Building an AI platform: Key principles and lessons learned
Who is this presentation for?
- Developers, architects, and data scientists
In today’s sales and marketing landscape, knowing your customer is everything. Traditionally, this would be achieved by dedicated sales agents covering accounts they know very well. However, account coverage complexity grows as agents can’t transform the vast quantities of data about their accounts into trusted insights in a timely manner. Moreover, there will never be enough salespeople to cover hundreds of thousands of accounts.
Moty Fania details the experience of the advanced analytics team at Intel IT as it implemented an internal sales AI platform to support the full cycle of sales. The sales AI platform continuously extracts and interprets massive amounts of internal and external public data and applies AI reasoning for taking the relevant actions. By imitating humans’ reasoning capabilities and decisions, AI technology helps by processing extensive, disparate data sources and converting them into actions or actionable insights for salespeople. This may allow effective coverage of a much larger number of accounts and gradually provides autonomous coverage by automating end-to-end sales services and actions.
To enable all of this at scale, the platform is based on streaming, microservices architecture with a message bus backbone. It employs cutting-edge open source technologies such as Ray, Snorkel, TensorFlow, TensorFlow Serving, Python, Kafka Streams and was optimized to be easily deployed with Docker and Kubernetes. The platform supports different kinds of data and knowledge representations including knowledge graph, search, and more. In addition, it enables online deep learning inference at scale for natural language understanding and recommender engines.
If you’re planning to implement a similar AI platform, you’ll learn from Intel’s experience, including how it identified the set of characteristics and needs that were required for sales AI scenarios and made them available in this platform, a thorough overview of the architecture Intel implemented with the related technologies, and how Intel uses this platform to address sales AI use cases that support end-to-end sales services to accelerate sales. The platform and related advanced analytic capabilities have increased Intel’s revenue by approximately USD 500 million in the past five years.
What you'll learn
- Learn how Intel identified the set of characteristics and needs that were required for sales AI scenarios and made them available in this platform
- Gain a thorough overview of the architecture implemented with the related technologies
- Discover the knowledge graph builder system—how to create and maintain large knowledge graph at scale
Moty Fania is a principal engineer for big data analytics at Intel IT and the CTO of the Advanced Analytics Group, which delivers big data and AI solutions across Intel. With over 15 years of experience in analytics, data warehousing, and decision support solutions, Moty leads the development and architecture of various big data and AI initiatives, such as IoT systems, predictive engines, online inference systems, and more. Moty holds a bachelor’s degree in economics and computer science and a master’s degree in business administration from Ben-Gurion University of the Negev.
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