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Artificial Intelligence


MLOPS TOOLS COMPARED: MLFLOW VS. CLEARML—WHICH ONE IS RIGHT FOR YOU?

An MLOps tool helps automate the deployment, monitoring, and management of
machine learning models. MLflow and ClearML are two popular MLOps tools. Check
out how they compare with each other in this article.

Anuj Mudaliar Assistant Editor - Tech, SWZD
February 28, 2024

--------------------------------------------------------------------------------




 * An MLOps tool is defined as a software platform that facilitates and
   automates the end-to-end machine learning life cycles.
 * This article compares MLflow and ClearML, the top two MLOps tools.


TABLE OF CONTENTS

 * What Is MLOps?
 * MLflow
 * ClearML
 * Features of MLflow vs. ClearML
 * Features Review of MLflow and ClearML


WHAT IS MLOPS?

MLOps, or machine learning operations, is a set of practices that efficiently
and reliably deploys, manages, and monitors machine learning models in
production environments. It bridges the gap between developing powerful ML
models and their real-world applications. At the heart of successful MLOps
implementation lies the MLOps tool—a versatile and sophisticated software
platform designed to support the entire machine learning process.

An MLOps tool is a central hub, facilitating the collaboration and coordination
of cross-functional teams, including data scientists, engineers, and operations
personnel. Its primary purpose is to optimize the deployment and maintenance of
machine learning models, making it easier to transition from experimentation in
a controlled environment to practical, real-world applications.

What Is MLOps?

Source: WikipediaOpens a new window


MLFLOW

MLflow Experiments

Source: MLflowOpens a new window

MLflow is an open-source platform that simplifies the machine learning
lifecycle, from experimentation and development to deployment and monitoring. It
provides a unified interface that enables data scientists and engineers to
manage the end-to-end machine-learning process seamlessly.

MLflow comprises four primary components: Tracking, Projects, Models, and
Registry. The Tracking component allows users to record and query experiments,
making it easier to track model development progress.

The Projects component standardizes packaging machine learning code for
reproducibility and sharing. The Models component simplifies model deployment
and integration with various platforms, while the Registry component manages
model versioning and enables collaboration among teams.

The data science and machine learning communities have widely adopted MLflow, as
it offers an open and extensible platform that integrates seamlessly with
popular machine learning libraries and frameworks.

Its versatility, ease of use, and ability to handle the entire machine learning
lifecycle have made it a valuable tool for organizations seeking to accelerate
their AI initiatives and improve model management and deployment processes.

With MLflow, data scientists and engineers can collaborate more effectively,
streamline model development, and ensure models perform optimally in real-world
production environments. This platform exemplifies the importance of a unified
approach to MLOps growth, enabling organizations to harness the full potential
of their machine learning projects and make AI more accessible and manageable.


CLEARML

ClearML Dashboard

Source: ClearMLOpens a new window

ClearML is a cutting-edge, open-source platform that revolutionizes machine
learning experiment management and orchestration. It empowers data scientists,
researchers, and engineers with end-to-end solutions to effortlessly manage the
complete machine learning lifecycle, from experiment design to deployment.

ClearML offers robust capabilities for distributed computing, automating
experiment execution, tracking results, and enabling seamless collaboration
among teams. The platform ensures reproducibility, version control, and complete
transparency by collecting and centralizing metadata for every experiment.

It also simplifies model deployment by streamlining the transition from research
to production. ClearML’s intuitive and unified interface transforms how machine
learning projects are managed, fostering efficient collaboration and enhancing
productivity. This makes ClearML a valuable asset for organizations committed to
achieving excellence in artificial intelligence and data science.


FEATURES OF MLFLOW VS. CLEARML

Here’s a comparison of the features of MLflow and ClearML.

