Skip to main content
HomeBlogMachine Learning

17 Top MLOps Tools You Need to Know

Discover top MLOps tools for experiment tracking, model metadata management, workflow orchestration, data and pipeline versioning, model deployment and serving, and model monitoring in production.
Dec 2022  · 13 min read

As we explore in our article on Getting Started with MLOps, MLOps is built on the fundamentals of DevOps, the software development strategy to efficiently write, deploy, and run enterprise applications.

It is an approach to managing machine learning projects at scale. MLOps enhance the collaboration between development, operational, and data science teams. As a result, you get faster model deployment, optimized team productivity, reduction in risk and cost, and continuous model monitoring in production. 

Learn why MLOps is important and what problems it aims to solve by reading our blog on The Past, Present, and Future of MLOps

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. To get a thorough introduction to the MLOps Fundamentals, check out our Skill Track. 

Experiment Tracking and Model Metadata Management Tools

These tools allow you to manage model metadata and help with experiment tracking:

1. MLFlow

MLflow is an open-source tool that helps you manage core parts of the machine learning lifecycle. It is generally used for experiment tracking, but you can also use it for reproducibility, deployment, and model registry. You can manage the machine learning experiments and model metadata by using CLI, Python, R, Java, and REST API. 

MLflow has four core functions:

  1. MLflow Tracking: storing and accessing code, data, configuration, and results.
  2. MLflow Projects: package data science source for reproducibility.
  3. MLflow Models: deploying and managing machine learning models to various serving environments. 
  4. MLflow Model Registry: a central model store that provides versioning, stage transitions, annotations, and managing machine learning models. 


Image by Author

2. Comet ML

Comet ML is a platform for tracking, comparing, explaining, and optimizing machine learning models and experiments. You can use it with any machine learning library, such as Scikit-learn, Pytorch, TensorFlow, and HuggingFace. 

Comet ML is for individuals, teams, enterprises, and academics. It allows anyone to easily visualize and compare the experiments. Furthermore, it enables you to visualize samples from images, audio, text, and tabular data.

Comet ML

Image from Comet ML

3. Weights & Biases

Weights & Biases is an ML platform for experiment tracking, data and model versioning, hyperparameter optimization, and model management. Furthermore, you can use it to log artifacts (datasets, models, dependencies, pipelines, and results) and visualize the datasets (audio, visual, text, and tabular).

Weights & Biases has a user-friendly central dashboard for machine learning experiments. Like Comet ML, you can integrate it with other machine learning libraries, such as Fastai, Keras, PyTorch, Hugging face, Yolov5, Spacy, and many more.

Weights & Biases

Gif from Weights & Biases

Note: you can also use TensorBoard, Pachyderm, DagsHub, and DVC Studio for experiment tracking and ML metadata management. 

Orchestration and Workflow Pipelines MLOps Tools

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

4. Prefect

The Prefect is a modern data stack for monitoring, coordinating, and orchestrating workflows between and across applications. It is an open-source, lightweight tool built for end-to-end machine-learning pipelines. 

You can either use Prefect Orion UI or Prefect Cloud for the databases. 

Prefect Orion UI is an open-source, locally hosted orchestration engine and API server. It provides you insights into the local Prefect Orion instance and the workflows.  

Prefect Cloud is a hosted service for you to visualize flows, flow runs, and deployments. Furthermore, you can manage accounts, workspace, and team collaboration. 


Image from Prefect

5. Metaflow

Metaflow is a powerful, battle-hardened workflow management tool for data science and machine learning projects. It was built for data scientists so they can focus on building models instead of worrying about MLOps engineering. 

With Metaflow, you can design workflow, run it on the scale, and deploy the model in production. It tracks and version machine learning experiments and data automatically. Furthermore, you can visualize the results in the notebook. 

Metaflow works with multiple clouds (including AWS, GCP, and Azure) and various machine-learning Python packages (like Scikit-learn and Tensorflow), and the API is available for R language too. 


Image from Metaflow

6. Kedro

Kedro is a workflow orchestration tool based on Python. You can use it for creating reproducible, maintainable, and modular data science projects. It integrates the concepts from software engineering into machine learning, such as modularity, separation of concerns, and versioning.

