While various AI/ML applications have been undergoing development for decades, their commercialization has begun to take off only recently. A report by Mckinsey says that AI can potentially add $13 trillion in economic activity by 2030, amounting to 1.2 % yearly growth in GDP.
The question that arises is, with these massive benefits of AI, why is not every business infusing AI and machine learning in their initiatives? The answer lies in the availability of high-quality data for AI model training, as only 15% of companies have enough quality data for AI initiatives.
This blog delves into the state of MLOps, in general, and how data quality can make or break your AI initiatives.
Challenges in Achieving High-Quality Data
To arm enterprises for the future of AI production at scale, we recently partnered with VentureBeat to gather insights from data scientists, industry leaders, and technology professionals across industries who are on the ground, shepherding these AI products into the market. What significantly came out as an insight from the survey is that data quality is critical for the success of your AI applications.
Download the 2023 State of MLOps Report
One of the primary reasons that data quality remains a significant challenge is the overwhelming volume of data that companies must manage. Across industries, organizations are increasingly dealing with complex data pipelines, simultaneously juggling multiple initiatives, often in very different stages of development.
In our State of MLOps survey, the vast majority (63%) of companies have more than six ML projects in production; 39% of companies have 6 to 10 in production, while 5% have more than 20. These projects span different development stages: Data gathering, product definition, proof-of-concept, development, scaling, and the deployment phase.
With the explosion of big data and the proliferation of data sources, ensuring that all data is accurate and reliable can be difficult. It is particularly true when companies are managing multiple initiatives simultaneously, as it can be easy for data quality to become compromised in a rush to complete projects.
Why is Data-Quality Critical for AI?
Around 60% of surveyed professionals in the State of MLOps survey believed that higher quality training data is more important than higher volumes of training data for achieving the best outcomes from AI investments. Almost half (46%) of the respondents said the #1 reason for project failure was lack of data quality.
Let’s look at the reasons why data quality is critical for AI:
- Data quality directly impacts the performance and accuracy of AI models in interpreting the prompts and predicting results or future outcomes.
- Low-quality data adds complexity to your AI models as they have to parse unorganized data to make it workable.
- Poor data quality can lead to biased or unreliable AI models, resulting in wrong predictions or decisions.
- Data quality is particularly crucial for AI applications in high-stakes areas like healthcare or autonomous driving, where errors or inaccuracies could have severe consequences.
How to Achieve High-Quality Data?
Here are five critical steps organizations can take to improve the data quality used for training machine learning models.
- Multiple Data Sources: Collect and incorporate diverse data sources to avoid bias and improve accuracy.
- Data Cleaning: Use data cleaning techniques to remove duplicates, inaccuracies, and inconsistencies from the data.
- Robust Data Annotation: Ensure that specialized teams label and annotate multiple data formats, including image, video, text, and audio, to enable effective machine learning.
- Resolving Edge Cases: Capture, identify, and resolve edge cases in datasets to avoid misinterpretation by the AI model.
- Continuous Monitoring & Improvement: Regularly monitor the quality of the data and make necessary improvements. Utilize platforms that provide access to real-time insights to capture issues and rectify them promptly.
Conclusion
Many AI initiatives fail due to poor data quality, and the projects are abandoned because data scientists do not trust the data they work with. While every company is at a different place in the AI maturity curve, we can conclude that data quality is imperative to any AI/ML initiative.
Check out iMerit’s 2023 State of MLOps Report for more such insights. This report covers insights on the latest trends and best practices in the field of MLOps that will help you optimize your AI and data workflows.