Feature MLflow ClearML Experiment tracking Provides experiment and parameter
tracking, making it easy to record and query experiment history and results.
Offers comprehensive experiment tracking, providing detailed metadata and
version control, ensuring full transparency and reproducibility. Model packaging
and deployment Offers a standardized way to package machine learning code into
reproducible runs and supports model deployment and serving. Simplifies model
deployment, allowing seamless transition from research to production
environments, with integrated platform support. Experiment versioning Provides
basic versioning for code, data, and models. Offers advanced experiment
versioning, enabling precise tracking of experiment artifacts and facilitating
reproducibility and collaboration. Distributed computing support Allows users to
run experiments locally or on a cluster to leverage distributed computing for
training models. Provides extensive support for distributed computing across
multiple machines, clusters, and cloud environments, ensuring efficient
execution of experiments. Model registry and collaboration Offers a basic model
registry to manage and organize models, with limited collaboration features.
Provides an advanced model registry that simplifies model versioning,
organization, and collaboration among team members, promoting effective model
management. User interface Offers a user-friendly web interface for experiment
tracking, making it easy to navigate and monitor experiments. Provides an
intuitive and unified interface for seamless experiment management,
collaboration, and tracking, enhancing productivity and visibility. Integration
with ML libraries Integrates with popular machine learning libraries and
frameworks, such as scikit-learn, TensorFlow, and PyTorch. Offers versatile
integration with various ML libraries and tools, ensuring compatibility with the
tools and libraries data scientists prefer to use. Open source and extensible
MLflow is open source, allowing users to contribute and extend its capabilities
to meet their needs. ClearML is open source and offers extensibility, enabling
users to customize and extend its functionality to suit their requirements.
Community support Backed by an active and growing community that contributes to
its development and provides support. Supported by a community of users and
contributors, offering community-driven solutions and assistance for users.


FEATURES REVIEW OF MLFLOW AND CLEARML

Based on our research, the following are the features review of MLflow and
ClearML.


1. EXPERIMENT TRACKING

Experiment tracking is a fundamental component in machine learning, enabling
data scientists and researchers to record, monitor, and analyze the outcomes of
various experiments. MLflow and ClearML are two notable platforms that excel in
this area, each offering distinct features and capabilities for experiment
tracking.

MLflow provides a robust experiment tracking system that simplifies the
management of machine learning experiments. It offers the ability to record and
query experiments and their parameters, making it easy for data scientists to
monitor the progress and results of their projects. 

MLflow’s tracking functionality is user-friendly and features a web interface
that offers an organized view of all recorded experiments. Users can quickly
access details of experiment runs, including metrics, parameters, and other
relevant information.

MLflow also enables basic versioning for code, data, and models. This feature
allows users to track changes to their machine-learning projects, promoting
reproducibility in the experiment management process.

ClearML offers an advanced and comprehensive experiment-tracking system designed
to meet the rigorous demands of modern machine-learning workflows. It collects
and centralizes metadata for every experiment, providing data scientists with a
detailed record of every run. This includes fine-grained information about the
code, data, and models used in each experiment, fostering transparency and
reproducibility.

ClearML’s experiment tracking system supports advanced versioning, allowing for
precise tracking of all experiment artifacts. This level of detail makes it
easier to ensure full reproducibility and enhances collaboration among team
members.

ClearML also facilitates experiment organization and collaboration, making it an
ideal choice for teams working on complex machine-learning projects.

Conclusion: ClearML is a better choice due to its advanced experiment tracking,
detailed versioning, and enhanced support for transparency and collaboration.


2. MODEL PACKAGING AND DEPLOYMENT

MLflow provides a standardized and practical approach to model packaging and
deployment. It offers the ability to package machine learning code into
reproducible runs, making creating and managing model artifacts easier.

MLflow also supports model deployment and serving through various integrations
and plugins. Its simplicity makes it ideal for data scientists and researchers
who require a straightforward way to transition from research to production.

However, it is essential to note that MLflow’s model deployment capabilities are
relatively basic and may require additional integration with external tools or
platforms for more complex production scenarios.

ClearML takes model packaging and deployment to a more advanced level. It
simplifies the transition from experimental models to real-world deployment
environments. ClearML provides a platform that streamlines model deployment,
integrating seamlessly with various platforms and environments. This feature
offers greater flexibility and adaptability, particularly for organizations with
diverse and complex production systems.

ClearML’s approach is characterized by a high degree of automation, allowing
users to deploy models with minimal manual intervention. It facilitates the
packaging and serving of machine learning models, simplifying the process of
scaling models to handle real-world workloads. 

This level of automation and versatility makes ClearML well-suited for
organizations seeking to optimize their machine-learning deployment strategies.

Conclusion: ClearML is a better choice due to its advanced automation,
versatility, and adaptability for complex machine-learning model deployment.


3. EXPERIMENT VERSIONING

MLflow provides basic experiment versioning to help users keep track of changes
in their machine-learning projects. This includes versioning of code, data, and
models used in experiments. MLflow’s version tracking allows data scientists to
understand the evolution of their experiments and revert to previous versions if
necessary.

While MLflow’s experiment versioning is valuable for basic use cases, it may be
limited in more complex and collaborative scenarios where fine-grained tracking
and detailed version history are essential. Advanced version control features,
such as branch management and more detailed metadata, are not the primary focus
of MLflow’s versioning capabilities.

ClearML offers advanced and comprehensive experiment versioning that addresses
the needs of modern machine learning workflows. It provides detailed tracking of
changes to code, data, and models at a fine-grained level.

