10 Must-Know Python Libraries for MLOps in 2025

MLOps, or machine learning operations, is all about managing the end-to-end process of building, training, deploying, and maintaining machine learning models. As machine learning becomes a bigger part of real-world applications, having the right tools is becoming more important than ever. As 2025 is nearly half in the books, Python continues to be the most popular language for machine learning and MLOps alike.

In this article, we’ll explore 10 Python libraries that every machine learning professional should know in 2025. These libraries help data scientists and machine learning engineers work faster, avoid mistakes, and build more reliable systems.

1. MLflow

MLflow helps track and manage machine learning experiments and models. It makes it easy to compare results and share models with your team.

Key Features:

• Experiment Tracking: Track and compare multiple runs of your machine learning experiments.
• Model Packaging: Package code in a standard format using a MLproject file.
• Model Registry: A centralized store for managing lifecycle stages of models.

2. Data Version Control (DVC):

DVC lets you version control your data and machine learning models alongside your code. This helps keep everything organized and reproducible.

Key Features:

• Data Versioning: Keep track of different versions of datasets and models, just like you do with code.
• Pipeline Management: Create machine learning pipelines that are easy to repeat and update.
• Remote Storage Support: Store large files in the cloud or on external storage, while keeping them linked to your project.
• Git Integration: Works with Git so you can manage code and data together in one place.

3. Kubeflow

Kubeflow helps run and manage machine learning workflows on Kubernetes. It makes it easier to build, train, and deploy models at scale.

Key Features:

• Pipeline Orchestration: Create and manage machine learning workflows using Kubeflow Pipelines.
• Model Training: Support for distributed training using Kubernetes-native custom resources.
• Hyperparameter Tuning: Automated hyperparameter tuning engine which supports Grid search, Random search, etc

4. Apache Airflow

Apache Airflow lets you automate and schedule data and machine learning tasks using workflows. It also provides a dashboard to monitor and manage these workflows.

Key Features:

• DAGs (Directed Acyclic Graphs): Define workflows as Python code where each node is a task and edges represent dependencies.
• Scheduling: Set tasks to run at specific intervals using cron-like syntax or built-in presets.
• Monitoring & UI Dashboard: Airflow comes with a web-based UI to view DAGs and monitor task status.
• Extensibility: Pluggable architecture with operators and hooks for services like AWS and Google Cloud.

5. BentoML

BentoML helps package your machine learning models so you can serve them as APIs. It works with many popular machine learning libraries like TensorFlow and PyTorch.

Key Features:

• Model Serving: Serve models via REST API, gRPC, or batch inference with minimal setup.
• Multi-Framework Support: Compatible with TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM, and more.
• Model Packaging: package machine learning models from several frameworks into standardized, versioned containers.

6. FastAPI

FastAPI is a modern, high-performance web framework for building APIs with Python. It automatically creates interactive documentation, making it easy for others to understand your API.

Key Features:

• High Performance: Built on ASGI (Asynchronous Server Gateway Interface), FastAPI is comparable to Node.js and Go in terms of speed.
• API Documentation: FastAPI auto-generates interactive documentation using Swagger UI and ReDoc.
• Python Type Hints: Use standard Python type hints to define request and response schemas.
• Asynchronous Support: Built-in async and await support for asynchronous endpoints.

7. Prefect

Prefect helps you build and run data and ML pipelines with built-in error handling. It keeps your workflows running even when some tasks fail.

Key Features:

• Pythonic Workflow Design: Uses Python to define workflows with clear, modular, and reusable tasks.
• Dynamic Scheduling: Supports flexible scheduling with CRON, interval, or event-based triggers.
• Fault Tolerance & Retries: Automatically retries failed tasks with customizable retry policies and error handling.
• Observability and Logging: Provides real-time visibility into pipeline execution with detailed logs, alerts, and dashboards.

8. Great Expectations

Great Expectations checks that your data is clean and correct before using it in ML models. It creates reports to show which data checks passed or failed.

Key Features:

• Data Documentation: Generates human-readable HTML reports showing what checks were applied and which ones passed or failed.
• Validation Workflows and Checkpoints: Run data validations as part of your machine learning or ETL pipeline to keep things reliable.
• Integration with the Data Ecosystem: Works with Pandas, SQL databases, Spark, and tools like Airflow and Prefect.

9. Optuna

Optuna automatically finds the best settings for your machine learning models. It saves time by stopping poor tests early and showing helpful tuning charts.

Key Features:

• Pruning: Supports early stopping of underperforming trials to save computational resources.
• Automated Hyperparameter Optimization: Optuna automates the search for optimal hyperparameters, reducing manual tuning efforts.
• Visualization Tools: Provides built-in visualization for optimization history, parameter importance, and intermediate values to better understand the tuning process.

10. Seldon Core

Seldon Core helps you deploy machine learning models on Kubernetes so they can serve predictions in real-time. It also provides tools to monitor model performance in production.

Key Features:

• Kubernetes-Native Deployment: Seamlessly deploy machine learning models as microservices on Kubernetes clusters.
• Multi-Framework Support: Compatible with popular machibe learning frameworks including TensorFlow, PyTorch, XGBoost, Scikit-learn, and more.
• Monitoring and Logging: Integrates with Prometheus, Grafana, and other tools to provide real-time metrics, logging, and tracing.
• Advanced Inference Graphs: Build complex inference pipelines with multiple models, transformers, and routers.

Wrapping Up

In 2025, managing machine learning projects is easier with the right Python libraries. These tools help you track experiments, version your data, train models, and put them into production. Using libraries like MLflow, DVC, and Kubeflow can save you time and reduce errors. They also make your work more organized and easier to share with your team.

Whether you are just starting with MLOps or already have experience, these libraries will help you build better and faster machine learning systems. Try them out to improve your workflow and get better results.