With Kedro, you can:

  1. Set up dependencies and configuration.
  2. Set up data.
  3. Create, visualize, and run the pipelines.
  4. Logging and experiment tracking.
  5. Deployment on a single or distributed machine.
  6. Create maintainable data science code.
  7. Create modular, reusable code.
  8. Collaborate with teammates on projects.


Gif from Kedro

Note: you can also use Kubeflow and DVC for orchestration and Workflow pipelines. 

Data and Pipeline Versioning Tools

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

7. Pachyderm

Pachyderm automates data transformation with data versioning, lineage, and end-to-end pipelines on Kubernetes. You can integrate with any data (Images, logs, video, CSVs), any language (Python, R, SQL, C/C++), and at any scale (Petabytes of data, thousands of jobs).

The community edition is open-source and for a small team. Organizations and teams who want advanced features can opt for the Enterprise edition. 

Just like Git, you can version your data using a similar syntax. In Pachyderm, the highest level of the object is Repository, and you can use Commit, Branches, File, History, and Provenance to track and version the dataset. 


Image from Pachyderm

8. Data Version Control (DVC)

Data Version Control is an open-source and popular tool for machine learning projects. It works seamlessly with Git to provide you with code, data, model, metadata, and pipeline versioning. 

DVC is more than just a data tracking and versioning tool. 

You can use it for:

  • Experiment tracking (model metrics, parameters, versioning).
  • Create, visualize, and run machine learning pipelines. 
  • Workflow for deployment and collaboration.
  • Reproducibility.
  • Data and model registry.
  • Continuous integration and deployment for machine learning using CML.


Image from DVC

Note: DagsHub can also be used for data and pipeline versioning.

Model Deployment and Serving Tools

When it comes to deploying models, these MLOps tools can be hugely helpfu:

9. TensorFlow Extended (TFX) Serving

TensorFlow Extended (TFX) Serving helps you deploy a trained model as an endpoint. With TFX, you can now experiment, train, deploy, and maintain machine learning models. It allows you to create a REST API by using TFX CLI.

TensorFlow Serving is robust, flexible, and scalable, and it comes with a load balancer to manage a large number of requests. You can server the predictions with Docker and Kubernetes or build a model server with unique configurations. 

TensorFlow Serving maintains hardware efficiency by initiating batch requests. Furthermore, it offers model versioning and management. On the downside, it only works with Tensorflow models.  

import tempfile

MODEL_DIR = tempfile.gettempdir()
version = 1
export_path = os.path.join(MODEL_DIR, str(version))
print('export_path = {}\n'.format(export_path))


os.environ["MODEL_DIR"] = MODEL_DIR
nohup tensorflow_model_server \
  --rest_api_port=8501 \
  --model_name=fashion_model \
  --model_base_path="${MODEL_DIR}" >server.log 2>&1

10. BentoML

BentoML makes it easy and faster to ship machine learning applications. It is a Python-first tool for deploying and maintaining APIs in production. It scales with powerful optimizations by running parallel inference and adaptive batching and provides hardware acceleration.  

BentoML’s interactive centralized dashboard makes it easy to organize and monitor when deploying machine learning models. The best part is that it works with all kinds of machine learning frameworks, such as Keras, ONNX, LightGBM, Pytorch, and Scikit-learn. In short, BentoML provides a complete solution for model deployment, serving, and monitoring.


Image from BentoML

11. Cortex

Cortex lets you deploy, manage, and scale machine learning models in production. It is an open-source, flexible, and multi-framework tool for model serving and monitoring. 

Cortex expands to Docker, Kubernetes, TensorFlow Serving, TorchServe, and other ML libraries. It manages loads by providing scalable endpoints. Furthermore, you can deploy multiple models on a single API endpoint, and it supports auto-scaling features for securing APIs. It's an MLOps tool that grants you full control over model management operations.

create or update apis

  cortex deploy [CONFIG_FILE] [flags]

  -e, --env string      environment to use
  -f, --force           override the in-progress api update
  -y, --yes             skip prompts
  -o, --output string   output format: one of pretty|json (default "pretty")
  -h, --help            help for deploy

Note: you can also use MLflow, Kubeflow, and AWS sagemaker for model deployment and serving. 

Model Monitoring in Production ML Ops Tools

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

12. Evidently

Evidently AI is an open-source Python library for monitoring ML models during development, validation, and in production. It checks data and model quality, data drift, target drift, and regression and classification performance. 

