Corporate investments in artificial intelligence (AI) are on the rise. In a recent O'Reilly Media survey, 61% of respondents indicated that artificial intelligence (AI) was their company’s most significant data initiative. The International Data Corporation forecasts spending on AI will grow to $35.8 billion worldwide this year.

And while investments in AI and machine learning applications have the potential to disrupt any industry and streamline tasks within every business, machine learning algorithms are only as good as the training data they use to learn. So as you scale your efforts, it becomes more challenging to clean, label, and prepare diverse data to support ever-evolving models.

In fact, Gartner predicts that as many as 85% of AI projects will fail. Successful data science is based on large datasets that are labeled accurately and ready to use. But getting labeled data - or ground truth for your algorithm - is time consuming and can be more difficult than it seems.

DataOps Applies People, Process & Tools

In recent years, DataOps has emerged as an adaptation of the software development methodology DevOps to address some of the data challenges facing AI. The DataOps methodology refers to the people, process, and tools that can improve the speed, quality, and reliability of data-driven applications, including AI.

We caught up with Chris Bergh, CEO and Head Chef at DataKitchen, who looks at these many factors as ingredients for great machine learning applications. We asked him about the challenges DataOps teams face in meeting customer expectations. Here’s what he said:

“If you focus on it, your team can work better and the results - the things that they create - are more attuned to what the customers need.” -Chris Bergh, CEO and Head Chef, DataKitchen

Data labeling is a major bottleneck in any DataOps process and is one of the leading reasons AI projects fail or go over budget. Data scientists are one of the most valuable resources in an organization, and they should focus their time on high-value work like data modeling, not on data labeling. Data scientists also need to make sure they have a rapid data labeling process in place so it can be ready for them as they iterate through the analysis process.

Data Labeling Challenges in Machine Learning

As a machine learning projects scale, DataOps teams face three major challenges in their data labeling operations:

  1. Quality - Poor data quality is the worst problem you can have in an AI application. The quality demands of machine learning are steep because low-quality data can backfire twice: first, when you train your predictive models and second, when the data is used by that model to inform future decisions.
  2. Velocity - Preparing data has always been time-consuming. As data volume and complexity increase, the time spent preparing or labeling data grows along with it. This can tax your internal resources to the point that your data scientists spend most of their time preparing data instead of analyzing and extracting business value from it.
  3. Agility - Building a machine learning algorithm is an iterative process. As your team iterates on processes to improve outcomes, they will need to prepare different datasets or modify existing ones to improve the algorithm results.

Many companies are reducing the time, effort, and cost associated with labeling data by augmenting their DataOps teams with crowdsourced labeling solutions. Unfortunately, they often find that crowdsourcing doesn’t provide the level of accuracy and quality needed to make a machine learning project successful.

3 Ingredients: Quality Data Labeling for Machine Learning

CloudFactory approaches these important data labeling and preparation issues by becoming a natural extension of your DataOps team. Our machine learning WorkStreams combine skilled data analysts and a proven methodology for scaling high quality training data to give you trust in the data powering your applications. Here’s how we address those key issues:

  1. Quality at scale - Quality is more than accurately labeled data; it’s accurate labeling across your entire dataset. And context matters. Unlike crowdsourcing solutions, we assign you a dedicated team lead who works with you to understand business requirements and project nuance. Our CloudWorkers are not an anonymous crowd that might do 10 tasks a day as a side gig. Instead, they are experienced data analysts who take pride in their work and bring their experience from previous machine learning projects to label your data with quality for every project.
  2. Elastic velocity - Our WorkStreams are designed like cloud computing solutions. We work with you to plan capacity and ensure that your data labeling team is large enough to process the volume of data required for you to meet project deadlines. Our WorkStreams are just another stage in the DataOps process you can use to launch products and features on-time and on-budget.
  3. Agility and flexibility - Our WorkStreams are designed to work the way you work, using virtually any toolset on the planet and keeping you closely connected to your data processes, regardless of project scale. This provides the best of both worlds - we do the heavy data lifting while you focus on innovation, transformation, and the culture needed to support them. New use cases or changes in requirements can easily be communicated to the whole team through your dedicated team lead, ensuring your data labelers are up-to-date and ready to label the next batch of data.

Want to learn more? Explore how we deliver quality labeled data at scale for machine learning so you don’t have to.

Crowd vs. Managed Team: A Study on Quality Data Processing at Scale

Data Labeling Workforce Strategy AI & Machine Learning Events

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