10 MLOps Tools for Machine Learning Practitioners to Know

Machine learning is not just about building models. It’s also about deploying, managing, and maintaining them. This is where machine learning operations (MLOps) comes in. MLOps combines machine learning with DevOps practices to streamline the entire model lifecycle, from training to deployment. It ensures automation, collaboration, and scalability in machine learning workflows. To support this, a growing set of tools has emerged.

In this article, we highlight 10 essential MLOps tools that every machine learning practitioner should know. These tools help build reliable and production-ready machine learning systems.

1. MLflow

MLflow is an tool that helps track machine learning experiments. It lets you log training runs, version models, and manage deployment stages. MLflow works with many popular machine learning libraries and can be used in any environment.

Key Features:

• Track metrics, parameters, and artifacts for each run
• Save and version models for reproducibility
• Manage models in different lifecycle stages

2. Weights & Biases

Weights & Biases is a platform for logging and visualizing machine learning experiments. It helps teams monitor model performance and organize experiments over time. W&B integrates with many ML libraries like TensorFlow, PyTorch, and Keras.

Key Features:

• Log training performance in real time
• Compare multiple runs and hyperparameters
• Track datasets, code, and model files

3. Comet

Comet is a tool that helps you monitor machine learning experiments from start to finish. It tracks metrics, parameters, code, and artifacts to make your experiments reproducible and well-documented.

Key Features:

• Track experiments, hyperparameters, and results
• Compare model runs using visual dashboards
• Record code versions and dataset changes
• Organize projects and collaborate with teams

4. Airflow

Apache Airflow is a workflow automation tool. It lets you define and schedule machine learning tasks like data preprocessing, training, evaluation, and deployment. You write workflows as Python code, and Airflow takes care of the execution order.

Key Features:

• Define machine learning workflows using Python scripts
• Schedule and automate repetitive tasks
• Monitor task progress through a web interface
• Handle retries, failures, and dependencies

5. Kubeflow

Kuberflow is a Kubernetes-based platform for building and managing machine learning workflows. It lets you run training, hyperparameter tuning, and model serving in the cloud or on local Kubernetes clusters.

Key Features:

• Build machine learning pipelines with full control
• Run jobs on Kubernetes clusters at scale
• Tools for tuning, serving, and tracking models

6. DVC (Data Version Control)

DVC is like Git for your data and models. It helps you version datasets, track changes, and keep everything in sync across experiments. It works well with Git and integrates with remote storage like S3 or Google Drive.

Key Features:

• Track and version datasets and models
• Connect large files to Git without storing them
• Reproduce experiments with consistent data and code
• Share projects with remote storage integration

7. Metaflow

Metaflow helps data scientists and machine learning engineers build and manage workflows using simple Python code. It supports tracking, scheduling, and scaling machine learning pipelines both locally and in the cloud.

Key Features:

• Run pipelines locally or on the cloud
• Automatically track runs and metadata
• Resume failed runs from the last step

8. Pachyderm

Pachyderm is a data pipeline and version control system. It helps you manage and track changes in data, and build reproducible pipelines that automatically update when data changes.

Key Features:

• Version control for datasets like Git for code
• Build automatic pipelines that run on data updates
• Reproduce results with full data and code history
• Works with Docker and any machine learning language

9. Evidently AI

Evidently AI is a monitoring tool for machine learning models. It helps detect issues like data drift, performance drops, or inconsistent predictions after deployment.

Key Features:

• Monitor data quality and model performance
• Detect data drift and changes over time
• Generate clear visual reports and dashboards

10. TensorFlow Extended (TFX)

TFX is Google’s platform for TensorFlow-based machine learning pipelines. It helps with everything from data processing to model training, validation, and deployment in real-world environments.

Key Features:

• Build full machine learning pipelines with reusable components
• Handle data validation and model evaluation
• Deploy models using scalable serving tools
• Use with Apache Airflow or Kubeflow orchestration

Final Thoughts

MLOps is an essential part of modern machine learning. It helps teams take models from notebooks to real-world use. Without MLOps, projects fail to scale or break in production. The right tools make this process easier and more reliable.

Tools like MLflow and W&B help track experiments. Airflow and Kubeflow help automate and run machine learning pipelines. DVC and Pachyderm take care of data and model versioning. Evidently AI supports monitoring model performance over time. TensorFlow Extended TFX provides a full pipeline for production-ready machine learning systems.

The best setup depends on your team’s size, goals, and infrastructure. By using these tools, you can save time, reduce errors, and improve model quality.