Introduction

ClearML is a very popular, open-source suite of tools to automate the preparation, execution and analyzing of Machine Learning (ML) experiments. Experiment management tools keep a track of jobs, parameters, artifacts, debug data, metrics, metadata, and then log it all in one clear interface. This is a heavy-duty tool that seems to have it all.

The tech industry heavyweights, such as Facebook, Microsoft, Samsung, Intel, Sony, Amazon, and Nvidia make use of ClearML to run their data science and ML operations. But is ClearML in fact the best choice?

What is ClearML?

ClearML removes the error-prone and time-consuming tasks associated with the entire ML lifecycle. Data scientists and ML developers can then concentrate on training and data only.

ClesrML everything we need to document – our work, visualize results, reproduce, tune, and compare experiments. Thus one can implement automated workflows, such as hyperparameter optimization and other pipelines. The Python-based package of ClearML supports:

• Libraries (Plotly, Pandas, AutoKeras).
• Frameworks such as TensorBoard/TensorFlow, Keras, PyTorch, Fastai, Scikit-learn.
• Visualization tools (Seaborn, Matplotlib).
• Storage (S3, file systems, Azure Storage, and Google Cloud Storage).

We can utilize the hosted service and implement a self-hosted ClearML server. This enhances ML development with explicit reporting, classes for experiments, workflow automation, optimization (HpBandster, Optuna, grid, random, and custom search strategies), storage, and models.

Features of ClearML

The three most prominent features of ClearML are:

• Feature Store
•  Orchestration,
• Deploy

Orchestration

ClearML Orchestrate offers data science teams control and autonomy over computing resources within one, simple dashboard. It manages cluster and resource allocation without using dedicated MLOps team members. The Orchestration tool has been developed to scale up to one’s specific needs, and it can scale dynamically to accommodate several GPUs. It extracts the workload from the infrastructure. One only has to do the initial set-up, and then the tool controls all the processes automatically.

Feature Store

ClearML’s Feature Store, including the automated pipelines, allows super speed for ML operations. One can build pipelines and experiment on structured as well as unstructured data in a matter of minutes. All you have to do is plug in the ClearML Feature Store and then add a simple way to iterate the features for ML experiments. The store will be integrated with just a couple of lines of code. The result can organize both structured and unstructured ML and DL operations. ClearML Feature Store manages all the work around ingesting structure and unstructured data from any source into the feature store. ClearML Orchestrate and ClearML Experiment combine with Feature Store to develop an integrated data feedback loop to re-train deployed models depending on real-world data.

 Deploy

ClearML can deploy a model to any environment and also offers complete control over the models. The Deploy module along with Orchestrate and Experiment completes the workflow that anyone can use. It provides a wide range of tools to deploy models. The target environment for the deployment can be modified with a single click. It manages all deployment operations, so that one can focus on training accurate models. We can run and deploy critical ML applications easily and repeatedly. The module provides complete visibility of model consumption, call details, and server used for any size deployment. Integrated with CI/CD, ClearML can scale flexibly and dynamically.

Conclusion

ClearML is an end-to-end MLOps suite which enables one to concentrate on developing the ML code and automation, while ClearML ensures that the work is scalable and reproducible.