With businesses uncovering more and more use cases for artificial intelligence and machine learning, data scientists find themselves looking closely at their workflow. There are a myriad of moving pieces in AI and ML development, and they all must be managed with an eye on efficiency and flexible, strong functionality. The challenge now is to evaluate what tools provide which functionalities, and how various tools can be augmented with other solutions to support an end-to-end workflow. So let’s see what some of these leading tools can do.

DVC

DVC offers the capability to manage text, image, audio, and video files across ML modeling workflow. 

The pros: It’s open source, and it has solid data management capacities. It offers custom dataset enrichment and bias removal. It also logs changes in the data quickly, at natural points during the workflow. While you’re using the command line, the process feels quick. And DVC’s pipeline capabilities are language-agnostic.

The cons: DVC’s AI workflow capabilities are limited – there’s no deployment functionality or orchestration. While the pipeline design looks good in theory, it tends to break in practice. There’s no ability to set credentials for object storage as a configuration file, and there’s no UI – everything must be done through code.

MLflow

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

Weights & Biases is a solution primarily used for MLOPs. The company recently added a solution for developing generative AI tools. 

The pros: Weights & Biases offers automated tracking, versioning, and visualization with minimal code. As an experiment management tool, it does excellent work. Its interactive visualizations make experiment analysis easy. Collaboration functions allow teams to efficiently share experiments and collect feedback for improving future experiments. And it offers strong model registry management, with dashboards for model monitoring and the ability to reproduce any model checkpoint. 

The cons: Weights & Biases is not open source. There are no pipeline capabilities within its own platform – users will need to turn to PyTorch and Kubernetes for that. Its AI workflow capabilities, including orchestration and scheduling functions, are quite limited. While Weights & Biases can log all code and code changes, that function can simultaneously create unnecessary security risks and drive up the cost of storage. Weights & Biases lacks the abilities to manage compute resources at a granular level. For granular tasks, users need to augment it with other tools or systems.

Slurm

Slurm promises workflow management and optimization at scale. 

The pros: Slurm is an open source solution, with a robust and highly scalable scheduling tool for large computing clusters and high-performance computing (HPC) environments. It’s designed to optimize compute resources for resource-intensive AI, HPC, and HTC (High Throughput Computing) tasks. And it delivers real-time reports on job profiling, budgets, and power consumption for resources needed by multiple users. It also comes with customer support for 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  

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. Like some of the other leading workflow solutions, ClearML is open source. Unlike some of the others, ClearML 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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