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With the recent evolution of ChatGPT and generative AI, the picture of what AI can accomplish is becoming more apparent. As new use cases and innovation accelerates, this is an exciting time for the industry. However, it will take time for these technologies to break into the mainstream market and reach a level of ease of use that can provide real value to enterprises at large.
Luckily, for organizations that are eager to embark on their own AI journeys but may not know where to start, AI models have existed for a while and are now relatively easy to use. For example, large vendors like Google, IBM, Microsoft and others have created and developed their own AI models that organizations can implement into their own workflows for their own benefit, making the AI barrier to entry much lower compared to years past.
The downside – these models need to be customized to an organization’s specific needs. If the process of customization is done incorrectly, it can eat into valuable resources and budget, and ultimately, impact an organization’s success. To avoid this, here are several considerations organizations should carefully review before implementing AI models into their workflows:
Consider your infrastructure
Implementing AI is more difficult than installing a computer program. It takes time and resources to do so correctly. Missteps in this process can result in unnecessary costs – for example, evaluating where you want to store your data is important to prevent being trapped in an expensive cloud model.
But before organizations can evaluate how to apply AI models, they must first identify if they have the correct infrastructure to enable and power them. Unfortunately, organizations often lack the infrastructure needed to train and operate AI models. For organizations facing this situation, it’s critical that they consider leveraging modern infrastructure that can process, scale, and store the massive amounts of data needed to power AI models. At the same time, data processing also needs to be done quickly to be effective in today’s digital world so leveraging solutions that can provide fast and strong performance is just as important. For example, investing in high-performance storage that can address multiple phases of the AI data pipeline can play a key role in minimizing slowdowns, accelerating development, and enabling AI projects to scale.
Identify your use case
Once the groundwork has been laid with modern infrastructure, the next step in the process of customization is identifying a use case for the AI model. This use case should be specific with tangible outcomes that the model can easily achieve. If identifying a use case is a challenge, start small and hone in on one particular purpose for the AI model. It’s also important to consider your ideal outcomes when identifying these use cases, as it can provide the foundation for measuring whether or not the model is actually operating correctly. Once the model begins to achieve these goals and becomes more effective and efficient in its approach, organizations can start to develop their models further and address more complex problems.
Data preparation
Data is the lens through which AI models operate, but to be successful, data must first be properly prepared to ensure an accurate result. Unfortunately, data preparation can be difficult to manage and challenging to ensure accuracy. But without proper preparation, models can be fed “dirty data” or data that is full of errors and inconsistencies, which can lead to biased results and ultimately impact the performance of your AI model (such as decreased efficiency and loss of revenue).
To prevent dirty data, organizations need to take measures to ensure data is properly reviewed and prepared. For example, implementing a data governance strategy can be an extremely beneficial tactic – through developing processes for regularly checking data, creating and enforcing data standards, and more, organizations can prevent costly malfunctions from their AI models.
Data training
Deploying and maintaining continuous feedback loops needed to train AI models is essential to the success of your AI deployment. Successful teams often apply DevOps-like tactics to deploy models on the fly and maintain the continuous feedback loop needed to train and retrain AI models. But enabling continuous feedback loops is difficult to achieve. For example, inflexible storage or network infrastructure may be unable to keep up with evolving performance demands caused by pipeline change. Model performance can also be hard to measure as the data flowing through them changes.
Investing in flexible, high-performing infrastructure that can power rapid pipeline changes is essential to avoid these roadblocks. It’s also vital for AI teams to set up spot checks or automated performance checks to avoid costly and annoying model drift.
The destination for data
AI is one of the many destinations for data. But while AI is important, what we can do with AI is what really matters. Now, we have more opportunities than ever to build and extract value from our data with AI, which ultimately drives real value with more efficiency and new innovations.
About the Author
Amy Fowler is the VP, GM for the FlashBlade Strategy and Operations, Pure Storage. A global business leader with 20 years of experience who’s built and run high-performing, cross-functional teams, Amy is skilled in creating new organizational structures and driving operational discipline to resolve critical scale challenges in hyper-growth environments. Before joining Pure in 2013, Amy led EMC’s North American Healthcare business.
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