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.
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?
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:
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
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.
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.
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.
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.
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.
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 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.
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.
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.
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.