Learn about 10 essential Python libraries that support core MLOps tasks like versioning, deployment, and monitoring.

While machine learning continues to find applications across domains, the operational complexity of deploying, monitoring, and maintaining models continues to grow. And the difference between successful and struggling ML teams often comes down to tooling.

In this article, we go over essential Python libraries that address the core challenges of MLOps: experiment tracking, data versioning, pipeline orchestration, model serving, and production monitoring. Let’s get started!

1. MLflow: Experiment Tracking and Model Management

 
What it solves: MLflow helps manage the challenges of managing hundreds of model runs and their results.

How it helps: When you’re tweaking hyperparameters and testing different algorithms, keeping track of what worked becomes impossible without proper tooling. MLflow acts like a lab notebook for your ML experiments. It captures your model parameters, performance metrics, and the actual model artifacts automatically. The best part? You can compare any two experiments side by side without digging through folders or spreadsheets.

What makes it useful: Works with any ML framework, stores everything in one place, and lets you deploy models with a single command.

Get started: MLflow Tutorials and Examples

2. DVC: Data Version Control

 
What it solves: Managing large datasets and complex data transformations.

How it helps: Git breaks when you try to version control large datasets. DVC fills this gap by tracking your data files and transformations separately while keeping everything synchronized with your code. Think of it as a better Git that understands data science workflows. You can recreate any experiment from months ago just by checking out the right commit.

What makes it useful: Integrates well with Git, works with cloud storage, and creates reproducible data pipelines.

Get started: Get Started with DVC

3. Kubeflow: ML Workflows on Kubernetes

 
What it solves: Running ML workloads at scale without becoming a Kubernetes expert

How it helps: Kubernetes is powerful but complex. Kubeflow wraps that complexity in ML-friendly abstractions. You get distributed training, pipeline orchestration, and model serving without wrestling with YAML files. It’s particularly valuable when you need to train large models or serve predictions to thousands of users.

What makes it useful: Handles resource management automatically, supports distributed training, and includes notebook environments.

Get started: Installing Kubeflow

4. Prefect: Modern Workflow Management

 
What it solves: Building reliable data pipelines with less boilerplate code.

How it helps: Airflow can sometimes be verbose and rigid. Prefect, however, is much easier for developers to get started with. It handles retries, caching, and error recovery automatically. The library feels more like writing regular Python code than configuring a workflow engine. It’s particularly good for teams that want workflow orchestration without the learning curve.

What makes it useful: Intuitive Python API, automatic error handling, and modern architecture.

Get started: Introduction to Prefect

5. FastAPI: Turn Your Model Into a Web Service

 
What it solves: FastAPI is useful for building production-ready APIs for model serving.

How it helps: Once your model works, you need to expose it as a service. FastAPI makes this straightforward. It automatically generates documentation, validates incoming requests, and handles the HTTP plumbing. Your model becomes a web API with just a few lines of code.

What makes it useful: Automatic API documentation, request validation, and high performance.

Get started: FastAPI Tutorial & User Guide

6. Evidently: ML Model Monitoring

 
What it solves: Evidently is great for monitoring model performance and detecting drifts

How it helps: Models degrade over time. Data distributions shift. Performance drops. Evidently helps you catch these problems before they impact users. It generates reports showing how your model’s predictions change over time and alerts you when data drift occurs. Think of it as a health check for your ML systems.

What makes it useful: Pre-built monitoring metrics, interactive dashboards, and drift detection algorithms.

Get started: Getting Started with Evidently AI

7. Weights & Biases: Experiment Management

 
What it solves: Weights & Biases is useful for tracking experiments, optimizing hyperparameters, and collaborating on model development.

How it helps: When multiple devs work on the same model, experiment tracking becomes all the more important. Weights & Biases provides a central place for logging experiments, comparing results, and sharing insights. It includes hyperparameter optimization tools and integrates with popular ML frameworks. The collaborative features help teams avoid duplicate work and share knowledge.

What makes it useful: Automatic experiment logging, hyperparameter sweeps, and team collaboration features.

Get started: W&B Quickstart

8. Great Expectations: Data Quality Assurance

 
What it solves: Great Expectations is for data validation and quality assurance for ML pipelines

How it helps: Bad data breaks models. Great Expectations helps you define what good data looks like and automatically validates incoming data against these expectations. It generates data quality reports and catches issues before they reach your models. Think of it as unit tests for your datasets.

What makes it useful: Declarative data validation, automatic profiling, and comprehensive reporting.

Get started: Introduction to Great Expectations

9. XGBoost

XGBoost is an optimized gradient boosting library designed for high performance and efficiency. It is widely used both in machine learning competitions and in practice. XGBoost is suitable for various tasks, including classification, regression, and ranking, and includes features for regularization and cross-platform integration.

Some features of XGBoost include:

• Implementations of state-of-the-art boosting algorithms that can be used for classification, regression, and ranking problems
• Built-in regularization to prevent overfitting and improve model generalization.

XGBoost tutorial on Kaggle is a good place to become familiar

10. FastAPI

So far we’ve looked at Python libraries. Let’s wrap up with a framework for building APIs—FastAPI.

FastAPI is a web framework for building APIs with Python. It is ideal for creating APIs to serve machine learning models, providing a robust and efficient way to deploy data science applications.

• FastAPI is easy to use and learn, allowing for quick development of APIs
• Provides full support for asynchronous programming, making it suitable for handling many simultaneous connections

FastAPI Tutorial: Build APIs with Python in Minutes is a comprehensive tutorial to learn the basics of building APIs with FastAPI.

