Building an AI platform – key principles and lessons learned
Who is this presentation for?Developers, Architects, Data scientists
In today’s sales and marketing landscape, knowing your customer is everything. Traditionally, this would be achieved by dedicate sales agents covering accounts which 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.
The advanced Analytics team at Intel IT has implemented an internal Sales AI platform to support the full cycle of sales. It 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 their decisions, AI technology helps processing extensive, disparate data sources and convert them into actions or actionable insights for salespeople. This may allow effective coverage of a much larger number of accounts and to gradually provide autonomous coverage by automating end-to-end sales services and actions.
To enable all of this at scale, the platform is based on streaming, micro-services 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 are planning to implement a similar AI platform, you will get a chance to learn from our experience:
• How we identified the set of characteristics and needs that are required for Sales AI scenarios and made them available in this platform.
• Thorough overview of the architecture we implemented with the related technologies
• Hear how we are using this platform to address Sales AI use cases that support end-to-end sales services to accelerate sales.
Impact: The presented platform and related advanced analytic capabilities have increased Intel’s revenue by approximately USD 500 million in the past 5 years.
What you'll learn
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.
Leave a Comment or Question
Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?
Join the conversation here (requires login)
For conference registration information and customer service
For more information on community discounts and trade opportunities with O’Reilly conferences
For information on exhibiting or sponsoring a conference
View a complete list of Strata Data Conference contacts