Evidently has three main components:

  1. Tests (batch model checks): for performing structured data and model quality checks. 
  2. Reports (interactive dashboards): interactive data drift, model performance, and target virtualization. 
  3. Monitors (real-time monitoring): monitors data and model metrics from deployed ML service.


Image from Evidently

13. Fiddler

Fiddler AI is an ML model monitoring tool with an easy-to-use, clear UI. It lets you explain and debug predictions, analyze mode behavior for the entire dataset, deploy machine learning models at scale, and monitor model performance.

Let’s look at the main Fiddler AI features for ML monitoring:

  • Performance monitoring: in-depth visualization of data drifting, when it’s drifting, and how it’s drifting.
  • Data integrity: avoid feeding incorrect data for model training.
  • Tracking outliers: shows univariate and multivariate outliers.
  • Service metrics: shows basic insights into the ML service operations.
  • Alerts: set up alerts for a model or group of models to warn of the issues in production.


Image from Fiddler

14. Censius AI

Censius is an end-to-end AI observability platform that offers automatic monitoring and proactive troubleshooting. It lets you monitor the entire ML pipeline, explain predictions, and fix issues. You can set up Censius using Python or Java SDK or REST API and deploy it on-premises or on the cloud. 

Key features:

  • Monitor performance degradation, data drifts, and data quality.
  • Real-time notification, alerting about future issues.
  • Customizable dashboards for data, models, and business metrics.
  • Native support for A/B test frameworks.
  • Data explainability for tabular, image, and textual datasets.


Image from Censius

Note: Amazon Sagemaker also provides model monitoring in production.

Also, read Machine Learning, Pipelines, Deployment, and MLOps Tutorial to learn how multiple MLOps tools are integrated into machine learning applications with code examples. 

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:

15. AWS SageMaker

Amazon Web Services SageMaker is a one-stop solution for MLOps. You can train and accelerate model development, track and version experiments, catalog ML artifacts, integrate CI/CD ML pipelines, and deploy, serve, and monitor models in production seamlessly.

Key features:

  • A collaborative environment for data science teams.
  • Automate ML training workflows.
  • Deploy and manage models in production.
  • Track and maintain model versions. 
  • CI/CD for automatic integration and deployment.
  • Continuous monitoring and retaining models to maintain quality. 
  • Optimize the cost and performance.

Amazon SageMaker

Image from Amazon SageMaker

16. DagsHub

DagsHub is a platform made for the machine learning community to track and version the data, models, experiments, ML pipelines, and code. It allows your team to build, review, and share machine-learning projects. 

Simply put, it is a GitHub for machine learning, and you get various tools to optimize the end-to-end machine learning process. 

Key features:

  • Git and DVC repository for your ML projects.
  • DagsHub logger and MLflow instance for experiment tracking.
  • Dataset annotation using label studio instance. 
  • Diffing the Jupyter notebooks, code, datasets, and images.
  • Ability to comment on the file, the line of the code, or the dataset. 
  • Create a report for the project just like GitHub wiki. 
  • ML pipeline visualization.
  • Reproducible results.
  • Running CI/CD for model training and deployment. 
  • Data Merging.
  • Provide integration with GitHub, Google Colab, DVC, Jenkins, external storage, webhooks, and New Relic. 


Image by Author

17. Kubeflow

Kubeflow makes machine learning model deployment on Kubernetes simple, portable, and scalable. You can use it for data preparation, model training, model optimization, prediction serving, and motor the model performance in production. You can deploy machine learning workflow locally, on-premises, or to the cloud. In short, it is making Kubernetes easy for data science teams. 

Key features:

  • Centralized dashboard with interactive UI. 
  • Machine learning pipelines for reproducibility and streamlining. 
  • Provides native support for JupyterLab, RStudio, and Visual Studio Code.
  • Hyperparameter tuning and neural architecture search.
  • Training jobs for Tensorflow, Pytorch, PaddlePaddle, MXNet, and XGboost.
  • Job scheduling. 
  • Provide administrators with multi-user isolation.
  • Works with all of the major cloud providers. 


Image from Kubeflow


We’re at a time when there is a boom in the MLOps industry. Every week you see new development, new startups, and new tools launching to solve the basic problem of converting notebooks into production-ready applications. Even existing tools are expanding the horizon and integrating new features to become super MLOps tools. 

