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Framework to Evaluate a Data Labeling Partner for Machine Learning

Mothi Venkatesh
July 6, 2018

There are many challenges in building AI that works in the real-world scenarios. One of those is the quality of the data that is needed to train your model.Being in the AV industry, in order to stay at the top, machine learning models need to be trained on representative datasets that include all the needed all possible circumstances and possibilities of roads, weathers, traffic and road conditions and other situations.There are three steps commonly followed by companies around the world to build safer autonomous vehicles.

  1. Perception: To perceive vehicles and other smaller objects in the environment. This task can be accomplished using radars and cameras or LiDAR.
  2. Mapping: This task involves constructing high-definition (HD) maps. Captured data is (manually) analyzed to generate semantic data.
  3. Localisation: By identifying the environment and the exact position of objects in the surrounding, effective decisions can be made about where and how to navigate.

It is almost impossible to know how much data is needed for an algorithm to be trusted with road conditions. But, estimates from Waymo suggest a minimum of 3 million miles of live test drives, 1 billion miles of simulated test drives along with a disengagement rate of 0.2 per 1000 miles (on average a human had to intervene every 5000 miles). It's extremely important for self-driving cars to have an extremely high level of accuracy. Failing to have a high level of accuracy can prove to be life-threatening and fatal.This calls for a very good data labeling partner. In-house teams can be an alternative but the process can be really slow and the cost of asking employees to take out time for labeling can be huge and is not a very efficient investment of resources.So,

How to choose your data labeling partner for machine learning

How do you choose your data labeling partner?

Level 1:

  1. Annotation quality (in terms of Precision/Recall %) High-quality training data is all you need for building AI for the real world.
  2. Smarter annotation tools you may need bounding boxes, polygons or semantic segmentation masks for your data. Whatever image annotation type, how much complex it can be, we've got you covered.
  3. Trained workforce After working with 50+ enterprise clients, we realized the cost of time incurred in your development. Look for someone, who has the capacity to generate tonnes of data with assured quality. We own the army of 300K trained workforce who can handle millions of annotations in a day.

Level 2:

  1. Enterprise-grade SLAs when it comes to data sharing, and project requirements you must need SLAs. So, it definitely makes sense to onboard your vendor with standard SLAs.
  2. Classes we support we understand you require a deep understanding of images
  3. Pricing Our lean approach and product engineering by design made our service more viable for our customers
  4. Customer support Our experts are ready to serve 24x7 support for customer needs

Level 3:

  1. Data Security Crowdsourcing/Captive Workforce
  2. Ease of Data transfer we support APIs, CSV, FTP and anything you prefer
  3. Ease of results evaluation we provide an exclusive client dashboard to track all stats
  4. Additional value-added services unlimited free re-runs, and complete project management makes our customer's life much easier.

Outsource Smartly

Playment offers high-quality training and validation data to enable AI work in the real world. You can better understand how it's different from other traditional crowdsourcing to scale your training data needs.

Get your data labeling vendor evaluation form and choose wisely.