From Data Annotators To AI Data Developers

There's an ongoing debate between the use of off-the-shelf data to create advanced artificial intelligence-based solutions for business. However, off-the-shelf training data sets could be the ideal solution for companies that don't have a dedicated in-house team of engineers, data scientists and annotators with them.

Even if companies have teams to handle massive ML implementations, they often struggle to collect the top-quality data needed to run the model.

Additionally the speed of creation and deployment is crucial to get a competitive edge in the marketplace, requiring businesses to rely on off-the-shelf data. Let's look at off-theshelf store data and then look at the advantages and drawbacks prior to making a decision to use these.

In the GTS MLDataOps Summit 2022, Avi Yashar, co-founder and CPO at Dataloop, Chris Karlin, the Head of Sales for Superb AI, and Michael Hazard who is the product manager for Applied Intuition unveil what makes an enterprise-grade tool, the reasons data tools require super-users realizing that there isn't one tool that is tool for all, and what's to come in the world of data tools. As GTS Vice President of Strategic Business Development, Jai Natarajan moderates the discussion, participants get the following information:

• Data workflows that are collaborative and a skilled tooling workforce are essential to create top-quality AI training data
• Although industry leaders usually utilize actual data for their decisions, it's crucial to comprehend the ways that synthetic data could be used to complement.
• AI Annotation Service are becoming experts in data, while data tools companies are working to avoid quality issues.

High Availability of System Stability

Based on Dataloop's Avi Yasnar, over the coming two to five years, numerous companies will take their AI solutions beyond the initial research phase into production. So, having a system that can scale easily without issues with quality is vital. Additionally, companies are always looking for custom options for their workflows by using their skilled workforce to ensure that their data is of high quality using the platform. Following strictly SLAs is an essential component of bringing a product to enterprise-grade.

Working with Customers of a larger size

Data tooling companies are working with larger organizations in the market, it's becoming standard to have the need to implement safety procedures in addition to the technology. As per Michael Hazard at Applied Intuition the technology must be tested and validated, and it must be demonstrated to correspond to the actual world. This is why investing in tools businesses worth it.

Collaboration is also becoming more essential in larger organizations. There are specific tasks assigned to specific people instead of using a multi-tasking jack-of-all-trades. The ability to transfer issues across the workflow, from the people who spot the problem to those who investigate and fix the issue is essential to scale up.

What are Off-the-Shelf Datasets?

A training dataset that is available off the shelf is an ideal option for companies seeking to rapidly build and deploy AI solutions, even if they don't have the time or resources to develop custom data.

Off-the-shelf training data like the name implies is a set of data which has already been gathered cleaned, categorized and is now ready for use. While the importance of custom data is unquestionable but the most suitable alternative is to use an off-the-shelf database.

Where does this tie in?

Jai Natarajan follows up the discussion by asking how series A firms manage to balance their many demands of their large customer's needs. Three panel members jump into the discussion to share their views that can be summed into the following paragraphs:

Be aware of your market

If you're looking to justify every cent you spend and incorporate everything in a long-term plan it is essential to know your customers very well know your customers, and be confident in the value you'll be able to provide. It's not enough to just know what you're doing now and you should also think about what's to come in the following phase.

Allowing different flows of users

The type of business you're working with the design may differ. For example working with an expert Video Data Annotation, such as GTS is a different experience from working with an engineer or data scientist who would like to utilize their software.

Different stakeholders are attracted to certain characteristics of a tool. Being able to create an application that is used by various types of stakeholders is what sets a data tools firm apart from other companies that cannot or won't use enterprise-grade clients.

From Data Annotators to Data Developers

Avi explains his thought process on how data analysts will move into a position that involves data development. Like software developers, data developers could end up developing pipelines since they have an extensive knowledge of data.

It is the next stage that deep learning will enter. The data scientists will be the main players over the next 2-5 years.

Moving forward with high-quality AI Training Data

In the case of creating top-quality AI learning data using an established and reliable data annotation firm is an ideal initial step. As a leading company in GTS, the company offers a solution that combines technology, expertise and methods to deliver quality data with precision in the production scale needed.