prometheus uses PromQL, a powerful query language, to filter, aggregate, and analyze time series data.
• Additionally, the system offers alerting functionality, triggering notifications based on predefined rules and conditions.

Related Readings: Install Prometheus and Grafana on Kubernetes using Helm

18) Amazon CloudWatch

Amazon CloudWatch is a cloud-based monitoring service provided by Amazon Web Services (AWS), designed to collect and track metrics, logs, and events from AWS resources.

Key features include

• AWS-centric monitoring with pre-configured integrations for seamless setup across various AWS services.
• CloudWatch allows users to set alarms that trigger when metrics exceed or fall below predefined thresholds.
• It also ingests, stores, and analyzes logs from AWS resources, helping you gain deeper insights into system performance.
• The service provides built-in dashboards for basic visualizations, though for more advanced visualizations, integration with Grafana is recommended.

End-to-End MLOps Platforms

If you’re looking for a comprehensive MLOps tool that can help during the entire process, here are some of the best:

19) AWS SageMaker

Amazon Web Services (AWS) SageMaker provides an all-in-one platform for the entire machine learning lifecycle, from training and experimentation to deployment, monitoring, and cost optimization.

• AWS SageMaker is a comprehensive MLOps solution that enables teams to efficiently train, deploy, and manage machine learning models.
• It offers a collaborative environment, making it easier for data science teams to work together on model development.
• With automated ML training workflows, you can accelerate the model development process, while also tracking and versioning experiments and ML artifacts.
• SageMaker seamlessly integrates with CI/CD pipelines to automate the integration and deployment of models, ensuring continuous delivery.

20) Dagshub

DagsHub is a collaborative platform designed for the machine learning community to track, version, and manage data, models, experiments, ML pipelines, and code. It offers a streamlined environment for teams to build, review, and share machine learning projects, making it the “GitHub for machine learning.”

Key features include:

• DagsHub provides a comprehensive set of tools to optimize the end-to-end machine learning workflow.
• Git and DVC repositories for managing ML projects, along with DagsHub logger and MLflow integration for experiment tracking.
• It also allows dataset annotation through a Label Studio instance, and supports diffing of Jupyter notebooks, code, datasets, and images for easy comparison.
• Users can comment directly on files, lines of code, or datasets, facilitating better collaboration. For project documentation, you can create reports similar to GitHub wikis.
• ML pipeline visualization, ensuring reproducible results, and running CI/CD pipelines for model training and deployment.

Conclusion

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