6 Common AI Model Training Challenges

Michael Chen | Content Strategist | December 20, 2023

When it comes to AI projects, every model training process is different. Scope, audience, technical resources, financial constraints, and even the speed and skill of the developers all factor into the equation, creating a wide range of challenges.

While each set of model training difficulties may be unique, some themes exist. This article reviews six of the most common problems found during AI model training and offers solutions and workarounds for both the development team and the organization as a whole.

What Makes AI Model Training So Difficult?

Despite the rapid expansion of AI-related resources, the AI model training process is still challenging. Some issues create a spiraling set of problems: As resources become more powerful and available, AI models increase in complexity. Are they accurate? Do they scale?

Key Takeaways

  • AI model training challenges can span a broad range of factors across an entire organization and go beyond technical issues.
  • Technical challenges often can be resolved by augmenting training data sets or adding external cloud resources for more compute power.
  • Overcoming these challenges requires a combination of technical expertise, flexible processes, and a culture of collaboration among stakeholders

6 Common AI Model Training Challenges

From initial project scoping to final go-live deployment, AI model training touches on many different departments. From a technical perspective, IT departments need to understand hardware infrastructure requirements, data scientists must consider training data set sourcing, and developers must weigh investments in other software and systems.

From an organizational perspective, the type of AI project defines the operational departments affected by the project: Marketing, sales, HR, and other teams may have input on the project’s purpose, scope, or goals.

That adds up to a lot of cooks in the AI model training kitchen. And the more cooks, the more restraints and variables, which all increase organizational challenges. The following list dives deeper into six of the most common challenges faced during AI model training:

AI model training challenges span technical and organizational issues. Here are common ones facing organizations today.

This image shows 6 AI model training challenges:

  • Hardware and software: Hardware resource/capability limitations and incompatible software
  • Algorithms: Model type selection, overfitting, or underfitting
  • Data sets: Insufficient, imbalanced, or poor quality data
  • Talent pool: A hot job market and competition for skilled AI workers
  • Project management: Communication gaps and problematic expectations among departments
  • Data management: Security, privacy, access, and ownership concerns across the organization

1. Data Set-Related Challenges

Training data sets are the foundation of any AI model. That means the quality and breadth of training data sets dictate the accuracy—or lack thereof—of data produced by the AI. Data problems can include

  • Imbalanced data: Imbalanced data creates a bias in the AI training model. For example, if a clothing retailer AI model uses only shoe data, the model won’t be able to factor in variables created exclusively by sizing for shirts or dresses.
  • Insufficient data: When AI training models work with only a small volume of data, the model’s ability to predict with accuracy becomes extremely limited. Projects require sufficient training data to fully refine outcomes and remove biases. Otherwise, it’s like driving to a destination with only some of the steps mapped out.
  • Poor-quality data: While imbalanced data creates biases in predictions and results, poor-quality data leads to overall inaccuracy. Vetting sources for quality is a key first step.

2. Algorithm-Related Challenges

If training data sets are the foundation of the AI model, the algorithm represents the main structure. To consistently get accurate results from the AI model, developers must carefully craft and train the algorithm to ensure the right fit for the project’s needs.

  • Choosing the right algorithm: Which algorithm is right for your project? A range of AI algorithms are available as starting points, and each has its own strengths and weaknesses. For example, logistic regression algorithms can move projects forward quickly but provide only binary results. The right balance of scope, results, and resource use all factor into the best choice for your project.
  • Overfitting: Overfitting is when an AI model becomes too attuned to a specific outcome, causing it to miss other results that should be within scope. These situations occur for a variety of reasons, including too few training data sets, homogenous training data sets, and overly complex models leading to misunderstandings and “data noise.”
  • Underfitting: Underfitting is when an AI model requires further training and delivers accurate results only in extremely limited circumstances. A common example of underfitting is when the model works well with initial training data sets but fails with both further validation and real-world data. Underfitting often occurs when the model is too simple for the project’s goals or teams fail to properly clean training data sets prior to use.

