At first glance, ClearML’s AI Development Center and alternatives such as Weights & Biases seem to offer similar capabilities for MLOps. For example, both solutions support experiment management, data management, and orchestration. However, each product is designed to solve a different use case. It is important to understand how these approaches affect the user experience.
Built for Different Use Cases
Weights & Biases’ solutions were designed to track researchers’ progress towards better models. They were not built with automation, data protection/governance, or compute optimization in mind. They are first and foremost a dashboard for model performance graphs, and we find that they are very good for that use case.
ClearML’s AI Platform, on the other hand, was designed with automation, governance, and compute optimization. As a company, our goal is to accelerate the adoption of AI development and deployment through automation and interface creation, and enable the full AI team, from the researchers to the engineers, to productize AI capabilities. That’s why our AI Platform enables companies to build and develop AI models that run within their products at scale.
Functionally, ClearML’s AI Platform includes robust functionality for developing AI research, (our AI Development Center), as well as managing and optimizing AI computing clusters (our AI Infrastructure Control Plane), which makes it a much broader solution than Weights & Biases.
What Distinguishes ClearML from the Competition
Automation that enables CI/CD and more efficient AI development
One of our most widely used features is Pipelines. On top of simply connecting a sequence of steps, ClearML lets AI Builders create graph- and logic-driven pipelines that can include steps related to orchestration and integrated external processes. For example, creating true CI/CD through feeding back new data for model training, accessing GitOps, or monitoring model performance. Each step within a pipeline is logged and cached, making it possible to reuse previously processed data without needing to repeat the cleansing or filtering process. You can find out more about Pipelines here.
Advanced compute utilization management
To help organizations better use existing compute resources, ClearML offers functionality to better manage resource allocation and ensure jobs are always flowing. We also provide mechanisms to extract more value from each GPU chip itself. For example, AI infrastructure owners can use the Resource Allocation Policy Manager to enable access to specific compute resources based on hierarchical priority while setting quotas and over-quota logic per team to ensure resources do not sit idle unnecessarily.
ClearML also supports dynamic fractional GPUs for virtually partitioning any NVIDIA GPU (MIG-enabled or not), whether it is running Kubernetes or just bare metal. Our dynamic fractional GPU capabilities automatically handle right-sizing the compute made available for each workload, which can increase throughput significantly.
Support for private shared computing
Large organizations with centrally managed or shared computing will benefit from ClearML’s AI Infrastructure Control Plane, which supports secure multi-tenancy that provides each tenant with private computing. Tenants are within fully isolated networks with no visibility or access into the main shared computing environment, ensuring the security and confidentiality for all users. ClearML also supports account- or user-level usage reporting for tracking computing hours, data storage, API calls, and other metrics, which can be integrated into the customer’s billing system. For more information on how ClearML approaches multi-tenancy, read our previous blog post about it.
Tools that facilitate LLM deployments
ClearML has in-platform apps that make it easier to test and launch LLMs. ClearML enables AI builders to easily spin up Gradio and Streamlit™ apps within the platform for any workflows needed, such as enabling stakeholders to test the accuracy of the LLM. This allows the team to secure the feedback needed from the subject matter experts themselves.
AI teams can also launch an LLM or embedding model that has been built with ClearML or an off-the-shelf-model from Hugging Face from CLI or directly from the ClearML web UI. Active model endpoints are displayed and logged, enabling teams to monitor which models are sharing data.
Security that controls access to data, models, and compute resources
Built with security capabilities that meet the rigors of enterprises, governments, and defense organizations, ClearML offers granular control over every user. Our role-based access control not only manages a user’s access to data sources and models, but also applies to projects (and sub-projects) and the queues they can use for running their workloads. The permissions can be further extended to control the user’s engagement with a LLM by restricting the data made available to the LLM for its response. Customers can easily integrate ClearML with their preferred SSO authentication provider and LDAP directory.
Specialized data handling for unstructured data
By using meta-data to control and manipulate datasets, ClearML makes creating AI models with large unstructured data (such as images, videos, audio, or text) easy and accessible from anywhere. Our Hyper-Datasets functionality lets AI builders explore their data with sampling and previews and create custom views over intersections of queries directly connected to models in development. Users can generate statistics based on their data annotations and make changes to individual frames.
Future-proofed architecture
Last but not least, ClearML’s fully open source, modular design is a key differentiator from Weights & Biases’ solution. ClearML is hardware-, cloud-, and silicon-agnostic, offering out-of-the-box compatibility with complex infrastructures, multiple cloud providers, and new chip manufacturers. Successful AI teams can scale quickly and frictionlessly on top of the ClearML architecture. For organizations focused on security, ClearML can also be deployed into an air-gapped environment with limited or no internet connectivity.
Making the Right Choice
When it comes to deciding on a platform for developing and deploying AI, consider your main stakeholders’s use case(s), the maintenance requirements, how the platform could impact other areas of the organization (such as engineering or devops), and how scalable it is for wider adoption within your organization. The market abounds with AI solutions created for different use cases. If you would like to speak with someone on our sales team about how the ClearML AI Platform helps you materialize value from your AI investments faster, please request a demo.