Michael Chen | Content Strategist | December 6, 2023
In popular culture, AI sometimes gets a bad rap. Movies show it as the first step on the road to a robot apocalypse, while the news is filled with stories of how AI will take all our jobs. The truth is that AI has existed for a while, and neither of those worst-case scenarios are likely imminent.
Fundamentally, AI uses data to make predictions. That capability may power “you may also like” tips on streaming services, but it’s also behind chatbots capable of understanding natural language queries and predicting the correct answer and applications that look at a photo and use facial recognition to suggest who’s in the picture. Getting to those predictions, though, requires effective AI model training, and newer applications that depend on AI may demand slightly different approaches to learning.
At its core, an AI model is both a set of selected algorithms and the data used to train those algorithms so that they can make the most accurate predictions. In some cases, a simple model uses only a single algorithm, so the two terms may overlap, but the model itself is the output after training.
In a mathematical sense, an algorithm can be considered an equation with undefined coefficients. The model comes together when the selected algorithms digest data sets to determine what coefficient values fit best, thus creating a model for predictions. The term “AI model training” refers to this process: feeding the algorithm data, examining the results, and tweaking the model output to increase accuracy and efficacy. To do this, algorithms need massive amounts of data that capture the full range of incoming data.
Outliers, surprises, inconsistencies, patterns that don’t make sense at first glance…algorithms must deal with all of these and more, repeatedly, across all incoming data sets. This process is the foundation of learning—the ability to recognize patterns, understand context, and make appropriate decisions. With enough AI model training, the set of algorithms within the model will represent a mathematical predictor for a given situation that builds in tolerances for the unexpected while maximizing predictability.
AI model training is an iterative process whose success depends on the quality and depth of the input as well as the ability of trainers to identify and compensate for deficiencies. Data scientists usually handle the training process, although even business users can be involved in some low-code/no-code environments. In fact, the cycle of processing, observing, providing feedback, and improving is akin to teaching a child a new skill. With AI model training, the goal is to create a mathematical model that accurately creates an output while balancing the many different possible variables, outliers, and complications in data. When you think about it, parenting offers a similar—but much messier—journey.
Consider how children learn a skill. For example, let’s say you want to teach a toddler to identify the difference between dogs and cats. This starts out with basic pictures and encouragement. Then more variables are introduced, with details such as average sizes, barks versus meows, and behavior patterns. Based on what the child might be struggling with, you can put more emphasis on a certain area to help facilitate learning. At the end of this process, the toddler should be able to identify all manner of dogs and cats, from common household pets to wildlife species.
Training an AI model is similar.
AI: Select algorithms and initial training data set for the model.
Child: Use basic photos to establish the general differences between a dog and a cat.
AI: Evaluate output accuracy and tune the model to reduce or eliminate certain inaccuracies.
Child: Give praise or corrections depending on the answers.
AI: Provide additional data sets with specific diverse inputs to customize and fine-tune the model.
Child: Highlight different traits, shapes, and sizes as part of the learning process.
Like with children, initial AI model training can highly influence what happens down the road—and if further lessons are needed to unlearn poor influences. This highlights the importance of quality data sources, both for initial training and continuous iterative learning even after the model launches.
Most organizations already benefit from AI within their workflows and processes, thanks to applications that generate analytics, highlight data outliers, or use text recognition and natural language processing. Think transcribing paper receipts and documents into data records, for example. However, many organizations are looking to develop AI models for the purpose of addressing a specific, pressing need. The development process itself may unlock deeper layers of benefits, from short-term value, such as accelerated processes, to long-term gain, such as uncovering previously hidden insights or perhaps even launching a new product or service.
A core reason to invest in an infrastructure capable of supporting AI stems from the way businesses grow. Simply put, data is everywhere. With so much data coming in from all directions, new insights can be generated for nearly every part of an organization, including internal operations and the performance of sales and marketing teams. With that in mind, proper training and thoughtful application allows for AI to provide business value in nearly any circumstance.
To consider how an organization might train AI for maximum benefit, the first step is to identify inputs and what goes into a solid decision. For example, consider a manufacturing supply chain. Once all relevant data is available to a properly trained AI system, it can calculate shipping costs, predict ship times and quality/defect rates, recommend price changes based on market conditions, and perform many more tasks. The combination of heavy incoming data volumes and a need for data-driven decisions make supply chains ripe for AI problem solving. In contrast, in cases where soft skills remain a top priority, AI can provide supporting information but is unlikely to offer a revolutionary change. An example is a manager’s assessment of employee performance during annual reviews. In this case, AI might make it easier to gather metrics, but it can’t replace the assessments made based on human-to-human interaction.
To get the most out of an AI investment, organizations must consider the following:
By establishing those parameters, organizations can identify the business areas most likely to benefit from AI, then begin taking steps to make those a reality.