3. Hardware and Software Challenges

IT departments face hardware and software challenges when supporting AI model training. Potential roadblocks include having enough computational power and storage capacity, data resources, and compatibility and integration tools to see an AI project through to completion.

Overall, AI model training success involves managing very large data sets. That means IT departments need to ensure trainers have enough data storage, the necessary access, a data management system, and compatible software tools and frameworks.

  • Hardware resources: To handle the processing and analysis of large data sets—particularly for very complex models, such as those for medical research—IT must secure enough high-performance servers and storage systems. AI model training requires significant computational power, so organizations need to ensure a project’s scope aligns with available resources.
  • Software considerations: AI training projects need to integrate a number of specialized software tools, frameworks, and systems, both upstream and downstream. That makes compatibility checking a key part of a project’s initial groundwork, because integrating specialized tools with existing IT systems can be a complex task.

4. Challenges Hiring Skilled Talent

It takes people with specialized skill sets across different technical disciplines to develop, manage, and iterate AI model training. A lack of experience in any area could easily derail the training process, ultimately leading to a complete reboot of a project.

  • Demand for AI talent: To assemble an excellent team of developers and data scientists, you’ll need to hire wisely. However, AI and machine learning skills are in high demand, which means bringing in the right people may force organizations into a highly competitive hiring process. Thus, employers must move quickly when they identify a qualified person and keep abreast of the state of market demand. To attract the best talent, show your commitment to the technology by, for example, launching an AI center of excellence.
  • Lack of trained AI professionals: If an organization begins an AI project with a thin development team, the initiative may wind up chronically inaccurate or biased—if it even reaches completion. Moving ahead with a lack of trained professionals wastes time and money, so be prepared to invest in both talent and technology.

5. Challenges Managing AI Projects

Enterprise AI projects can be costly and resource-intensive endeavors. Beyond the immediate concerns of model development, data source curation, and AI model training, management requires a fine balance of financial, technological, and scheduling oversight.

  • Communication gaps: Effective project management for any industry requires solid communication, but AI project managers must interface with many teams, including IT, legal, and finance, along with the project’s end users. Gaps in communication can lead to problems that have ripple effects and cost the organization in accuracy, time, money, or all of those.
  • Misaligned expectations: Popular culture has set lofty expectations about what AI can do. Bringing those expectations down to earth requires effective communication from team leads about the AI project’s purpose, goals, and capabilities. Without these, users may not understand the project’s practicalities or limitations.

6. Challenges Managing Data

In the context of AI training, different elements of data security apply at each stage. Collectively, this creates a series of challenges under the umbrella of data management.

  • Data access and ownership: Who has access to training data? Who can see training results? Who curates, archives, and manages the process? All of these questions must be considered. Without sound data management strategies, such as using role-based access, project logistics can get caught on the smallest of steps—and these hiccups may open the door to security issues.
  • Data privacy and security: Training data sets may contain sensitive data, including personally identifiable information, financial details, and sensitive corporate plans. Ensuring privacy may require encryption and/or cleaning in both training and output data. In addition, standard cybersecurity concerns apply to the AI model during both training and deployment, particularly when the project involves public or external resources.

Overcoming AI Model Training Challenges

During the AI model training process, challenges can come from all sides. Technical issues involving hardware resources, algorithm practicalities, or data sets can make developers wonder, “How will we actually get this done?”

Overcoming these challenges requires planning, smart resource use, and—perhaps most importantly—frequent, complete, and inclusive communication.

Smart use of technology can help too.

Technical Solutions

Technical hiccups in AI model training can stem from many causes. In some cases, the model type demands more resources than the organization can supply. Other times, the training data set isn’t properly prepared, or the model may need more training data sets than are available. The following three techniques can help overcome common technical challenges.

