Arming developers with best practices and tools surrounding AI work can scale the ability to apply AI in the best ways possible.
FREMONT, CA: In business use cases leaders are continually discussing the value of Artificial Intelligence (AI). Implementing AI is costly in terms of talent, computing resources, and time, and to completely unravel the wave of innovations that AI promises, developers need to be empowered and appropriately equipped. Following are the critical elements required for successful AI implementation that have something to do the tools and processes around them.
• Cleaning Up Data
A need for the application of AI has a comprehensive understanding of the data. The performance of an AI model is inherently tied to the data it is trained in, and it is essential to have clean data to work with. When choosing which data sets to use for training, collaborating with business partners can help organizations understand what the ultimate business goal is.
• Standardized and Repeatable Features
Across the different product lines, diverse teams are using AI to solve for various problems such as optimizing the feed and suggesting services, to name a few. Each group uses different pipelines to produce the desired features of their AI models, as each use case is unique. Across these teams, there are similar features pop up and decides the process must be streamlined.
• Active Model Management
Models degrade over time and is an unavoidable part of the machine learning lifecycle, which can be outsmarted by taking a proactive approach to model maintenance. From the very beginning of building models, retraining should be made more accessible. So when a time comes to retrain the model, organizations will have a solid definition to follow that makes retraining easier.
AI is transforming how people do business, and for companies large and small, this could mean an unsettling change. Enterprises implementing an AI strategy today indeed will be best positioned to take advantage of its opportunities.