In this blog, we have learned about the best MLOps tools for various steps of the MLOps process. These tools will help you during the experimentation, development, deployment, and monitoring stage. 

If you are new to machine learning and want to master the essential skills to land a job as a machine learning scientist, try taking our Machine Learning Scientist with Python career track. 

If you are a professional and want to learn more about standard MLOps Practices, read our article on the MLOps Best Practices and How to Apply Them and check out our MLOps Fundamentals skill track. 

MLOps Tools FAQs

What are MLOps Tools?

MLOps tools help standardize, simplify, and streamline the ML ecosystem. These tools are used for experiment tracking, model metadata management, orchestration, model optimization, workflow versioning, model deployment and serving, and model monitoring in production. 

What skills are needed for an MLOps Engineer?

  • Ability to implement cloud solutions.
  • Experience with Docker and Kubernetes.
  • Experience with Quality Assurance using experiment tracking and workflow versioning.
  • Ability to build MLOps pipelines.
  • Familiar with Linux operating system.
  • Experience with ML frameworks such as PyTorch, Tensorflow, and TFX.
  • Experience with DevOps and software development.
  • Experience with unit and integration testing, data, and model validation, and post-deployment monitoring.

Which cloud is best for MLOps?

AWS, GCP, and Azure provide a variety of tools for the machine learning lifecycle. They all provide end-to-end solutions for MLOps. AWS takes the lead in terms of popularity and market share. It also provides easy solutions for model training, serving, and monitoring.

Is MLOps easy to learn?

It depends on your prior experience. To master MLOps, you need to learn both machine learning and software development life cycles. Apart from strong proficiency in programming languages, you need to learn several MLOps tools. It is easy for DevOps engineers to learn MLOps as most of the tools and strategies are driven by software development.

Is Kubeflow better than MLflow?

It depends on the use case. Kubeflow provides reproducibility at a larger level than MLflow, as it manages the orchestration. 

  • Kubeflow is generally used for deploying and managing complex ML systems at scale.
  • MLFlow is generally used for ML experiment tracking and storing and managing model metadata.

How is MLOps different from DevOps?

Both are software development strategies. DevOps focuses on developing and managing large-scale software systems, while MLOps focuses on deploying and maintaining machine learning models in production.

  • DevOps: Continuous Integration(CI) and Continuous Delivery(CD).
  • MLOps: Continuous Integration, Continuous Delivery, Continuous Training, and Continuous Monitoring.

MLOps Course

MLOps Concepts

BeginnerSkill Level
2 hr
Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
See DetailsRight Arrow
Start Course
See MoreRight Arrow

What is Named Entity Recognition (NER)? Methods, Use Cases, and Challenges

Explore the intricacies of Named Entity Recognition (NER), a key component in Natural Language Processing (NLP). Learn about its methods, applications, and challenges, and discover how it's revolutionizing data analysis, customer support, and more.
Abid Ali Awan's photo

Abid Ali Awan

9 min

The Curse of Dimensionality in Machine Learning: Challenges, Impacts, and Solutions

Explore The Curse of Dimensionality in data analysis and machine learning, including its challenges, effects on algorithms, and techniques like PCA, LDA, and t-SNE to combat it.
Abid Ali Awan's photo

Abid Ali Awan

7 min

Machine Learning Engineer Salaries in 2023

Find out how much machine learning engineers make around the world at different career stages. Learn how you can become a top-earning machine learning engineer today.
Natassha Selvaraj's photo

Natassha Selvaraj

16 min

What is Continuous Learning? Revolutionizing Machine Learning & Adaptability

A primer on continuous learning: an evolution of traditional machine learning that incorporates new data without periodic retraining.

Yolanda Ferreiro

7 min

What is Natural Language Processing (NLP)? A Comprehensive Guide for Beginners

Explore the transformative world of Natural Language Processing (NLP) with DataCamp’s comprehensive guide for beginners. Dive into the core components, techniques, applications, and challenges of NLP.
Matt Crabtree's photo

Matt Crabtree

11 min

What is Topic Modeling? An Introduction With Examples

Unlock insights from unstructured data with topic modeling. Explore core concepts, techniques like LSA & LDA, practical examples, and more.
Kurtis Pykes 's photo

Kurtis Pykes

13 min

See MoreSee More