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Artificial intelligence: 4 questions to ask while choosing solutions

Artificial intelligence: 4 questions

As / solutions become more ubiquitous, choose one that will grow with your organization’s needs. Consider these key questions

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningOrganizations are increasingly using  () and  () to solve business problems and transform their operations. These technologies are affecting nearly every process across all industries and becoming imperative for a competitive edge. 

As and techniques continue to evolve, so do the requirements of the businesses that use them. When choosing an / strategy, it is important to ensure that the product roadmaps of your prospective / vendors align with your business’s future objectives in areas such as democratization, augmented analytics in BI, and cloud.

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Here are a few key questions to consider when choosing an / strategy and solution:

1. How will your solution save my team time?

There are plenty of open source tools and solutions out there that you can cobble together to get the job done for little or no cost. If you’re going to shell out real money for a data science platform, you need to see tangible ROI. 

Data preparation

Data preparation dominates most data science projects, turning your data scientists into expensive, and often unproductive, data engineers. To counter this, it’s important to choose a platform that makes data acquisition and transformation easy. These data engineering features should be seamlessly integrated into the modelling workflow.

Model selection

Another area ripe for optimization and automation is model selection. The platform should provide access to a variety of models, open-source and proprietary, and recommend the best model for the job. 

Feature stores

Feature development is often a time-consuming and error-prone process. Choose a platform that integrates with a variety of “feature stores,” like Feast or Uber’s Michelangelo to reduce duplicative work and drive consistency across data science projects and teams.  […]

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