ClearML’s experiment versioning allows users to precisely understand the
evolution of an experiment, providing insights into every artifact and parameter
change.

ClearML excels in supporting experiment reproducibility and collaboration by
offering a high degree of transparency and granular version control. Its
advanced versioning capabilities make it an ideal choice for teams working on
complex machine-learning projects, where maintaining a detailed version history
is essential.

Conclusion: MLflow is better for organizations looking for a simplified,
straightforward approach to experiment versioning in machine learning workflows.


4. MODEL REGISTRY AND COLLABORATION

MLflow includes a model registry that provides basic functionalities for
organizing and managing machine learning models. It allows users to track and
compare different model versions, making it easier to select the best-performing
models.

While MLflow supports model versioning, its model registry is relatively simple
and may be more suitable for smaller teams or projects with straightforward
model management needs. Collaboration within MLflow is possible through its
user-friendly web interface.

ClearML offers an advanced model registry that facilitates model versioning,
organization, and collaboration. It allows teams to manage and monitor model
versions with high granularity, making tracking changes and improvements across
iterations easier.

ClearML’s collaborative features allow data scientists, researchers, and
engineers to work together seamlessly. Its unified interface offers clear
visibility into experiment runs, model versions, and associated metadata,
enhancing collaboration and knowledge sharing among team members. This makes it
ideal for larger teams and more complex machine-learning projects.

Conclusion: ClearML is a better choice due to its advanced model registry and
comprehensive collaboration features, making it suitable for larger teams and
complex machine-learning projects.


5. COMMUNITY SUPPORT

MLflow has gained significant popularity in the machine learning community
thanks to its user-friendly approach and ease of use. It has an active and
growing user base, which has led to an engaged and supportive community.

Users can access various resources, including documentation, forums, and online
communities, to seek help, share insights, and troubleshoot issues.

The MLflow community has contributed to developing integrations, plugins, and
extensions that extend the platform’s capabilities. This vibrant community
support benefits data scientists and organizations looking for guidance, MLOps
best practices, and solutions for using MLflow effectively.

ClearML, while also open source, is associated with a community of users and
contributors actively engaged in the platform’s development. The community
offers valuable support, with users sharing their experiences, solving problems,
and collaborating on enhancements and extensions. ClearML’s community-driven
solutions give users practical guidance and insights to maximize the platform’s
utility.

ClearML’s community support complements its extensibility, enabling users to
tailor the platform to their needs and workflows. The active engagement of users
and contributors makes ClearML a strong choice for organizations and data
scientists seeking a dynamic and evolving platform backed by a responsive
community.

Conclusion: MLflow is a better choice due to its widespread adoption,
user-friendly approach, and active, growing community that provides valuable
support to users.


TAKEAWAY

The choice between MLflow and ClearML ultimately depends on the needs and
complexities of your machine learning projects. MLflow offers simplicity and
user-friendliness, making it suitable for those with straightforward experiment
versioning requirements. ClearML excels in advanced experiment versioning,
detailed model registry, collaboration, and fine-grained model tracking, making
it ideal for larger teams and complex machine-learning projects.

Both platforms have active communities for support and insights, but MLflow’s
popularity gives it an edge in terms of available resources. In summary, choose
MLflow for a user-friendly, straightforward experience, and opt for ClearML if
you require advanced experiment management, collaboration, and detailed
versioning for complex machine learning projects.

Did this article help you understand the comparative features of MLflow and
ClearML? Which MLOps tool will you prefer among these? Let us know on
LinkedInOpens a new window , XOpens a new window , or FacebookOpens a new window
. We’d love to hear from you!

Image source: Shutterstock


MORE ON ARTIFICIAL INTELLIGENCE

 * What Is Artificial Intelligence (AI) as a Service? Definition, Architecture,
   and Trends
 * What Is Deep Learning? Definition, Techniques, and Use Cases
 * Data Science vs. Machine Learning: Top 10 Differences
 * What Is General Artificial Intelligence (AI)? Definition, Challenges, and
   Trends
 * Narrow AI vs. General AI vs. Super AI: Key Comparisons



artificial intelligence Machine Learning

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Anuj Mudaliar

Assistant Editor - Tech, SWZD

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Anuj Mudaliar is a content development professional with a keen interest in
emerging technologies, particularly advances in AI. As a tech editor for
Spiceworks, Anuj covers many topics, including cloud, cybersecurity, emerging
tech innovation, AI, and hardware. When not at work, he spends his time outdoors
- trekking, camping, and stargazing. He is also interested in cooking and
experiencing cuisine from around the world.
Do you still have questions? Head over to the Spiceworks Community to find
answers.
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