Machine learning algorithms work best on well-curated, clean, simple data sets (including text) but not as well on the messy data you find in the real world. Incorporating semantic technologies help you filter out the noise and allow machine learning to focus on what’s important.
Semantic technologies bring together structured and unstructured information - internal and external to the organization. They use models, classification, and extraction strategies that result in quality contextual information to drive machine learning applications.
Listen to Steve Ingram, Solution Architecture Leadership, Semaphore, and Frederick Bednar, Data Science Consultant, EBCONT, and learn about:
- The trouble with enterprise data
- Machine Learning challenges
- How semantic models, classification, and extraction provide the context and meaning to supercharge ML
Register today!
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