A Comparison of MLOps Platforms: Coretex and ClearML

Vuk Manojlović
Computer Vision
Dataset Management
User Experience
MLOps platforms play a central role in streamlining and optimizing the machine learning workflow by serving as a sophisticated interface between the user and the ML process. Therefore, they allow their users to spend more time on high-level tasks, abstracting most of the low-level engineering into more manageable tools and configurations.

This comparison between Coretex.ai and ClearML delves into their unique features, usability, and how they cater to various needs within the machine learning ecosystem. Through detailed examination, it highlights Coretex.ai's all-in-one approach and user-friendly features against ClearML's script-centric methodology, offering a comprehensive guide to help readers navigate the complexities of choosing between these two MLOps platforms.

Feature Coretex ClearML
Model Training and Validation
Hyperparameter Tuning
Integration with ML Frameworks
Scalable Computing Resources
Collaborative Tools
Data Visualization Tools
APIs for Model Deployment
Security and Privacy Features
Custom Scripting and Plugin Support
Dataset Management and Storage
Image Annotation Tools
Drag-and-Drop Pipeline Creation
Template Repository
Bioinformatics Support
R and Bash Support
Version Control for Models
(model tagging)

Web Application

ClearML offers a web UI equipped for specific parts of the ML process like experiment monitoring, hyperparameter tuning, and remote code execution, encouraging the use of third-party tools for various aspects of the workflow. A notable difference is the initiation process; operations in ClearML often start from the terminal, which might appeal to users who favor a more technical approach. However, this could introduce additional steps for those looking to manage their entire workflow within a single platform.

Coretex integrates the full spectrum of the ML workflow within its web application, making it a central hub for activities from data labeling to model deployment. This all-in-one approach means users can effortlessly transition between stages of their project without needing to switch tools or contexts. Importantly, Coretex supports interactions through both the web interface and the command line interface (CLI), ensuring flexibility for users who prefer either.

ClearML and Coretex run windows

CLI (Command Line Interface)

Operations in ClearML are often performed through the terminal or code, which may attract users who prefer to engage with their ML workflows programatically. While ClearML provides web UI support, its capabilities are suitable more towards specific tasks rather than covering the entire workflow spectrum.

Almost all operations in Coretex can be performed purely through the Web UI serving users who prefer a graphical interface. However, it maintains robust support for CLI interactions, ensuring that users who are more comfortable with scripting or require automation capabilities are well accommodated.

ClearML and Coretex cli configuration

Pipeline Creation

When it comes to creating Pipelines (or how we call them at Coretex - Workflows) ClearML adopts a more technical approach, strengthening users with the flexibility to construct pipelines using Python scripts. This approach provides a high degree of control over the pipeline configuration but may require a higher level of technical expertise.

Coretex introduces a unique approach to pipeline creation by offering an interactive visual interface. This feature enhances accessibility, enabling both amateur and advanced users to construct pipelines with much less effort and required knowledge. For those seeking granular control, Coretex provides an advanced editor, offering a bridge between simplicity and configurational detail.

ClearML and Coretex pipelines


Dataset upload in ClearML is performed through code or its CLI, without an upload functionality on its web application.

Coretex offers flexible data upload options, allowing users to upload their data through code or use the web app's intuitive drag-and-drop/select files feature.

ClearML and Coretex dataset creation

In terms of dataset management, ClearML, adopts a uniform, yet flexible, approach to datasets, not distinguishing between dataset types. It features a smaller array of visualization tools relative to the what is offered by Coretex.

In comparison, Coretex provides a structured approach by supporting various dataset types, each equipped with its unique features. These differences are reflected in both the code, through distinct dataset classes, and within the web UI, which offers specialized data display methods.

ClearML and Coretex dataset previews
Coretex IMU data

ClearML's dataset versioning works through its SDK, supporting the creation, management, and tracking of dataset iterations directly in code. Each version can be in two states: Draft and Published.

Coretex utilizes a tagging system for dataset management, allowing users to categorize and easily access datasets through descriptive labels. This system supports efficient organization and retrieval of datasets, accommodating complex projects and diverse data needs.