While each project comes with its own challenges and requirements, the general process for training AI models remains the same.
These five steps comprise an overview for training an AI model.
Prepare the data: Successful AI model training starts with quality data that accurately and consistently represents real-world and authentic situations. Without it, ensuing results are meaningless. To succeed, project teams must curate the right data sources, build processes and infrastructure for manual and automated data collection, and institute appropriate cleaning/transformation processes.
Select a training model: If curating data provides the groundwork for the project, model selection builds the mechanism. Variables for this decision include defining project parameters and goals, choosing the architecture, and selecting model algorithms. Because different training models require different amounts of resources, these factors must be weighed against practical elements such as compute requirements, deadlines, costs, and complexity.
Perform initial training: Just as with the example above of teaching a child to tell a cat from a dog, AI model training starts with basics. Using too wide of a data set, too complex of an algorithm, or the wrong model type could lead to a system that simply processes data rather than learning and improving. During initial training, data scientists should focus on getting results within expected parameters while watching for algorithm-breaking mistakes. By training without overreaching, models can methodically improve in steady, assured steps.
Validate the Training: Once the model passes the initial training phase, it reliably creates expected results across key criteria. Training validation represents the next phase. Here, experts set out to appropriately challenge the model in an effort to reveal problems, surprises, or gaps in the algorithm. This stage uses a separate group of data sets from the initial phase, generally with increased breadth and complexity versus the training data sets.
As data scientists run passes with these data sets, they evaluate the model’s performance. While output accuracy is important, the process itself is just as critical. Top priorities for the process include variables such as precision, the percentage of accurate predictions, and recall, the percentage of correct class identification. In some cases, the results can be judged with a metric value. For example, an F1 score is a metric assigned to classification models that incorporate the weights of different types of false positives/negatives, allowing a more holistic interpretation of the model's success.
Test the Model: Once the model has been validated using curated and fit-for-purpose data sets, live data can be used to test performance and accuracy. The data sets for this stage should be pulled from real-world scenarios, a proverbial “taking the training wheels off” step to let the model fly on its own. If the model delivers accurate—and more importantly, expected—results with test data, it’s ready to go live. If the model shows deficiencies in any way, the training process repeats until the model meets or exceeds performance standards.
While going live is a significant milestone, achieving that stage doesn’t mean the end of the model’s training. Depending on the model, every data set processed may be another “lesson” for the AI, leading to further improvement and refinement of the algorithm. Data scientists must continue to monitor performance and results, particularly when the model deals with unexpected outlier data. Should inaccurate results arise, even only on rare occasions, the model may need further tweaking so as not to taint future output.
AI training comes in many different forms that range in complexity, types of results, capabilities, and compute power. One method may use up more resources than necessary while in other cases a method may provide a binary response, as in a yes or no for a loan approval, when the situation requires a more qualitative outcome, such as a conditional “no” until more documentation is supplied.
The choice of method used for an AI model must factor in both goals and resources; venturing forward without careful planning may require data science teams to restart from scratch, wasting time and money.
While some AI models use rules and inputs to make decisions, deep neural networks offer the ability to handle complex decisions based on diverse data relationships. Deep neural networks work with numerous layers that identify patterns and weighted relationships among data points to make predictive outputs or informed assessments. Examples of deep neural networks include voice-activated assistants such as Apple’s Siri or Amazon’s Alexa.
In statistics, linear regression is used to determine the relationship between input and output. In its simplest form, this can be represented by the algebraic formula y = Ax + B. This model uses a data set to create that formula based on input, output, and possible variable coefficients. The final model used for prediction assumes a linear relationship between input and output. An example use case for linear regression is a sales forecast based on previous sales data.
Taken from the field of statistics, logistic regression is an effective model for binary situations. Logistic regression is based on the logistic function, which is an S-curve equation often used for calculating probability. In the case of AI modeling, logistic regression determines probability and delivers a binary outcome to ultimately make predictions or decide, for example, whether an applicant should be approved for a loan. An example use case for logistic regression is a finance application performing fraud detection.
Most people have experience with decision trees, even outside of AI. Decision trees work similarly to nodes in flowcharts. In machine learning, the training processes feed the tree through iterative data to identify when to add nodes and where to send the different node paths. An example use case for decision trees is a financial loan approval.
Decision trees can become overfit for their training sets by establishing too much depth. The random forest technique compensates for that by combining a group of decision trees—hence the term “forest”—and finding the greatest consensus or a weighted average in results. An example use case for a random forest is predicting customer behavior based on a variety of decision trees across different elements of a customer’s profile.
In child education terms, supervised learning is the equivalent of having your child go through a set curriculum with methodical lessons. For AI modeling, that means using established training data sets and defined parameters to train the model, with data scientists acting as the proverbial teachers in curating training data sets, running test data sets, and providing model feedback. An example use case for supervised learning is finding abnormal cells in lung X-rays. The training data set is X-rays with and without abnormalities and telling the model which is which.
