Understand and Evaluate Training Data Platform for AI-focused Companies

This article provides a high-level overview of what a TDP is and what to look for in evaluating training data solutions to best suit your needs.

Understand and Evaluate Training Data Platform for AI-focused Companies
Understand and Evaluate Training Data Platform for AI-focused Companies

Data science teams spend a disproportionate amount of their time processing, labelling and augmenting training data. Training data platforms can help free up time so they can focus on building the actual structures which they were tasked to create.

In this article you’ll learn:

  • What are the common features of a training data platform?
  • How does a training data platform improve data labelling productivity?
  • What is the ROI on a training data platform?
  • How do you know when it’s time to invest in a training data platform?

Table of contents

Overview
What is the Training Data Platform?
What It Is Not
Framework
Is it time to invest in a Training Data Platform?
How does a Training Data Platform improve data labelling productivity?
Who Can Benefit Most From a Training Data Platform?
What are Common Features of a TDP?
Business Value and ROI
How Can I Maximize My Success with a TDP?
Conclusion

Overview

There is a tremendous amount of data being generated around the world today. The task of collecting relevant information, organizing it, and piecing it together to make accurate predictions seems like a monumental task, but this is the everyday job of data science and machine learning teams.

In recent years, teams have capitalized on technological advancements in artificial intelligence and are making it possible for companies to deploy deep learning technology across nearly every industry, particularly in the fields of computer vision and natural language processing where cameras or the human eye were traditionally involved in making decisions. The trend towards broad business adoption has been accelerated by the emergence of enabling technologies such as open-source machine learning models and access to cheap processing power.

What stands in the way of AI realizing its full potential are the costs and uncertainties associated with creating high-quality training data, a process known as training data management and commonly achieved with the help of in-house teams and external data labelling service vendors. The most frequent pain points include getting a clear estimate of what the ongoing costs will be for this data and having greater transparency into the quality being created.

In supervised learning, working closely with these internal and external labelling teams to take raw data and transform it into useful training data is commonly arduous and resource-intensive due to the back and forth required. As a solution, an interface that enables all of this supervision and training to occur gives data scientists the time to focus on modelling, design and evaluation, as opposed to building training data infrastructure themselves.

A recent survey of 500 companies by the firm Algorithmia found that data science teams spend less than a quarter of their time training and iterating machine-learning models, which is their primary job function. Instead, a disproportionate amount of their time is spent processing, labelling and augmenting training data to suit their needs. There is a strong demand for dedicated software to free up data scientists to spend more of their time building the actual structures which they were tasked to create. Ultimately, the emergence of these types of platforms will make teams more efficient and allow AI to be within reach of both enterprises and fast-growth startups that see AI as part of their competitive differentiation.

What is the Training Data Platform?

A training data platform, abbreviated as TDP, helps companies manage their training data workflows so that data science teams can work efficiently with annotation teams both internally and externally. The primary benefits of a TDP include giving organizations the ability to create and track their labelled data and to optimize the quality of their training data before feeding it into their machine learning models.

A comprehensive TDP provides organizations with a 360-degree view of their training data. The TDP functions not merely as a tool, but helps to define a new way of working that enables companies to become the most effective with their training data efforts. This is typically accomplished by supplying users with a convenient interface to handle common workflows such as project creation, ontology set up, and the labelling operation itself. In short, the primary objective of a TDP is to help standardize ML workflows in a similar way to what platforms like Salesforce and Hubspot have done for managing customer relationships.

In recent years, TDPs have evolved to become enterprise-grade products that include customizable labelling interfaces, deep API access, and strong security controls. One of the core features of a TDP is workforce access management. This suite of features gives users control of their training data by allowing managers to coordinate any number of labelling teams, across both full-time and outsourced staff, all in one platform. The business benefits of a TDP are multi-fold and include: (1) reduced project times and faster model iteration via enhanced collaboration among teams, (2) higher performance AI applications due to the quality of the training data created, and (3) greater risk management with access control and analytics.

What It Is Not

While the market for managing and labelling training data continues to evolve, the vast majority of labelling offerings available in the market today focus on providing human labelling services. Typically these solutions are offered by, or work in conjunction with, Business Process Outsourcing firms (BPOs). To this extent, AI research firm Cognalytica has observed that “raw labour is easy to come by, but the assurance of quality is not easy to guarantee.”

