Why Your Internally Built AI Tool Isnt Enough

Why Your Internally Built AI Tool Isn’t Enough

We’ve seen it time and time again where organizations that begin with tools built in-house often discover they tend to build for those early-stage purposes. The problem starts arising when your organization starts scaling and this is when you need to assess whether your existing in-house tooling still suits your needs. 

We’ve seen this pattern where AI projects strive for the cheap path, spending 2-3 years before they actually acknowledge the true costs. While some projects are successful by developing toolchains and solutions, the other 99% move one step closer to failure by attempting to go all in-house. The facts are clear, according to Gartner, 85% of AI projects fail due to unclear objectives and obscure R&D project management processes. Additionally, 87% of AI projects never even reach the production phase, and 70% of clients have acknowledged they’ve had minimal or even no impact from AI. Does this sound familiar? Well, there’s good news, there is something you can do about it.

Focus On Core Business & Not Platform Maintenance

Many AI developers begin by building their own tools for data annotation, only to realize later in the process the undeniable resources that are required to develop and maintain such tooling. This means that countless engineering hours are spent on building a technology which is not their core business, but simply a means to an end.

By implementing Dataloop, AI and ML development teams can invest their time and resources on doing what they came to do – develop high-performance AI/ML applications. Dataloop allows data teams to focus on their core development, automated scalability, top performance, and secure access across the organization.

Achieving Scalability and Efficiency in Data Annotation with Dataloop

AI models require a continuous feed of high-quality data in order to maintain their confidence levels. This means constantly growing the human workforce in order to keep products’ existing accuracy standards – a constant uphill battle.

To support teams requiring large volumes of annotated data, sometimes with hundreds or even thousands of annotated objects per image, Dataloop employs a combination of manual and automated workflows which ensure dataset production is made with maximum efficiency and scalability, but with minimal room for human error. These include built-in and customizable workforce management systems, made for distributing tasks and assignments among thousands of users. These are combined with automated QA and QC workflows, using integrated models to detect anomalies and lack of consensus among labelers.

pipeline3 updated

The platform is designed to support globally distributed workforces who work in parallel and at scale, with real-time communication and AI-Assisted validation tools at their service.

Dataloop’s platform is also highly flexible, supporting integrations with a wide variety of apps and data sources. This means that data can be easily imported from different sources and different formats, such as images, video, audio, lidar, and text, and then processed and annotated within the platform. This allows teams to take advantage of the platform’s efficient and scalable workflow management systems, while also being able to use the data they already have, or easily import new data as needed.

The platform’s wide variety of annotation tools also makes it well-suited for a wide range of use cases, whether it be object detection, semantic segmentation, or natural language processing. This allows teams to use the platform for a variety of tasks, from training object detection models for self-driving cars, to training natural language processing models for chatbots.

Overall, Dataloop’s platform provides a powerful and flexible solution for managing the process of data annotation, from managing a large and globally distributed workforce, to automating quality control and validation processes, and integrating with a wide variety of apps and data sources. This helps to ensure that the data used to train AI models is of the highest quality, and is constantly updated and improved to keep the models working at their best.

As your AI models rely on high-quality annotated data to maintain their accuracy, keeping up with increasing demand can become an expensive challenge. Dataloop’s platform offers cost-effective solution that allows you to scale your annotation process and still maintain high standards.

Expertise in Managing High-Volume and Complex Datasets for AI and ML Development

Dataloop serves AI/ML development teams across dozens of industries – including autonomous vehicles, precision agriculture, smart retail, medical imaging, aerial imaging, robotics and more – starting from early-stage startups to Fortune 500 enterprises.

Having gained years of experience in leading AI/ML developments, Dataloop excels in managing high volumes, high variance, and complex datasets for AI and ML developments. Furthermore, its ever-growing developer community forms a unique knowledge base that together with their internal ML experts, can guide you through your entire AI development journey.

In terms of data management, Dataloop offers a range of advanced capabilities to help you organize, process and analyze data effectively. Dataloop’s data management platform makes it easy for you to access, store, and manage large amounts of data, giving you the insights you need to improve your AI and ML models. Additionally, it provides the ability to store, curate, and share data across teams, locations and organizations, making collaboration seamless and efficient.

Data Management

A key advantage of our data management tool is its ability to efficiently query large datasets using a user-friendly query language – JSON format, which is easy to understand and apply. Our platform also provides the capability to duplicate metadata for improved utilization and tracking of data versions, it also allows you to link data streams to pipelines and functions to run Dataloop or user-designed applications with ease. 

Overall, Dataloop can help you streamline your AI development process and accelerate time-to-market.

Summed Up

Many organizations begin their journey by building their own tools for data annotation, but later realize the resources required to develop and maintain such tooling. This can lead to many engineering hours being spent on building technology that is not the organization’s core business, rather than developing high-performance AI and ML applications. 

With Dataloop, AI and ML development teams can invest their time and resources on their core business, while ensuring automated scalability, top performance, and secure access across the organization and can improve their chances of success. 

Most projects fail to reach production, but with Dataloop you can focus on pushing your AI/ML projects to production readiness instead of wasting time and engineering resources on maintaining tools that are irrelevant to your customers and core business. 

It is important to keep in mind that Dataloop is an end-to-end solution that is capable of managing data pipelines for all stages of the AI development lifecycle, from early research to development to production. Meaning, that this is a platform that helps you grow your AI products and capabilities without the hassle of rebuilding your infrastructure to fit new needs that come with maturity.

If you’d like to learn more about Dataloop, and have a personalized 1:1 demo, then make sure to book today!

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