Four Key Links to Successfully Customizing an AI Model

As ChatGPT and generative AI continue to develop, it becomes increasingly clear what AI can achieve. It’s an exciting time for the industry with the acceleration of new use cases and innovation. However, it will take time for these technologies to enter the mainstream market and reach a level of ease of use that provides real value to the entire enterprise.

Fortunately, for organizations that are eager to embark on their own AI journey but may not know where to start , artificial intelligence models have been around for a while and are now relatively easier to use. For example, large technology companies like Google, IBM, Microsoft and other large technology companies have created and developed artificial intelligence models, and enterprise organizations can apply these models to their own workflows around their own commercial interests, making the entry barrier to artificial intelligence higher than in the past. much lower.

The disadvantage is that these models need to be customized to the specific needs of the organization. If the customization process is not done correctly, it can consume valuable resources and budget, and ultimately affect the success of the business. To avoid this, organizations should carefully review the following points before applying AI models to their workflows:

Consider Infrastructure

Implementing artificial intelligence is more difficult than installing a computer program. Doing this correctly takes time and resources. Mistakes in this process can lead to unnecessary costs – for example, evaluating where your data is stored is important to prevent getting stuck in an expensive cloud model.

But before organizations can evaluate how to apply AI models, they must first determine if they have the right infrastructure in place to enable and drive these models. Organizations often lack the infrastructure needed to train and operate AI models. For organizations facing this situation, it is critical that they consider leveraging modern infrastructure to process, scale and store the vast amounts of data required to power AI models. At the same time, data processing needs to be done quickly to be useful in today's digital world, so it's equally important to leverage solutions that deliver fast, powerful performance. For example, investing in high-performance storage that can address multiple stages of the AI ​​data pipeline can play a key role in minimizing slowdowns, accelerating development, and enabling AI projects to scale.

Validate Use Cases

Once the foundation of modern infrastructure is laid, the next step in the customization process is to identify the use cases for the AI ​​model. This use case should be concrete, with tangible results that the model can easily implement. If identifying a use case is a challenge, start small and strive for a specific purpose for your AI model. When identifying these use cases, it's also important to consider your ideal outcome, as it can provide a basis for measuring whether the model is actually working correctly. Once the model begins to achieve these goals and becomes more effective and efficient in its approach, the organization can begin to further develop its model and solve more complex problems.

Data Preparation

Data is at the heart of how artificial intelligence models operate, but to be successful, data must first be prepared to ensure accuracy the result of. Data preparation can be difficult to manage and accuracy difficult to ensure. But without proper preparation, models can be fed “dirty data” or filled with errors and inconsistencies, which can lead to biased results and ultimately impact the performance of AI models (such as reduced efficiency and lost revenue).

To prevent dirty data, organizations need to take steps to ensure data is properly reviewed and prepared. For example, implementing a data governance strategy can be a very beneficial strategy—by developing processes for regularly checking data, creating and enforcing data standards, and more, organizations can prevent costly failures in their AI models.

Data Training

Deploying and maintaining the continuous feedback loop required to train AI models is critical to the success of AI deployments. Successful teams often apply DevOps-like tactics to dynamically deploy models and maintain the continuous feedback loop needed to train and retrain AI models. However, achieving a continuous feedback loop is difficult to achieve. For example, inflexible storage or network infrastructure may not be able to keep up with changing performance demands caused by pipeline changes. Model performance is also difficult to measure as the data flowing through the model changes.

Investing in flexible, high-performance infrastructure that can drive rapid pipeline change is critical to avoiding these obstacles. It’s also crucial for AI teams to set up spot checks or automated performance checks to avoid costly and annoying model drift.

Artificial Intelligence is one of the many destinations for data. While AI is important, what we can do with it is what really matters. Now, more than ever, we have more opportunities to build and extract value from our data through artificial intelligence, which ultimately drives real value with greater efficiencies and new innovations.

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