In our comprehensive guide on Initiating Your MLOps Journey, we delve into the exciting realm of the best MLOps tools, a methodology firmly rooted in the foundational principles of DevOps. DevOps is a well-known strategy in the world of software development. Its goal is to make it easier to create, deploy, and run enterprise-level apps. On the other hand, MLOps is a completely new way to handle machine learning projects, especially when they grow.
Its main goal is to help development, operations, and data science teams work together better, which has a lot of important benefits. These benefits include faster model deployment, a big increase in team productivity, a big drop in both risks and costs, and the ability to keep an eye on models that are already in use in production environments. Below we have mentioned the best MLOps tools.
Benefits of using MLOps tools
Using MLOps (Machine Learning Operations) tools can help organizations and data science teams build, deploy, and manage machine learning models in a number of ways. Here are a few of the main pros:
🤝 Collaboration is better because MLOps tools make it easier for data scientists, engineers, and other stakeholders to work together. They make it easy for teams to work together on machine learning projects by giving them a central place to share code, models, and documentation.
🔄 Version Control: Like traditional software development, MLOps tools allow machine learning models and code to have version control. This keeps track of changes and makes it easy to go back to older versions if something goes wrong.
🔁 Reproducibility: MLOps tools help make sure that machine learning experiments can be done again and again. Experiments can be repeated with the same code and data, which is important for debugging, auditing, and validating models.
🤖 Automation: One of the main parts of MLOps is automation. These tools automate tasks like training, deploying, and monitoring models, which cuts down on mistakes and saves time.
📈 Scalability: MLOps tools are made to meet the needs of machine learning projects for scalability. They can make it easy to handle large datasets and automate the deployment of models to different environments.
Best MLOps Tools Comparison Table
The best MLOps (Machine Learning Operations) tools are shown in the table below. It gives a brief summary of their most important features, strengths, and weaknesses. This helps data professionals and organizations choose the best MLOps tool to streamline and improve their machine learning workflow.
|Tool||Purpose||Language Support||Visualization||Version Control||Integration|
|MLflow||Experiment Tracking||Multiple||Yes (UI & APIs)||Git||Many|
|Comet ML||Experiment Tracking||Multiple||Yes (UI & APIs)||Git||Various|
|Weights & Biases||Experiment Tracking||Multiple||Yes (UI & APIs)||Git||Many|
|Prefect||Workflow Management||Python||Limited (APIs)||Git||Customizable|
|Metaflow||Workflow Management||Python||Limited (UI)||Git||AWS Integration|
- Open-source MLOps platform
- End-to-end machine learning lifecycle management
- Tracking experiments, packaging code, and model serving
MLflow is an open-source tool that helps you manage key parts of the machine learning lifecycle. Usually, it is used to keep track of experiments, but it can also be used for reproducibility, deployment, and model registry. You can use CLI, Python, R, Java, and REST API to take care of the machine learning experiments and model metadata. Still, this is one of the best MLOps Tools you can consider now.
- Open-source and supported by Databricks.
- Easy tracking of experiments and model versions.
- Supports multiple ML frameworks.
- Integration with popular libraries like TensorFlow and PyTorch.
- Limited visualization capabilities.
- Lacks advanced orchestration and deployment features.
- May require additional tools for complete MLOps pipelines.
- Experiment tracking and collaboration platform
- Version control for machine learning models
- Visualization and sharing of results
Comet ML is a place where machine learning models and experiments can be tracked, compared, explained, and made better. It can be used with any library for machine learning, like Scikit-learn, Pytorch, TensorFlow, and HuggingFace. Comet ML is for people, groups, businesses, and schools. It makes it easy for anyone to see the experiments and compare them. It also lets you see samples of data from images, audio, text, and tables. Overall, this is one of the best MLOps Tools you can consider now.
- Strong focus on experiment tracking and collaboration.
- Robust visualization and reporting features.
- Integration with popular ML frameworks.
- Easy sharing of results and findings with team members.
- Some advanced features are available only in the paid version.
- Pricing can be expensive for larger teams or organizations.
- Limited support for full-scale MLOps pipelines.
Weights & Biases
- Experiment tracking and visualization for ML projects
- Real-time monitoring of model performance
- Hyperparameter optimization and project collaboration
Weights & Biases is an ML platform for keeping track of experiments, keeping different versions of data and models, optimizing hyperparameters, and taking care of models. You can also use it to log artifacts, such as datasets, models, dependencies, pipelines, and results, and to see the datasets in different ways (audio, visual, text, and tabular).
