The human brain is a prediction machine. It sees patterns, then makes predictions from previous experiences. This part of human intelligence has been critical to our survival. For example, many years ago, a forager might have eaten a particular berry, gotten sick, and thus learned the clues that indicate that a berry is poisonous. This would happen automatically—we’d get nauseous when seeing the berry again, which would make us steer clear. In other words, our brain and nervous system make a prediction that dictates our outcomes.
Artificial intelligence works the same way. It goes through a training process, learns via trial and error (although it can use other deep learning techniques, too), and then predicts outcomes. Just as we learn to avoid poisonous berries (or learn to let coffee cool before obliterating our taste buds for half a day), AI model training teaches an AI system how to respond to a set of conditions. This allows organizations to offload repetitive tasks to AI. And when combined with other automation tools, AI can bolster a wide range of business processes, from enhanced customer service to faster time to market for products.
Training artificial intelligence models properly can make AI transformative for your organization. This post covers the primary steps in training AI models and the different approaches to using enterprise artificial intelligence.
[The AI revolution stems from the proliferation of generative AI and large language models. Learn the relationship between the two: Generative AI vs Large Language Models (LLMs): What's the Difference?]
AI model training methods depend on several factors such as the use case and the scope and type of data involved. But while the specifics vary, the broad strokes of AI model training remain the same—whether you’re a hobbyist building a personal model or a professional creating an enterprise-grade, AI-powered deep digital transformation.
Data is the lifeblood of AI. Strong data equals strong models. Building a robust AI model starts with choosing data sources, and then collecting them in a single place.
Consider a financial services example—risk and loan processing. The data sources might include:
Personal data on the applicant (credit history, addresses, or income level, to name a few).
Financial behavior like banking transactions, large cash withdrawals or windfalls.
Market data and economic factors that could affect someone’s ability to repay a loan.
Additional records such as court histories, property ownerships, eviction notices, or housing liens.
Databases that include names and aliases of known white-collar criminals and fraud perpetrators.
Corporate data on loan repayment history to help you discover your own risk markers.
Incorporating these data points helps train the model to weight individual risk markers so it can make suggestions and predictions when someone later applies for a loan.
[Gathering data from multiple data sources can quickly get complex. A data fabric can help. Learn how: The Data Fabric Advantage: De-Silo Your Data for Rapid Innovation.]
The next step involves preparing data for training. If we use a culinary analogy, then step one is gathering ingredients and step two is slicing them to prepare them for cooking. Pre-processing involves:
Reviewing data for appropriateness and completeness.
Formatting data for training (expound on this).
Cleansing data (expound – not sure what this means).
Pre-processing is critical. AI models require multiple sources of data, often in vastly disparate data formats. Pre-processing makes these data elements easier for an artificial intelligence system to access, process, and train on.
Pro tip: This is the perfect time to consider bias. In this step, remove any data elements that might cause the model to make inaccurate predictions or, worse, discriminate. For example, check data sources for information that could identify someone based on a protected demographic.
The last thing you want is an AI model making poor decisions based on these factors. Even though bias is usually unintentional, organizations can still be on the hook for both fines and reputational damage. Avoiding bias is an ongoing process, but this step sets a strong foundation.
The specifics of AI model training depends on your use case. Selecting machine learning training models is the domain of the data science expert. Types of AI training models would require an article unto itself, but here are a couple examples.
Reinforcement learning models run a number of simulations where the AI attempts to produce an output or reach a goal using trial and error. The model takes actions, then receives positive or negative reinforcement based on whether it reached the outcome.
Deep learning models use neural networks to learn from data. They can be fed information, and within each repetition, they can start to classify this information and draw distinctions. For example, you might feed a deep learning AI model images, and it might learn on a first repetition that a specific image includes furniture. Then, in a subsequent learning cycle, it may start to draw distinctions between types of furniture, like learning the difference between a chair with cushions and a table.
There are multiple different types of AI models you can choose from. Choosing the right model for a given task will depend on your goal—in the previous example, reinforcement learning might make more sense in business goal forecasting while deep learning makes more sense for building models that need to recognize things like images, documents, or text. Often, a task might involve using multiple methods.
Finally, we train the model. This involves machine learning. How training occurs will depend on the machine learning model you chose in the previous step, of course. But in general, the AI runs a series of tests or simulations, makes predictions, then compares those predictions against an expected goal or outcome. Over multiple training rounds, the model adjusts. Over time, the delta between prediction and expected results should get smaller, leading to more accurate predictions.
After training, test the results. Just like in any other business arena, you’ll want quality assurance for AI. Try testing the model on a small set of real-world tasks to ensure it performs well. If satisfied, you can deploy it to a higher environment; if not, it’s worth going back to retrain.
But model evaluation isn’t a one-time event. Organizations must continuously evaluate AI models to ensure they produce the right results. For example, several major US health insurance companies have come under fire and face legal cases around excessive claim denials. Having human oversight to ensure these models aren’t making the wrong decisions is critical to prevent poor performance, reputational damage, lowered customer satisfaction, or even compliance fines.
I’ve mentioned a few times that building these models takes expertise. Enterprises have three methods of gaining the resources to build these models.
Method one: Build everything in house. This approach offers numerous advantages. You gain full control over your models. Models are trained on your data, ensuring accuracy. You can tweak models easily if bias occurs. And most of all, your data remains private. But, this method is expensive. You’ll need a team of data engineers, data scientists, and software developers, plus the budget for your hardware, software, and infrastructure. This puts the method out of reach for organizations where AI isn’t mission-critical.
Method two: Use a large public cloud provider (also known as hyperscalers). These companies provide pre-existing AI models you can use to apply to your tasks. This cuts the cost of a large data team. But there’s a catch. You have little control over your data. This means the models won’t be tailored to your organization, and worse, the provider may use your data to train their own algorithms. This jeopardizes your data privacy.
Method three: Use a vendor that emphasizes private AI. These are large hyperautomation platforms that adopt a private AI approach. You can create your own models using low-code or no-code tools, and integrate them into a wider end-to-end process that transforms your business. For example, you can upload a batch of emails or documents, then have the platform train a model for you. From there, you can review results and deploy to production. And you can even tweak the models on an ongoing basis. This keeps data private to you without having to hire an extremely expensive team and purchase a prohibitive amount of hardware and systems to maintain.
Want to understand how this private AI approach works? Go in depth on these three methods to figure out which one’s right for you—and learn more about how private AI platforms work—by reading Implementing Private AI: A Practical Guide.