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Managing The Data For The AI Lifecycle​

Sujatha Sagiraju | 10:15am EDT
As the Chief Product Officer at Appen, I am responsible for delivering innovative products to help our clients build successful AI models.
For the past decade or so, the conversation around artificial intelligence (AI) has focused on how this new technology can solve a myriad of business problems. While that’s true, the conversation missed a critical component for AI success: high-quality data. Without high-quality data, an investment in AI technology and algorithms is essentially moot. If you invest in AI technology without also investing in high-quality data, it’s the same as hiring unqualified, unmotivated employees to operate your business.
The quality of the training data you use to train your AI algorithm determines the quality of your output. If the training data is of low quality, you’ll get low-quality solutions, which will lead you to make decisions that won’t benefit your company. It’s time to shift the conversation from which business problems AI can solve to how to make your AI technology the best it can be—and that conversation starts with understanding the AI lifecycle.

Data For The AI Lifecycle

• Data sourcing. Data sourcing is all about finding the right data from the right source. Whether it’s a custom dataset, prelabeled dataset or synthetic data, it needs to be high quality.
• Data preparation. Data preparation is critical for success and includes data annotation, quality assurance, knowledge graph and ontology.
• Model training and deployment. The important part of model training and deployment is going from pilot to production. According to Gartner, Inc., 85% of AI projects fail to make it into production largely due to data.
• Model evaluation by humans: AI deployment isn’t one and done. You must continuously evaluate and update your model, ensuring there’s no bias and that you’re getting accurate results. This stage must include humans to ensure accuracy.
As a recent ODSC article notes: “Without data and specifically, high-quality data, your AI investment is useless. It’s essentially like purchasing an expensive car with an incredibly powerful motor without any access to a fuel source.”

Data For The AI Lifecycle

The first step of the AI lifecycle is all about choosing the right training data. Whether you choose a prelabeled dataset, a custom dataset or a synthetic dataset, you want to ensure you’re getting the right data.
• Look for a high-quality training dataset unique to your AI use case and problem.
• Choose a trustworthy data partner that is familiar with your use case.
• Evaluate the data partner’s ability to label, annotate and prepare the dataset.
• Remember that the largest dataset isn’t necessarily the best dataset—you need depth and variety.

Data Preparation

When it comes to AI project success, the most important step is data preparation. Data annotation requires accurately labeling each data point and then running the data through a quality assurance process to ensure labeling accuracy. Data can be prepared in-house or with a data partner and can be done either by hand or with smart annotation technology, which is a combination of human and AI annotation.

Model Training And Deployment

It’s important to connect your data provider, whether in-house or external, with your model infrastructure. A data provider that integrates with ML platforms can provide a seamless transition from the earlier stages to the final stage, which can make it easier for initial development and continuous training.​

Model Evaluation By Humans

The final stage is a continuous cycle of testing, retraining and evaluating to ensure the model is continuing to work in the real world. Benchmark the model output against real-world simulation use cases and other models in the market to ensure it’s continuing to work accurately and is still relevant in the industry. We often see models quickly become obsolete and outdated due to data drift. As the environment and model users evolve, the model needs to evolve with it. This is where continuous training comes into play.​

How You Can Improve Your Data

• Automate data annotation. ML-assisted data annotation can speed up the data labeling process by using AI in combination with human annotators. This can make the data preparation process more efficient, cost-effective and, in some cases, more accurate.
• Use the cloud for safe, efficient data sharing. Moving data from one part of your organization to another or from a data partner to your internal system can be time-consuming. Consider using secure cloud storage to help streamline this process and limit the number of problems you encounter moving large amounts of data.

Why You Should Pay Attention To The Data For The AI Lifecycle

Placing a focus on the data for the AI lifecycle is all about increasing the success of the deployment of your AI projects. The better the data you use to train your AI model, the higher the quality of output you’ll receive and the higher the return on your investment. A focus on high-quality data—and now “data for the AI lifecycle”—can help your company see a high return on investment in AI projects and more easily scale your work with AI.