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Organizations in every industry are rapidly embracing artificial intelligence (AI) to enable and accelerate their business transformation — with machine learning proving to be foundational to gaining insights – fueled by data. With the advent of GPU-accelerated data science businesses are realizing faster time-to-insight, making organizations more productive and cost-efficient, gaining competitive advantage.
Capital One is one such business that has integrated AI and machine learning at scale, even developing its own Machine Learning Center of Excellence, an in-house consolidation of expertise and technology that enables the financial services giant to expand innovation across many businesses. Senior director Zach Hanif shares his insights on what every business should know as they seek to tap into the power of AI and machine learning.
Understand the definition of “good data.”
Data is the lifeblood of machine learning efforts. When there isn’t great data management then the system may be spending more time categorizing and deciphering every data gathered. Having data accessible, discoverable, and maintainable can keep the data high quality. Making it easily available will pay off immediately to your efforts.
Identify the key use cases you want to solve in your business.
Machine learning is an investment of time and energy on multiple fronts. By identifying which use cases are most important to solve then businesses can benefit from learning how others have stepped up to the particular challenge. In this webinar on data management and scaling AI infrastructure, Hanif talks about how Capital One leverages AI/machine learning for key use cases such as financial crimes. There are transformative opportunities in business processes with machine learning in any industry.
Scaling AI infrastructure is important.
Machine learning experts should spend their time deciding what success means, not mechanically determining if success was achieved in order to scale more effectively. There are multiple solutions to scaling infrastructure as AI and machine learning demands grow. First: Orient the data around the natural software development cycle with predefined metrics. Second: Embed good software engineering practices will see a far more predictable process in the infrastructure.
Learn more about the insights and best practices from Zach Hanif in this free on-demand webinar recording.
Credit: Google News