With the challenge of quality in mind, a training data platform such as Labelbox is primarily focused on providing a software-first approach to managing and facilitating the monitoring, management, and improvement of the model as it’s operating, thus moving beyond the mere labelling and instead of providing a complete feedback loop on model performance.

Additionally, training data platforms do not conventionally manage the modelling process, which includes tuning model parameters, feature selection, and deployment.

Framework

Data science leaders are facing the challenge of creating and managing a continuous, iterative improvement process for their production AI applications which all ties back to training data. Before being fed into an ML framework, data has to be aggregated, transformed, cleaned, augmented, and – in most cases – labelled. This process consumes roughly 80% of resources in an average ML project, far exceeding other categories like algorithm development, model training, and deployment. Data preparation, in other words, is the engine powering modern AI and ML.

Turning raw data into accurate and consistent training data is a team effort. Engineers, domain experts (labellers), product leaders, and managers must work together while playing different roles. Workflows must facilitate this by providing information and interfaces unique to these roles.

This requires a TDP rather than just labelling services because a world-class data labelling interface will be able to accommodate the myriad ontologies and data features (as well as iterative changes to it) and present it to labellers so that they can be the most efficient with their workflows.

As the chart below demonstrates, taking supervised learning into production AI is a continuous process similar to DevOps, with workflows that emphasize consistent testing, deployment and iteration.

Taking supervised learning into production AI is a continuous process similar to DevOps, with workflows that emphasize consistent testing, deployment and iteration.
Taking supervised learning into production AI is a continuous process similar to DevOps, with workflows that emphasize consistent testing, deployment and iteration.

Is it time to invest in a Training Data Platform?

Here are some of the telltale signs of when investing in a Training Data Platform could benefit your organization:

Your data science team is spending valuable time building and maintaining training data infrastructure. As is often the case with key business infrastructure, there are hidden costs of the building. Buying a solution might look more expensive upfront, but it is often cheaper in the long run.

You want to move to more secure and reliable workflows. A TDP is able to support both cloud-hosted environments and on-premises solutions to equip data labellers to do their jobs securely, with a focus on requirements such as encryption at rest with AES-256 and Auth0 for authentication.

You are seeing that scaling your labelled data efforts are becoming a bottleneck. A TDP helps serve as the interface between AI systems and the domain experts that make these systems function. For many organizations, it’s important to consider a TDP when your team is looking to significantly scale out your data labelling in the coming months.

You want to diversify your training data labelling services. With a TDP, you gain full visibility and get granular insight into the performance of labelling teams, freeing you from dependence on any one vendor of labelling services.

You have multiple ML teams in your organizations as a shared service. A TDP basically acts as a single source of truth for defining, storing, and accessing training data across an entire organization.

You want greater visibility into preventing bias in your AI product. A TDP can help users avoid bias as blind spots or unconscious preferences in the project team or training data can be seen and corrected directly in the platform.

How does a Training Data Platform improve data labelling productivity?

When evaluating a TDP, some of the primary benefits to your ML team can be assessed along with the following criteria:

Collaboration. One of the critical features of a TDP is the ability to allow engineers, product managers and labellers to work collaboratively in the same platform which speeds up labelling iteration time.

Training Data quality. For any TDP, tools like reviewing, consensus and benchmarks (Golden Set) allow customers to create high-quality training data and manage labelling productivity.

Model Integration. A TDP provides customers with ways to setup model-assisted labelling to reduce labelling costs.

Extensibility. Custom labelling interfaces enable TDP customers to support any kind of labelling tasks (text, video, audio, point clouds) with the use of extensible SDKs and APIs.

Who Can Benefit Most From a Training Data Platform?

Organizations investing in AI are equipping themselves to face the need for high-quality training data in order to build performant models and applications. Below are the specific types of team members that can derive outsized benefits when adopting a training data platform:

For AI product managers, you’ll be empowered by the ability to more quickly take your app into production as well as maintain the application by allowing for seamless collaboration with your team, model and integrated data labelling service(s).

For data scientists, you’ll be able to see whether labelled data (which is the source code for your ML model) is high-quality and whether it reflects the business objectives.