  • Data augmentation: If your AI model needs more training data sets or broader diversity in those data sets, yet further resources remain inaccessible, teams may be able to generate their own. Data augmentation refers to the process of manually augmenting training data sets to provide further model training, sometimes with a specific goal in mind.
  • Regularization: Overfitting is one of the most common issues found during AI model training. Regularization offers techniques to compensate for that within a training data set. Through regularization, models calibrate to compensate for overfitting through various optimizations that create simpler, more accurate output. Common regularization techniques include ridge regression, lasso regression, and elastic net.
  • Transfer learning: Transfer learning allows developers to skip ahead several steps by using an existing algorithm as a starting point. Successful transfer learning depends on several factors. First, a viable model must exist, one that demonstrates a successful similar process while being flexible enough to adapt to a new project’s context. Second, the project’s scope and goals must be capable of adapting to existing work.

Organizational Solutions

In any organization, successful AI models require more than technical expertise. Because a variety of stakeholders can get involved during the training process, including for nontechnical issues such as finances and goals, project success often depends on involvement from the whole organization. Thus, creating a unified front is a challenge in itself.

Here are some practical ways to achieve a smoother organizational process.

  • Establish clear communication channels: AI projects may demand diverse skill sets across different teams. Challenges can arise when these teams don’t usually work together. Thus, open and clear communication about a project’s goals, scope, and work cadence builds unity and limits confusion that can lead to duplicative work or missed steps.
  • Foster a culture of collaboration: Successful AI projects involve many different stakeholders with different points of view. Pulling all these folks into a cohesive working unit requires a culture of collaboration. For creative solutions, ensure that individual opinions can be expressed and debated in a constructive, respectful manner.
  • Encourage continuous learning: AI capabilities have evolved significantly over the past 10 years, with compute power and cloud accessibility growing especially quickly. New possibilities, skills, and strategies are emerging, and staying on top of advances requires continuous learning. Teams should keep one eye on the future even as they push ahead on current projects.

Overcome Your AI Model Training Challenges with Oracle

AI model training challenges can run the gamut from technical to organizational; fortunately, Oracle Cloud Infrastructure (OCI) can be part of the solution for nearly all of them. Scalable compute and storage resources can power training even with large data sets and complex models, while in-depth security and governance tools help meet the latest privacy and security requirements.

OCI also expedites collaboration and communication among departments by enabling data sharing and connecting data sources, all to provide more transparency during development. With comprehensive coverage of compute, storage, networking, the database, and platform services, OCI offers a flexible and powerful advantage for AI model training while reducing project and organizational costs.

For organizations that persist and overcome the challenges inherent in AI model training, the payoffs can include improved levels of automation and competitive advantages, even entirely new products and services, based on insights that wouldn’t be discoverable without AI.

IT teams, project managers, and executive leadership have the tools to overcome these challenges and others involving case-specific AI model training. It just takes some creative thinking.

Establishing an AI center of excellence before organization-specific training commences makes for a higher likelihood of success. Our ebook explains why and offers tips on building an effective CoE.

AI Model Training Challenges FAQs

How can transfer learning be used to improve the accuracy of AI models?

Transfer learning in AI models refers to the process of using an existing model as a starting point for a new project. This gives projects a head start, though it comes with limitations. Transfer learning works best when the existing model addresses a general situation, with the new project diving deeper into more specifics. As AI capabilities become more sophisticated, the latitude of transfer learning start/end points should increasingly widen.

How can organizations promote a culture of collaboration among team members involved in AI model training?

Organizations often need collaboration across teams with diverse skill sets to successfully complete AI projects. To encourage collaboration, leaders should encourage open lines of communication, input and constructive discussion among all stakeholders, and a philosophy of continuous learning. By emphasizing the how and why of “we’re all in this together” while also looking at future possibilities, an organization can step toward greater overall cohesion and communication within its various teams.

How can organizations overcome hardware and software limitations during AI model training?

Many different solutions can overcome hardware and software limitations. Some can be achieved within the organization, such as by allocating internal staff with more experience to evaluate and refine the particular model. Another example may be in the training data sets themselves—they may need proper cleaning and preparation to limit their impact on resources. In other situations, using external resources, such as a cloud-based infrastructure platform, can let teams scale more easily with greater flexibility to handle compute demands.