To build successful AI models that can impact the way we do business, we need AI systems requiring thorough training before performing their intended functions.
This is especially true with demanding our AI models to make human-like judgments about images and videos.
For our AI/ML models to be competent enough to make near-human-like interpretations and predictions — we need to expose our AI/ML models to enormous volumes of accurately labeled and annotated training data.
As we need to build enterprise solutions and services out of AI, our data science teams are under tremendous pressure to deliver projects but frequently are challenged to produce training data at the required scale and quality.
This post looks at the challenges faced by enterprises while getting their AI/ML projects off the ground and tackling foundational problems.
AI/ML modeling realities are laid out bare
Dimensional Research and AIegion surveyed to understand the ground reality of AI/ML projects at enterprises. The survey respondents highlight four truths.1 - enterprise machine learning is developing, 2 - data science teams are still small, 3 - growing data science expertise is not yet matched with equally mature ML project expertise, 4 - and training data challenges pose broad challenges to project success.
96% of respondents reported that their lack of training-data technology and skills had impeded their ability to train their ML algorithms and attain the confidence their model must provide.
An MIT Sloan Management Review quips that organizations with an excess of 100,000 employees are the only ones to have an AI strategy in place. But the reality is that only 50% of such enterprises currently have an AI strategy.
The Dimensional Research and AIegion survey further throw light on how nascent AI is:
- 70% report that their first AI/ML investment was within the last 24 months.
- Over 50% report they have undertaken fewer than four AI and ML projects.
- Only 50% have released AI/ML projects into production.
Only over two-thirds of enterprises indicate that they are at the stage of training and labeling data for their ML projects.
More enterprises reveal that their ML projects fail to move forward as they struggle to get the processes established in the first place. Survey respondents say that 78% of their AI/ML projects stall at some stage before deployment.
81% admit the process of training AI with data is more complicated than they expected. And that 76% combat this challenge by attempting to label and annotate training data on their own.
The survey also highlights that a remarkable 40% of projects stalled at the intensive data training phases.
Why is training data for ML modeling so difficult?
- Bias or errors in the data.
- Not enough data.
- Data not in a usable form.
- Don’t have the people to label data.
- Don’t have the tools to label the data.
These are the hurdles the survey respondents face when building an AI/ML model.
Nearly 75% of the respondents say that they're attempting to label and annotate training data independently.
And a little over 40% suggested that they're relying in whole or in part on off-the-shelf, pre-labeled data.
There is a clear indication to seek external partnerships and vendors to get data trained and prepared.
70% of survey respondents have utilized external services for their AI/ML projects, mainly focusing on data collection and labeling.
As expertise and skills are so rare and expensive if available, enterprises are slowly shifting their focus on partners and experts who can help them solve some of the fundamental AI/ML modeling problems.
Surely, enterprises don't want to put their ambitious AI/ML projects on the back-burner due to their inability to find solutions to foundational problems in-house.
The data provides evidence that outsourcing leads to improved outcomes.
That's why we built Traindata Inc. to help enterprises prepare data through labeling, annotation, structuring, and cleaning on time and budget.
Talk to us about your AL/ML data training challenges today, or visit www.traindata.us to learn more.