Wrapping Up

I hope you found this round-up of data science libraries helpful. If there’s one takeaway, it should be that these Python libraries are useful additions to your data science toolbox.

We’ve looked at Python libraries that cover a range of functionalities—from data manipulation and visualization to machine learning, web scraping, and API development. If you’re interested in Python libraries for data engineering, you may find 7 Python Libraries Every Data Engineer Should Know helpful.

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.

A data scientist’s toolkit is incomplete without a strong grasp of Python and its powerful ecosystem of libraries. These libraries streamline everything from data cleaning and manipulation to building complex machine learning models and creating compelling visualizations.
Here are 10 essential Python libraries that every data scientist should know:

• NumPy: The foundational library for numerical computing in Python. It provides the ndarray object, a multi-dimensional array that is far more efficient for numerical operations than standard Python lists. NumPy is the backbone of many other data science libraries.
• What is NumPy?
  NumPy is a Python library used for working with arrays.
  It also has functions for working in domain of linear algebra, fourier transform, and matrices.
  NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.
  NumPy stands for Numerical Python.
  
  Why Use NumPy?
  In Python we have lists that serve the purpose of arrays, but they are slow to process.
  NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.
  The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy.
  Arrays are very frequently used in data science, where speed and resources are very important.
  Data Science: is a branch of computer science where we study how to store, use and analyze data for deriving information from it.
  
  Why is NumPy Faster Than Lists?
  NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently.
  This behavior is called locality of reference in computer science.
  This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures.
  
  Which Language is NumPy written in?
  NumPy is a Python library and is written partially in Python, but most of the parts that require fast computation are written in C or C++.
  
  Where is the NumPy Codebase?
  The source code for NumPy is located at this github repository https://github.com/numpy/numpy
  github: enables many people to work on the same codebase.
• Pandas: A must-have for data manipulation and analysis. Pandas introduces the DataFrame, a tabular data structure that is intuitive for working with structured data. It offers powerful tools for tasks like data cleaning, filtering, merging, and aggregation.
• pandas
  pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,
  built on top of the Python programming language.
  
  Getting started
  Install pandas
  Getting started
  Try pandas online
  Documentation
  User guide
  API reference
  Contributing to pandas
  Release notes
  Community
  About pandas
  Ask a question
  Ecosystem
  With the support of:
  The full list of companies supporting pandas is available in the sponsors page.
• Matplotlib: The classic and most widely used library for creating static, animated, and interactive visualizations. While it can require more code for complex plots, it offers a high degree of customization and is the foundation for other visualization libraries.
• Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.
• Create publication quality plots.
• Make interactive figures that can zoom, pan, update.
• Customize visual style and layout.
• Export to many file formats.
• Embed in JupyterLab and Graphical User Interfaces.
• Use a rich array of third-party packages built on Matplotlib.
• Try Matplotlib (on Binder)errorbar(x, y, yerr, xerr)
• Getting Started
• Examples
• Reference
• Cheat Sheets
• Documentation
• News
• Dec 13, 2024
• Matplotlib 3.10.0 Released
• We thank the 128 authors for the 337 pull requests that comprise the 3.10.0 release.
• May 30, 2024
• GSOC 2024: Bivariate Colormaps
• A warm welcome to Trygve Magnus Ræder, who is working on bivariate colormapping
• Matplotlib 3.9.0 Released
• We thank the 175 authors for the 450 pull requests that comprise the 3.9.0 release.
• Older Announcements
• Resources
• Be sure to check the Users guide and the API docs. The full text search is a good way to discover the docs including the many examples.
• Join our community at discourse.matplotlib.org to get help, share your work, and discuss contributing & development.
• Check out the Matplotlib tag on StackOverflow.
• Meet us at our monthly call for new contributors to the Matplotlib project. Subscribe to our community calendar at Scientific Python to get access to all our community meetings.
• Short questions related to contributing to Matplotlib may be posted on the gitter channel.
• Seaborn: Built on top of Matplotlib, Seaborn is a high-level library for creating aesthetically pleasing and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots, making it a favorite for exploratory data analysis.
• Scikit-learn: The go-to library for machine learning in Python. Scikit-learn provides a vast collection of algorithms for classification, regression, clustering, dimensionality reduction, and more. It also includes tools for data preprocessing, model selection, and evaluation.
• SciPy: A library for scientific and technical computing. SciPy builds on NumPy and provides modules for optimization, linear algebra, integration, interpolation, and other scientific and mathematical functions.
• Statsmodels: This library is dedicated to statistical modeling, providing a rich set of tools for estimating various statistical models, conducting statistical tests, and performing time-series analysis. It is a powerful resource for those who need a deep dive into the statistical aspects of their data.
• TensorFlow / PyTorch: These are the two dominant frameworks for deep learning. Developed by Google and Facebook, respectively, they are essential for building and training neural networks for tasks such as computer vision, natural language processing, and advanced predictive modeling.
• Plotly: A powerful library for creating interactive and high-quality visualizations for web-based dashboards and applications. Plotly allows you to build dynamic charts and graphs that users can explore, zoom, and interact with.
• Requests / Beautiful Soup: For data scientists who need to gather data from the web, these two libraries are invaluable. Requests is used for making HTTP requests to retrieve data from a website, while Beautiful Soup is used to parse the HTML or XML content and extract the desired information.