As the new year approaches and you contemplate your current data struggles this past year, many look towards adopting or switching their current AI/ML data management tools. But how do you know where to start? What are your priorities, goals, and areas desperate for improvement? Starting with the right tool can help you accelerate the production of datasets and uplift your annotation process for success in 2023.
How do you achieve this? Read on, and we’ll let you in on 3 key components we feel are crucial in choosing the right annotation solution and that can easily help you manage your AI data projects.
Key Components for the Right Annotation Solution
1.EFFICIENCY VIA DEVELOPER FIRST TOOLS & INTERFACES
Problem: How can you alleviate the influx of data and render the best results?
As AI projects mature, more and more data gets pumped into the pipeline. It is often managed by different teams in different locations, moving between various tools along the ML development lifecycle. This results in slowing down the workflow at best, with vital information getting lost in the process. Additionally, transferring data across different platforms can become cumbersome. The need to constantly convert data formats in order to make the data readable to different tools can result in duplications which commonly occur when the data resides in multiple places. The metadata gets lost in translation, which is a valid pain point.
Function-as-a-Service empowers users to build, operate, and deploy custom automation functions within our Kubernetes-powered environment. The compute infrastructure is managed by Dataloop for minimum latency, max resource efficiency and unlimited auto-scaling.
2. END TO END THROUGHOUT YOUR AI JOURNEY
Problem: Finding a solution that can handle all stages of the model development cycle.
Solution: There are many tools in the market that answer parts of the ML model development pipeline, but NONE of them provide a full solution. You don’t want just another annotation tool; what you need is a comprehensive platform that supports all teams across all stages of the model development lifecycle, from early-stage research through the deployment of AI applications in production environments.
As teams begin their initial labeling process, Dataloop provides an array of AI-assisted annotation tools and automation features, made to accelerate the annotation process and increase dataset accuracy.
As the data activities mature, the labeling process needs to be streamlined and data teams need to consolidate larger datasets. At this stage, Dataloop provides powerful data management infrastructure and tooling, from dataset versioning, metadata indexing and querying, to cloud integrations and dockerized Kubernetes cluster for on-premise or private cloud deployments.
Teams deploying their AI systems in production or near production need more robust tooling in place to manage their end-to-end data pipelines and human-in-the-loop workflows. Dataloop is uniquely positioned to support production-ready teams, with integrated Python SDK, REST API, model integrations and Function-as-a-Service (FaaS) automation workflows, all combined to accelerate production pipelines.
3. FLEXIBLE & EXPANDABLE
Problem: Finding a solution for a wide variety of AI applications and different data types and expanding the platform into new use cases as the customers grow their AI product.
Solution: Built from the ground up as an operating system, Dataloop is an open and flexible platform that data teams utilize for building and running an unlimited variety of AI/ML applications. The platform supports a diversity of data types, annotation tools, and data applications, with the ability to support countless more via customization. Dataloop provides AI-assisted tooling in order to accelerate the data annotation process. We integrate smart automation models in order to reduce the labeling time for unstructured data (LiDAR, image, video, audio etc.) We have a wide range of AI-assisted automations such as auto-segmentation, automated object detection, and automated video tracking to name a few, enabling teams to speed up the labeling process.
Most importantly, all data displays and data output is fully customizable. The platform interface is adaptable, supporting unique applications such as similarity classification, multimodality, collection view and more, via a user-generated Apps. Built-in data output converters can be customized to support the export of any annotation format required, and can be consumed directly from the platform UI, REST API, CLI and Python SDK.
Managing your AI data projects can be accomplished far more efficiently and painlessly if you have the right tools at your disposal. As we explained we feel the three crucial points in this equation include having developer friendly tools for efficiency in designing the data flow, end to end support for all stages of AI model development, from early research stages to advanced development to full implementation in production environments. And finally, expandable tools to support a wide variety of AI applications, use cases and data types. If you’d like to learn more about how Dataloop can help set you up for success with your AI data projects for 2023, then be sure to book your demo today!