Take a proven path to developing and deploying AI
Artificial intelligence (AI) and machine learning are transformative technologies, but they’re also relatively new to some enterprises. In order to get from where you are today to where you want to be with AI—driving better customer experiences, streamlining operations, and lowering costs throughout your organization—you need to map out a plan to get there.
And while the path is far from easy, there are five essential steps you can take to vastly simplify your journey and increase your chances for success.
Step one: Define your use case and outcomes
While it may sound crazy, some organizations begin their path to AI without having a clear understanding of the business problem they’re trying to solve—or what results they should expect. Instead of starting with the technology and the data, begin with a specific business problem and identify key success metrics for a solution. Then consider the feasibility of implementing an AI-based solution to solve it.
Step two: Make your data AI-ready
AI requires massive amounts of data for accurate results. Fortunately, most organizations are drowning in data. Unfortunately, data is increasingly decentralized and unstructured, hidden in silos and hard to locate. Before embarking on an AI project, make sure data scientists can find and access the data they need, and that they have the tools in place to clean, tag, transform and catalog data for their models.
Step three: Identify needed skillsets to train the model
Now that you’ve identified your use case and located and refined the data, you’ll need to identify the skillsets and tools required to build, train and prove your model. Teams should consider the accuracy, response times and depth of insight that can be delivered by different models. This step should also consider security, governance and the reproducibility of your training.
Step four: Overcome the last mile problem with DevOps
Converting a trained AI model into a proof of concept (PoC) is often referred to as a “last mile” problem, or the point where projects falter. A robust software ecosystem that enables data scientists to work more collaboratively with DevOps positions your organization for a successful launch of a proof of concept and an eventual production application.
Step five: Scale to production
Once the PoC has demonstrated business value, it’s time to rollout a production application. Make sure that you’ve addressed any challenges with the PoC and identified differences that need to be accounted for between the PoC and the future production model. It’s helpful to consider the full lifecycle of your model, including operationalization, retraining considerations, ongoing data sources.
Get the assistance you need
AI success requires a clearly thought out strategy for moving an AI from vision to production. Dell Technologies has the experience to help you make the journey.
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