Training AI models requires heavy volumes of data that contain appropriate diversity and granularity. Teams also face significant practical concerns regarding storage, access, and quality control.
Similar to teaching a child to distinguish between cats and dogs, the model learning process involves processing inputs, observing patterns, guessing, correcting errors, and retrying until performance meets expectations.
Choosing the Right Model
When choosing an AI model for a project, it’s important to consider all options available. From the underlying compute resources required to the scope of the project and the complexity of the algorithm, every choice impacts how well your model performs. The right model is the key to achieving your project goals.
Initial training requires a wide volume of data to refine results and remove biases from predictions and results. Insufficient data sets limit the scope of an AI project’s potential and can lead to inaccurate performance.
The training process also includes selecting an algorithm, which is the primary model structure that influences a system’s output. Selecting the right algorithm can help you avoid common technical hiccups during model training, such as overfitting or undertraining. Overfitting occurs when an AI model performs extremely well on its training data but fails to generalize to new data, while undertraining happens when a model trains for too long or with too much complexity, resulting in slow convergence and poor performance.
To avoid these problems, a team should carefully curate the training data set, institute processes for data collection and cleaning/transformation, and choose appropriate models for each step of the model fine-tuning process. This process involves balancing technical elements, such as data requirements and compute power, with practical aspects, like time and costs, to achieve optimal performance. If a model requires substantial computational resources, IT departments should ensure that those resources are available and accessible to the training teams.
Choosing the Right Data
For AI models to provide accurate and reliable results, they rely heavily on the quality of the data they are trained with. In many cases, AI needs more training data to better understand complex patterns and deliver more precise outcomes. This is especially true in fields like healthcare and finance, where decisions have a significant impact. By continuously feeding the system diverse and comprehensive datasets, AI can become more capable and adaptive to real-world situations.
The training data sets must be curated from quality sources, then properly prepared (cleaned/transformed). Data prep is a time-consuming, labor-intensive task, but it’s essential to the success of an AI project. Insufficient attention to this step can lead to inaccurate results or an AI model that’s stuck in its training data sets and unable to interpret fresh, unseen data.
A high-quality AI model is a result of many different decisions, including the choice of an algorithm and initial training data set. Just like teaching a toddler the difference between dogs and cats, it’s important to start with the basics and encourage progress in steady, assured steps.
Depending on the algorithm, selecting the right hyperparameters is crucial for optimizing model performance. Like the way you might adjust the gas pedal and brake on your car, adjusting these settings helps your AI model find that sweet spot between overfitting and underfitting. The goal is to find a parameter “C” that will allow the model to memorize the training data set but also prevent it from being overly biased toward that data in future predictions. This is called regularization, and it’s a common technique to improve model accuracy.
Choosing the Right Hyperparameters
Creating an AI model requires a high level of technical expertise, but it also demands collaboration across teams with different skill sets. Establishing clear communication channels and a regular work cadence will help teams come together around the goals of their projects.
Choosing the right hardware and software is vital to meeting the computational requirements of an AI project, especially when it involves complex models that use multiple layers and sophisticated algorithms. For example, implementing a scalable GPU solution like Oracle Cloud Infrastructure allows you to train and deploy models more quickly without sacrificing stability and availability.
Carefully curating data sets and ensuring they are representative of real-world scenarios is another critical element of AI model training. This ensures that the model learns from a variety of perspectives, which helps it make more generalized predictions.
In addition, it can help mitigate issues such as overfitting and overtraining. Overfitting occurs when a model performs well on the training data set but struggles to generalize to new datasets. Overtraining happens when a model is trained too long or with too much complexity, leading to poor performance on test data.
A final step in the process is evaluating the model to determine whether it meets business objectives and operational requirements. This evaluation typically includes confusion matrix calculations and other machine learning metrics, as well as a determination of whether the model satisfies ethical concerns regarding avoiding bias and discrimination.
Choosing the Right Pre-processing
Like teaching a toddler to distinguish dogs and cats, AI model training requires the right volumes of diverse data to refine and fine-tune its outputs. But poor data sets can taint a model’s accuracy, causing it to lock into specifics that won’t translate to real-world conditions.
To prevent overfitting, data scientists must select and prepare data sets for training in a process called data preprocessing. This involves transforming raw data into a format the AI model understands, leveraging techniques such as dimensionality reduction and feature selection to optimize data for learning.
Then, they use additional data augmentation tools to diversify the training dataset. This may involve rotating, translating, or flipping image data; permuting text data with synonym replacement and word shuffling; or other specialized methods to provide a more balanced training experience. This can improve generalization, making the AI model more likely to transfer its skills to unseen data.
Many challenges can arise during the training process, from technical to organizational. IT departments must determine hardware infrastructure requirements; data scientists must weigh training data sourcing options; and project teams must consider the potential impact on other systems and processes. Oracle Cloud Infrastructure (OCI) helps mitigate these obstacles through scalable compute and storage resources, comprehensive analytics and reporting capabilities, and integrated cloud platform services that simplify integration and compliance. It also provides robust privacy and security tools that support the unique training needs of sensitive corporate data.
Choosing the Right Post-processing
AI model training relies on massive amounts of data. As algorithms digest this data, they can identify patterns and determine what coefficient values fit best, creating a model for prediction. The process is iterative: feeding the algorithm data and evaluating its accuracy can improve results and ensure the AI’s efficacy.
While sourcing quality training data may be challenging, best practices help ensure the success of the AI model. For example, data collected for an AI that aims to diagnose rare diseases should be diverse and include both public (e.g. medical research papers) and private sources (e.g. patient records). Collecting this information and ensuring proper preprocessing and data modeling can reduce inaccuracies. Additionally, establishing an AI inventory promotes accountability and ensures compliance with regulatory mandates.
Another key step is ensuring the model is not overfitting, which occurs when it performs well on training data because of memorization rather than learning and fails to generalize to new unseen data. Optimizing hyperparameters using techniques such as grid search, random search, and Bayesian optimization can improve model generalization and precision. In addition, regularization techniques such as early stopping can prevent overfitting by monitoring a validation metric and stopping the training process when it starts to deteriorate.
Once the model is trained, it should be validated against a separate and often more complex dataset not used for training to assess its real-world performance and ensure it is ready for production. This step is also critical for identifying overfitting, as it can help organizations pinpoint which areas of the model need improvement.
Choosing the Right Hyperparameters
Training AI models is a complex process that touches upon several departments within an organization. IT must determine hardware infrastructure requirements, data science teams need to consider training data set sourcing, and operations may have to weigh in on software tools and systems.
The model’s initial training can have a profound impact on its final accuracy. If the model gets too locked into the specifics of a particular training data set, it can become overfitted, which will prevent it from accurately interpreting new, unseen data sets. To avoid overfitting, the model should train with a wide range of data that includes both high and low-quality examples.
In addition, the model must be configured and tuned for optimal performance. This can be done manually or automatically using algorithms that optimize hyperparameters. Several methods exist for automating this task, including grid search, random search, and Bayesian optimization. Each approach has its own pros and cons, but all can improve the overall model performance.
Finally, the model must be evaluated to ensure it meets business KPIs and operational requirements. This step involves the use of a validation data set to determine confusion matrix calculations and other machine learning metrics.
