Building enterprise-grade AI: Sberbank and AI Telekom
Summary
Sberbank uses a 500-CPU cluster to transcribe about 30% of .wav voice recordings of corporate client calls into text, which is converted into meaningful sentiment by a “word-embedding” machine learning technique popular in natural language processing. Efforts from A1 Telekom and Sberbank to create a more enterprise-ready approach to machine learning come as suppliers and analysis debate changing enterprise data architecture to meet these needs. Claudia Imhoff, president and founder of research company Intelligent Solutions, says: “In the last five years, we’ve all had to rethink our architectures because there is no longer a lovely tidy world where all analytics are in a single data warehouse. “If the data scientist creates a great idea using some dodgy open source software that does not scale and has no support, it is probably not going to go into production, and IT will figure out another capability,” he says. “From the beginning, you should create these cross-functional analytics-ops teams, taking the concept of agile and DevOps and putting them together in a data-centric context, rather than a software-centric context.” Machine learning and AI are growing out of their teenage years and need to prove their value to the business at an affordable cost.