ClearML and Coretex dataset versioning

A notable feature shared by Coretex and ClearML is dataset lineage tracking, enabling users to trace the evolution and combination of datasets over time. This ability is crucial for knowing how datasets are related and how they change. It helps make machine learning projects more repeatable and easier to review.

ClearML and Coretex lineages

APIs for Model Deployment

ClearML offers a model serving feature which supports a wide range of popular ML frameworks (XGBoost, PyTorch, ONNX…). It features customizable RestAPIs with automatic scaling based on load. It provides online model updates, canary deployment strategies, and comprehensive metrics collection for monitoring model performance.

Coretex, on the other  hand, provides a built-in solution for model deployment via Coretex Endpoints. It allows users to deploy model APIs within the platform, handling both local and cloud environments. The process involves uploading a script that defines the model's inputs, logic for any kind of pre and post processing, and outputs, alongside the model files, making the deployed models readily accessible through exposed endpoints on cloud queue clusters for robust and easy inference.

Coretex endpoint

Cloud Computing

ClearML is provider-agnostic, allowing for flexibility across various cloud services and on-premise setups, ideal for teams looking for versatile scaling options.  Genesis Cloud powered, green, cloud GPUs are also offered as an option.

In contrast, Coretex currently only supports AWS from the big three cloud service providers. It uses cloud queues for dynamically managing experiments and the ability to incorporate users' machines into these queues, offering a blend of cloud and on-premise support.

Both platforms support Kubernetes, enabling efficient container orchestration.

ClearML and Coretex clouds

Code Templates

ClearML provides a variety of tutorials for users, focusing on educating and guiding through different aspects and functionalities of its platform. However, it presents a more limited selection of pre-made templates. This might require users to invest more time in setting up their projects from scratch or customizing existing resources to fit their specific needs.

By comparison, Coretex boasts a wide collection of easily modifiable, ready-to-use templates covering different areas such as generative AI, computer vision, and gene sequence analysis. This extensive template repository offers a head start in project initiation. It enables rapid deployment of projects and reduces the entry barrier for users of varying skill levels, making it easier for beginners and experts alike to leverage the platform's capabilities for their research or development projects. All of our templates are open source and available for use.

ClearML and Coretex code templates

Model Versioning

ClearML offers model versioning, allowing users to track and manage different versions of their models. This feature facilitates the organization of model development over time, allowing for efficient comparison, rollback, and evolution of models based on performance metrics or changes in data. By maintaining a structured version history, ClearML ensures that teams can easily access previous versions, enhancing replicability of machine learning workflows.

However, Coretex employs a tagging system to differentiate between model versions. This approach allows users to assign unique identifiers called Tags to specific model iterations, simplifying the process of categorizing and retrieving different versions. Tags in Coretex offer a flexible mechanism to track model evolution, enabling teams to mark and locate various stages of model development and deployment.

ClearML and Coretex models

Bioinformatics Support

ClearML, whilst capable of handling bioinformatics tasks, offers more generalized ML support. This broader approach means users might need to undertake additional steps to integrate specialized bioinformatics tools.

Coretex provides battle-tested bioinformatics support for gene sequence analysis through simple integration with tools like QIIME2 and BWA. This focus offers a tailored experience for bioinformatics professionals. For example, Coretex is used for postgraduate bioinformatics theses by the University of Zurich

Coretex bioinformatics

Language and Scripting Support

Coretex and ClearML both support Python, with Coretex extending its support to R, Jupyter Notebooks, Docker images, and Bash scripts. This allows Coretex to provide a broader range of programming environments and workflows, offering versatility for projects that might utilize different technologies.

Languages and Docker


While both Coretex and ClearML are best suited for small to mid-sized teams, ClearML, with its script based approach, is more suitable for teams with strong technical skills and a preference for detailed, hands-on control of their ML workflows.

Coretex’s integrated platform and user-friendly features make it ideal for a broader range of expertise levels, facilitating collaboration and efficiency across various project sizes, while still offering sophisticated capabilities to handle complex tasks.

Vuk Manojlović
May 21, 2024