Trade show season has begun! Last week we were summiting the peaks of innovative research at AI Summit in San Francisco and this week we are high-fiving robots at Robo Business in Santa Clara. We love sponsoring these conferences because it is a great way to support and learn about the pioneering work happening in our industry. You can feel the buzz of the hive mind working on a shared vision of a world in which machines provide massive efficiencies and solve some of our more complex business problems. These conferences also continually demonstrate the demand that surrounds ML data labeling.
Teams have already experienced the hard earned lesson that overall performance of even the most sophisticated model can be easily compromised if it is trained on data that is poorly labeled or does not accurately reflect the target values. Overall, ML initiatives are becoming more complex, as use-cases become are more diverse, which makes high quality training data at scale a necessity.
Here are our 5 big takeaways from trade show season so far:
ML initiatives are becoming more complex
Machine learning initiatives have already solved a range of problems from recommending products to detecting spam. These have been so successful that teams started tackling more complex challenges such as detecting deep fakes and getting fully autonomous vehicles on the road. These projects have been in production for years and as advancements pick up speed they open new innovative applications, such as analyzing drone footage to check the structural integrity of wind farm propellers and tracking animal species in Serengeti National Park.
Use-cases are more diverse
Innovation begets innovation. As ideas for new ML initiatives become more complex, use cases also become more diverse. Each use case is by definition unique, so each requires training data to be configured to meet the project’s specific needs. On the back end, that means customizing techniques for each industry, tooling and sometimes re-tooling workflows to increase accuracy, and consistent adaptation as goals and parameters evolve.
High quality training data is essential
Training data quality determines the performance of machine learning systems, so at the end of the day your model will only be as good as the data on which it learns. If you put “garbage-in” you’re going to get “garbage-out.” Bad data can ravage a model, leading projects to be decommissioned, like Microsoft’s bot Tay.
Expediency is a priority
When models are ready for training, they usually need the data yesterday. Timing is everything. If your team is readying a model to go to market, your competition is as well. The faster you can train the model on high-quality data, the better your chances of differentiating your offering from competitors through increased revenue, cost savings, and more advanced product features.
Expediency is also tied to scale. To successfully scale efforts more and more must be done in the same time frame, which means you need a data strategy in place that can quickly and adequately respond to data demands. For more information on this topic, check out, How To Properly Prepare Your Data For Supervised Learning.
Data security requirements are high
There are two reasons for strict security requirements:
Protecting customer data - Platforms are the custodians of some of our most personal and important information, from our medical files to our banking information. The major digital security breaches of the past decade have not only been embarrassing but also costly and damaging to individuals, enterprises, and society at large.
Protecting proprietary innovations - Machine learning requires substantial upfront investment, with the promise of excellent ROI and potential market dominance.
These shared goals and concerns are the ammunition that fuel our own innovative approach to data labeling. They are the reason we have built the most flexible ML-assisted training data platform, assembled an on-demand workforce of trained data specialists, and attracted expert project managers that are able to meet your team where they are at to achieve your ML goals.
Our mission is to create shared value in a post-AI World. To our customers: by providing exceptional human and machine intelligence to solve large-scale business challenges. To our workforce: by providing opportunities for dignifying work and a path to personal improvement.
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