Continuing the child education analogy, unsupervised learning is similar to the Montessori philosophy, where children are presented with a range of possibilities and the freedom to self-direct based on their curiosity. For AI modeling, that means ingesting an unlabeled data set without parameters or goals—it’s up to the AI to determine patterns in the data. An example use case for unsupervised learning is a retailer feeding an AI model quarterly sales data with the goal of finding correlations in customer behavior.
If you’ve ever reinforced desired behavior with treats, you’ve participated in reinforcement learning. On an AI level, reinforcement learning starts with experimental decisions that lead to positive or negative reinforcement. After time, the AI learns the best decisions, as in the most accurate or successful ones, to handle a situation and maximize positive reinforcement. An example use case for reinforcement learning is that list of “you might also like” suggestions presented by YouTube based on viewing history.
An AI model may have success when applied to a different situation. Transfer learning refers to the method of using an existing AI model as a starting point for a new model. This repurposing works best when the existing model handles a general scenario; anything too specific may prove too difficult to retrain. An example use case for transfer learning is a new AI model for a specific type of image classification based on parameters from an existing image classification model.
Using principles of both supervised and unsupervised learning, semi-supervised learning starts with training the model on a small group of labeled data sets. From there, the model uses unlabeled and uncurated data sets to refine patterns and create unexpected insights. In general, semi-supervised learning uses only labeled data sets for the first few steps, like training wheels. After that, the process heavily leans on unlabeled data. An example use case for semi-supervised learning is a text-classifying model, which uses a curated set to establish basic parameters before being fed large volumes of unsupervised text documents.
Generative models are an unsupervised AI method that use very large example data sets to create a prompted output. Examples of this are AI-generated images based on the metadata of an image archive or predictive text based on a database of typed sentences. Rather than simply classifying data in its output, results from generative models can take thousands, possibly millions, of pieces of example data to learn and create an original output. An example use case of a generative model is a chatbot, such as ChatGPT.
For an AI model to be properly trained, it needs data—a lot of data. In fact, data is the most crucial element in AI model training. Without it, the model simply can’t learn. And without quality data, the model will learn the wrong things. Thus, data scientists select data sets for their projects with intention and care.
Data set curation must involve the following factors for optimal AI model training:
AI model training comes with its own unique challenges. Some of these are logistical—infrastructure, compute power, and other practical considerations of getting from start to finish. Other challenges require introspection on the part of data scientists, such as developing an understanding of how to mitigate biases and keep the resultant system objective.
The following challenges should be considerations for any AI model training initiative:
Data bias: To get accurate results from an AI model, training requires quality data. To mitigate data bias, data scientists must vet data sources thoroughly before curating training data sets.
The right data: Training data sets requires heavy volumes of data that represent appropriate diversity and granularity. Not only does this call on teams to curate large amounts of quality data, it brings in many practical considerations. Storage, cleaning/transformation, processing, and general quality control all grow increasingly difficult as a data set gets larger.
Computing power and infrastructure requirements: The more complex the AI model, the more compute power and infrastructure support are required. The practicality of running the model, from training to going live, needs to be considered when selecting the model method. If a model type requires more resources than what’s feasible to deliver, the whole project will collapse.
Overfitting: When an AI model becomes too tuned into the training data sets, it can lock into those specifics rather than being capable of handling diversity and surprises. That phenomenon is known as “overfitting,” and it prevents accurate predictions in the future. An example of overfitting is when the training data set produces 99% accuracy but a real-world data set produces only 75% to 85% accuracy. Note that perceived accuracy in AI refers to how well a system appears to perform in terms of accuracy based on its current capabilities. It’s the accuracy that’s observed or experienced by users or stakeholders. On the other hand, potential accuracy in AI refers to the maximum level of accuracy that a system could achieve in ideal conditions, with optimal resources. Understanding the difference between perceived accuracy and potential accuracy is important in evaluating the performance of an AI system and identifying areas for improvement or future development.
The terms “overfitting” and “overtraining” are often used interchangeably, but they have distinct meanings. Overfitting, as discussed, is when AI performs extremely well on its training data but fails to generalize well on new data. Overtraining is when a model has been trained excessively, leading to poor performance on both the training data and new data. Overtraining can occur when a model is trained for too long or with too much complexity, causing it to struggle to generalize. Both issues need to be avoided in the model training process.
Explainability: One outstanding issue in AI modeling is the lack of explainability around how decisions are made. Users can make inferences based on outputs, but the model’s reasons may remain nebulous. Some developers have created tools to bridge this gap, including models built to have more transparent explainability. However, implementation, usability, detail, and accessibility all vary, both for input and output.