For machine learning experiments, Weights & Biases has a central dashboard that is easy to use. Like Comet ML, you can use it with other machine learning libraries like Fastai, Keras, PyTorch, Hugging face, Yolov5, Spacy, and many more. Overall, this is one of the best MLOps Tools you can consider now.
- Comprehensive experiment tracking and visualization.
- Support for various ML frameworks and languages.
- Strong collaboration and sharing capabilities.
- Pricing can be costly for large-scale usage.
- Advanced features may require a paid subscription.
- Limited orchestration and deployment features.
- Workflow management system for data engineering and ML
- Scheduling, monitoring, and error handling for data pipelines
- Supports dynamic workflows and dependency management
The Prefect is a modern data stack for monitoring, coordinating, and orchestrating workflows between and among applications. It is a small, open-source tool for building machine learning pipelines from start to finish. Prefect Orion UI is an orchestration engine and API server that is hosted locally and is free to use. It tells you about the local instance of Prefect Orion and the workflows. You can see flows, flow runs, and deployments in Prefect Cloud, which is a hosted service. You can also manage your accounts, your workspace, and teamwork. Thus, this is one of the best MLOps Tools you can consider now.
- Powerful workflow orchestration and automation capabilities.
- Supports building complex data pipelines.
- Open-source with an active community.
- Extensive Python SDK for custom workflows.
- Less focus on experiment tracking and model management.
- May require integration with other tools for complete MLOps.
- Steeper learning curve for complex workflows.
- Netflix’s open-source framework for real-life data science projects
- Focuses on simplicity and scalability
- Supports easy integration with AWS services for data processing
Metaflow is a powerful workflow management tool for data science and machine learning projects that has been tried and tested in the field. It was made for data scientists so they can focus on building models instead of worrying about MLOps engineering. Overall, this is one of the best MLOps Tools you can consider now.
With Metaflow, you can design workflow, run it on a large scale, and put the model into production. It automatically keeps track of machine learning experiments and data and makes versions of them. Also, you can see what the results are like in the notebook.
- Developed by Netflix and optimized for data science workflows.
- Easy-to-use framework with Python SDK.
- Built-in support for versioning and reproducibility.
- Integration with AWS services.
- Limited support for non-Python languages and ML frameworks.
- May not be as feature-rich as some other MLOps tools.
- Primarily designed for use within AWS environments.
How to choose the best MLOps tool for your needs?
Choosing the best MLOps (Machine Learning Operations) tool for your needs can be a key step in streamlining your machine learning workflows, improving collaboration, and making sure that your ML projects can be reproduced and scaled. Here’s a step-by-step guide to help you choose wisely:
🧐 Look at what you need: Start by finding out what your organization needs and what it can’t do. Think about things like the size of your ML team, the difficulty of your ML projects, your budget, and the technology stack you already have. Find the most important problems you want to solve with an MLOps tool.
🎯 Set clear goals: Use MLOps to make a clear list of your goals. Do you want to automate model deployment, improve model monitoring, make it easier for data scientists and DevOps teams to work together, or make sure that compliance and governance are met? Knowing your goals will help you decide which features to put first.
👥 Community and Helping Hands: Think about the people who use the tool. When communities are active, there are usually more resources, more documentation, and community-driven extensions. Check the quality and responsiveness of customer service for commercial tools.
🛠️ Simple to Use: Make sure the tool is easy to use and fits the skill level of your team. Training and getting started should not take too much time or be too hard to understand.
💰 Analysis of costs: Calculate the total cost of ownership (TCO), which includes licensing fees, infrastructure costs, and any other resources that may be needed. Think about both immediate and long-term costs.
✈️ Test Flights: Run a test project with a tool before you commit to it to see how well it fits your needs. This will give you useful information about how it can be used and how well it works in your organization.
Because MLflow is both reliable and easy to use, and because it is also a powerful tool, it is one of the best ways to manage almost the whole lifecycle of a machine learning project. Let’s go into more detail about MLflow and talk about why it’s one of the most popular MLOps tools.
An MLOps platform gives data scientists and software engineers a place to work together. It allows for iterative data exploration, real-time co-working on experiment tracking, feature engineering, and model management, as well as controlled model transitioning, deployment, and monitoring.