As increasing numbers of businesses discover various applications for artificial intelligence (AI) and machine learning (ML), data scientists are scrutinizing their workflows. The AI and ML development landscape involves numerous components that require efficient management to ensure both flexibility and robust functionality. The current challenge lies in determining which tools possess specific capabilities and how they can be integrated with other solutions to support a comprehensive workflow. Let’s explore the functionalities provided by some of the key tools in this field.
DVC is equipped to handle management of text, image, audio, and video files throughout the ML modeling process.
The pros: DVC is open source and features strong data management capabilities. It supports custom dataset enrichment and bias elimination, and it efficiently logs data modifications at natural transition points within the workflow. Command line operations are notably swift. Additionally, DVC’s pipeline features are language-independent.
The cons: The AI workflow features of DVC are somewhat restricted, lacking deployment and orchestration functions. Although its pipeline structure is theoretically appealing, it can often be unreliable in practical application. DVC does not provide a method to manage credentials for object storage via a configuration file, and it lacks a graphical user interface, necessitating that all operations be conducted through coding.
MLflow is an open-source tool, built on an MLOps platform.
The pros: Because it’s open source, it’s easy to set up, and requires only one install. It supports all ML libraries, languages, and code, including R. The platform is designed for end-to-end workflow support for modeling and generative AI tools. And its UI feels intuitive, as well as easy to understand and navigate.
The cons: MLflow’s AI workflow capacities are limited overall. There’s no orchestration functionality, limited data management, and limited deployment functionality. The user has to exercise diligence while organizing work and naming projects – the tool doesn’t support subfolders. It can track parameters, but doesn’t track all code changes – although Git Commit can provide the means for work-arounds. Users will often combine MLflow and DVC to force data change logging.
Weights & Biases is a solution primarily used for MLOPs. The company recently added a solution for developing generative AI tools.
The pros: Weights & Biases excels in automated tracking, versioning, and visualization with minimal code integration. It serves as an effective experiment management tool, providing top-notch interactive visualizations that simplify the analysis of experiments. It enhances team collaboration by enabling members to share experiments easily and gather insights to refine future projects. Moreover, it offers robust management of model registries, featuring comprehensive dashboards for monitoring models and reproducing any model checkpoint at will.
The cons: Weights & Biases isn’t an open-source platform. Lacking pipeline functionalities within its system, users must utilize additional tools like PyTorch and Kubernetes. Its AI workflow capabilities remain limited, particularly in areas of orchestration and scheduling. Although Weights & Biases can track all coding activities and changes, this functionality may inadvertently heighten security risks and increase storage costs. Furthermore, it does not provide detailed management of computing resources, necessitating the use of supplementary tools for more specific tasks.
Slurm promises to deliver workflow management and optimization on a large scale.
The pros: Slurm shines as an open-source platform equipped with a powerful and scalable scheduling system suitable for extensive computing clusters and high-performance computing (HPC) environments. It is engineered to maximize computing resources for demanding tasks in AI, HPC, and High Throughput Computing (HTC). It also provides real-time analytics on job profiling, budget allocations, and power consumption, which is vital for projects requiring substantial resources shared among various users. Customer support is readily available to assist with technical guidance and troubleshooting.
The cons: Scheduling is the only piece of AI workflow that Slurm solves. It requires a significant amount of Bash scripting to build automations or pipelines. It can’t boot up different environments for each job, and can’t verify all data connections and drivers are valid. There’s no visibility into Slurm clusters in progress. Furthermore, its scalability comes at the cost of user control over resource allocation. Jobs that exceed memory quotas or simply take too long are killed with no advance warning.
ClearML offers scalability and efficiency across the entire AI workflow, on a single open source platform.
The pros: ClearML’s platform is built to provide end-to-end workflow solutions for GenAI, LLMops, and MLOps at scale. For a solution to truly be called “end-to-end,” it must be built to support workflow for a wide range of businesses with different needs. It must be able to replace multiple stand-alone tools used for AI/ML, but still allow developers to customize its functionality by adding additional tools of their choice, which ClearML does. ClearML also offers out-of-the-box orchestration to support scheduling, queues, and GPU management. To develop and optimize AI and ML models within ClearML, only two lines of code are required. Unlike some other leading workflow solutions, ClearML is open source, creates an audit trail of changes, automatically tracking elements data scientists rarely think about – config, settings, etc. – and offering comparisons. Its dataset management functionality connects seamlessly with experiment management. The platform also enables organized, detailed data management, permissions and role-based access control, and sub-directories for sub-experiments, making oversight more efficient.
One important advantage ClearML brings to data teams is its security measures, which are built into the platform. Security is no place to slack, especially while optimizing workflow to manage larger volumes of sensitive data. It’s crucial for developers to trust their data is private and secure, while accessible to those on the data team who need it.
The cons: While being designed by developers, for developers, has its advantages, ClearML’s model deployment is done not through a UI but through code. Naming conventions for tracking and updating data can be inconsistent across the platform. For instance, the user will “report” parameters and metrics, but “register” or “update” a model. And it does not support R, only Python.
In conclusion, the field of AI/ML workflow solutions is a crowded one, and it’s only going to grow from here. Data scientists should take the time today to learn about what’s available to them, given their teams’ specific needs and resources.
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