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Companies with the best training data produce the best performing models. AI industry leaders like Andrew Ng have recently emerged as major proponents of data-centric machine learning for enterprises, which requires creating and maintaining high-quality training data. Unfortunately, the tremendous effort it takes to gather, label, and prep that training data often overwhelms teams (when the task is not outsourced) and can compromise both the quality and quantity of training data.
Just as importantly, model performance can only improve at the speed at which your training data improves, so fast iteration cycles for training data is crucial. Iteration helps ML teams find new edge cases and improve performance. Additionally, iteration helps to refine and course correct data throughout the AI development lifecycle to maintain its reflection of real-world conditions. Shrinking the length of that iteration cycle lets you hone your data and conduct a greater number of experiments, accelerating the path to production AI systems.
It’s clear that iterating on training data is vital to building performant models quickly — so how can ML teams create the optimal workflow for this data-first approach?
Overcoming the challenges of a data-first approach
A data-first approach to machine learning involves some unique challenges, including management, analysis, and labeling.
Because machine learning requires a great deal of iteration and experimentation, companies often find themselves with a management system that’s a patchwork of models and results, stored haphazardly. Without a centralized spot for data storage and standard, reliable tools for exploration, results become difficult to track and reproduce, and finding patterns in the data becomes a challenge.
That means teams are often overwhelmed when digging out the insights they need from their data. Of course, large quantities of data is technically the way to solve business problems. But unless teams can streamline the data labeling process by labeling only the data that has true value, the process will quickly become unmanageable.
Using data to build a competitive advantage
Building an AI data engine is a series of iteration loops, with each loop making the model better. As companies with the best training data generally produce the most performant models, these companies will attract more customers who will generate even more data. It continuously imports model outputs as pre-labeled data, ensuring that each cycle is shorter than the last for labelers. That data is used to improve the next iteration of training and deployment, again and again. This ongoing loop keeps your models up to date, boosts their efficiency, and strengthens your AI.
Building this often required a great deal of hands-on labeling from subject matter experts — medical doctors identifying images of tumors; office workers labeling receipts; and so on. Automation dramatically speeds up the process, sending labeled data to humans to check and correct, eliminating the need to start from scratch.
A robust data engine needs only the smallest set of data to label to improve model performance, automatically labeling a sample of data for the model to work with, and only requiring verification from humans in some instances.
Putting it all together to improve model performance
Speeding up your data-centric iteration process takes just a few steps.
The first is to bring all your data to a single place, enabling your teams to access the training data, metadata, previous annotations, and model predictions quickly at any time, and iterate faster. Once your data is accessible within your training data platform, you can annotate a small dataset to get your model going.
Then, evaluate your baseline model. Measure your performance early, and measure it often. One or more baseline models can speed up your ability to pivot, as its performance develops. To create a solid foundation, your team should focus on identifying any errors early on and iterating, rather than optimizing.
Next, curate your data set according to your model diagnosis. Rather than bulk-labeling a massive amount of data, which takes time, energy, and money, create a small, carefully selected set of data to build on the baseline version of your model. Choose the assets that will best improve model performance, taking into account any edge cases and trends you found during model evaluation and diagnosis.
Finally, annotate your small dataset, and keep the iterative process going by assessing your progress and correcting for any errors like data distribution, concept clarity, class frequency errors, and outlier errors.
Training data platforms (TDP) are purpose-built for just this advantage, helping combine data, people, and processes into one seamless experience, and enabling ML teams to produce performant models quicker and more efficiently.
To learn more about boosting the performance of your model, reducing labeling costs, eliminating errors, solving for outliers and more, don’t miss this VB Live event!
Register here for free.
Attendees will learn how to:
- Visualize model errors and better understand where performance is weak so you can more effectively guide training data efforts
- Identify trends in model performance and quickly find edge cases in your data
- Reduce costs by prioritizing data labeling efforts that will most dramatically improve model performance
- Improve collaboration between domain experts, data scientists, and labelers
- Matthew McAuley, Senior Data Scientist, Allstate
- Manu Sharma, CEO & Cofounder, Labelbox
- Kyle Wiggers (moderator), AI Staff Writer, VentureBeat
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