For business leaders, you’ll get more accountability of AI investment to key business priorities so you can correlate ML projects to business value.

For a head of labelling operations, you’ll be able to measure labelling efficiency and performance to optimize your workflows.

For labelling teams, you’ll get access to the fastest and most intuitive tools for labelling training data.

What are Common Features of a TDP?

In terms of functionality, here is a representative list of features that are most often used and included in a TDP in order to enable the creation and management of training data:

  1. Fast and configurable annotation tools to support all types of labelling (e.g., polygon, rectangle, line, and point segmentation, as well as pixel-wise annotation).
  2. Real-time labelling queuing to enable the management of hundreds or thousands of labellers all working in parallel. These systems should support Active Learning as well.
  3. Easy uploading via raw data or link uploads which can scale to support millions of pieces of data.
  4. Role and permission-based access to conveniently move users in and out of your projects and organization.
  5. Reporting tools that generate automatic reports for label consensus when multiple labellers annotate the same asset.
  6. Workflows around measuring and improving dataset quality including but not limited to custom review queues and QA & QC benchmarks.
  7. Flexible editor tools to fit with data structure (ontology) requirements (e.g., custom attributes, hierarchical relationships, infinite nesting)
  8. Extensible to data types (e.g., images, video, text, audio, tiled imagery etc)
  9. Privacy and security – Support for on-premises or private cloud.
  10. API support which allows teams to operate fully automated human in the loop machine learning pipelines.

Business Value and ROI

As data labelling needs scale, data management and quality assurance processes are needed to produce accurate and consistent training data. A common cause of underperforming AI systems is low-quality training data which is the reason why companies adopt a TDP.

A TDP helps teams save months in custom R&D given that it can be difficult to accurately define the scope and construct a solution for needs across engineering and product groups. Building dedicated software to handle one of the most time-intensive parts of their workflow means spending less time on infrastructure planning, resource allocation, and preparing for the unknown. In the absence of a TDP, companies find that their internal tools are generally not built for usability, scalability, or cross-team support.

In addition to reducing costs, organizations develop higher-quality training data and are able to deploy applications with this data in nearly all industries with the assistance of a TDP. A few specific real-world use case examples include:

Agriculture technology companies like John Deere use a TDP to label images of individual plants, so that smart tractors can spot weeds and deliver pesticide precisely, saving money and sparing the environment unnecessary chemicals.

Dual-cam dash cameras are used by transportation and trucking industry leaders to record critical events such as accidents or evasive manoeuvres. These companies use a TDP to build models that identify important patterns such as driver distraction and causes of front-facing accidents.

For the largest insurances companies in the world, a TDP can be used to build computer vision applications and understand the quality of the structure and other key attributes about any address on the planet, given that large roof and overall home outdoor inspection is slow and costly.

How Can I Maximize My Success with a TDP?

Creating accurate and consistent training data requires a TDP that enables your cross-functional team of engineers, labellers and managers to collaborate effectively. When evaluating any solution, here are a few key considerations to prioritize:

  • What is the stability and reliability of the platform?
  • What is configurable without additional coding and how hard is it to maintain on an ongoing basis in terms of maintenance and cost of ownership?
  • Is it intuitive to use and are the labelling tools user friendly?
  • How well supported is the software on an ongoing basis?
  • Does it meet enterprise needs with regards to security, privacy and compliance?

Conclusion

Companies are making large-scale investments in AI with the goal of leveraging these sets of technologies to solve complex business problems and deliver differentiated experiences for end-customers. The majority of AI systems learn by example. It stands to reason that the higher quality of examples, the better the applications will be at turning pixels into “meaning”. A central piece of infrastructure is needed to facilitate the management of all of this training data, which in turn serves as the source code for AI applications. It’s increasingly important for companies to move deliberately and to consider whether a TDP will help their team become more successful with their AI initiatives.

Source: Labelbox

Published by Tommy Droste

, Windows Insider MVP, MCP, MCITP EA and SA, has almost six years technical writing experience. He is now the author of Pupuweb Blog. Before working as a writer, He was a technical support helping people to solve their computer problems. He enjoys providing solutions to computer problems and loves exploring new technologies.