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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. BentoML: Package and Deploy Models Anywhere
What it solves: BentoML standardizes model deployment across different platforms
How it helps: Every deployment target has different requirements. BentoML abstracts these differences by providing a unified way to package models. Whether you’re deploying to Docker, Kubernetes, or cloud functions, BentoML handles the packaging and serving infrastructure. It supports models from different frameworks and optimizes them for production use.
What makes it useful: Framework-agnostic packaging, multiple deployment targets, and automatic optimization.
Get started: Hello world Tutorial | BentoML
10. Optuna: Automated Hyperparameter Tuning
What it solves: Finding optimal hyperparameters without manual guesswork.
How it helps: Hyperparameter tuning is time-consuming and often done poorly. Optuna automates this process using sophisticated optimization algorithms. It prunes unpromising trials early and supports parallel optimization. The library integrates with popular ML frameworks and provides visualization tools to understand the optimization process.
What makes it useful:Advanced optimization algorithms, automatic pruning, and parallel execution.
Get started: Optuna Tutorial
Wrapping Up
These libraries address different aspects of the MLOps pipeline, from experiment tracking to model deployment. Start with the tools that address your most pressing challenges, then gradually expand your toolkit as your MLOps maturity increases.
Most successful MLOps implementations combine 3-5 of these libraries into a cohesive workflow. Consider your team’s specific needs, existing infrastructure, and technical constraints when selecting your toolkit.
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.