While AI has been around in some form since the dawn of computing, advancements in algorithms, CPU power, graphics processing unit (GPU) power, and the cloud-based sharing of resources have significantly pushed AI forward over the last two decades. AI is embedded in so many applications that many users employ it without realizing it. When you stream music, customized playlists come from an AI analyzing your favorite songs and artists. When you type a text message, an AI offers predictive suggestions based on your commonly used words. If you found a new TV show you love thanks to an automated recommendation, thank AI.
That’s the present of AI, but what lies just over the horizon?
The potential of AI depends on the evolving capabilities of model training. Let’s take a look at future possibilities in AI model training.
If it feels like AI’s innovations have grown exponentially, there’s a good reason for that: The explosion of data and connectivity over the past decade has made it much easier to train AI systems and allowed for complex models to be realized, and new and improving algorithms are adding to success. Because of that, a number of lofty goals seem feasible within the next decade, including deep reasoning, where AI gains the ability to understand the how and why behind situations; increased training efficiency using smaller data sets; and more efficient and accurate models grown from unsupervised learning.
For people, transferable skills increase employability and productivity by making it much easier to get started on a new task. The same applies to transfer learning in AI. However, effective transfer learning still faces a number of challenges. Currently, transfer learning works best in immediately similar domains for the original model, limiting its use. Widening the capabilities of transfer learning will require significantly more compute power and resources to support the greater complexity of retraining. Without innovations in efficiency and processing, it may be easier to simply build a model from scratch.
Perhaps the most powerful trait of AI is its ability to perform tasks faster and more accurately than humans, relieving shipping clerks, accountants, and others from performing repetitive tasks. Of course, getting to that point requires time and effort curating data sets, observing outputs, and tweaking the model.
A variety of AI model training tools can accelerate the development and training process. These tools include prebuilt model libraries, open-source frameworks, coding and environment aides, and gradient boosting. Some rely on the type of model used while others require certain standards for compute resources.
To determine which tool, or tools, work best for your project, compile the answers to the following questions:
These answers can help build a short list of effective tools to help your AI model training process.
Training complex AI models can be a resource-intensive initiative as hundreds, possibly thousands, of independent services coordinate and share information. Oracle Cloud Infrastructure (OCI) provides GPUs connected via a high-performance Ethernet network to save customers time and money while maximizing availability and stability. With OCI, customers get simple, fast interconnects to support training and deployment of highly complex models at scale.
The machine learning precursors to AI were built on intensive rules and probability driven by high-powered calculations. The supercomputer Deep Blue competed in world-class chess tournaments that way. However, AI has evolved beyond using rules powered by outside data; instead, AI models now focus on generating internal insights by training through heavy volumes of data sets. While some AI models still use rule-based decision trees, others support complex processes and predictions thanks to neural networks.
Advances in AI are exciting, but the future of this technology depends on high-quality training.
Enterprises undertaking model training, at whatever level, will want to ensure relevant data sets and institutional knowledge are well documented. One great way to accomplish this is an AI center of excellence, which delivers myriad benefits beyond training support.
What is AI model training?
AI model training is the process of feeding an AI model curated data sets to evolve the accuracy of its output. The process may be lengthy, depending on the complexity of the AI model, the quality of the training data sets, and the volume of training data. Once the training process passes a benchmark for expected successes, data scientists continue to monitor results. If accuracy dips or the model has difficulty handling certain types of situations, the model may require further training.
Where can I train an AI model?
Anyone with access to the proper tools can train an AI model using any PC, assuming they have access to the needed data. The steps include identifying the problem, selecting the training model, finding training data sets, and running the training processes. This can be on a small, local scale or a large enterprise scale depending on the scope of the project and resources available. New or independent developers can take advantage of cloud services that provide CPU resources across a variety of programming languages and remove geography from the equation.
How much does it cost to train AI models?
The cost of training an AI model depends on the project’s scope. Across the industry, costs continue to trend downward as CPU/GPU power and cloud access provide more resources. In fact, the average training cost for a small project, such as image classification, was $1,000 in 2017 but only $5 in 2022, according to Stanford’s Institute for Human-Centered Artificial Intelligence AI Index.
In comparison, the cost for large enterprise AI projects is actually increasing. For example, something like ChatGPT training can require an estimated budget of $3 million to $5 million. This disparity comes down to the complexity of projects and the fact that growing resources make increasingly complex and boundary-pushing projects available—if you can afford them.
How to learn AI modeling?
To learn how to perform AI model training, formal education or on-the-job-training is required. Once you have the expertise, start with the four steps involved in creating an AI model.
What are the four types of AI models?
In general, the four types of AI models are the following:
Some data scientists also use transfer learning, where an existing AI model is a starting point for a new model, and semi-supervised learning, which melds supervised and unsupervised learning.