Successful tech companies share a core set of elements that allow them to create meaningful products and make money selling them. For tech companies who describe themselves as “AI startups,” there are four essential elements for success:
DATA: The company has reliable access to enough relevant data to train ML models.
REVENUE RELEVANCE: The company is capable of creating a bridge between data science and customer value to drive revenue growth.
SCIENCE: The company has effective algorithms to extract relevant insights from its data.
PIPELINE: The company has the ability to build, train and deploy machine learning models quickly into production and do so at scale. A machine learning pipeline commonly has the following capabilities:
1. Model deployment and verification
2. Live data ingest and prediction generation at scale
3. Dataset prioritization for dynamically changing business needs
4. Ability to train models in a cost effective manner
5. Continuous outcome verification and auditing
This presentation will introduce the four elements and describe the architecture, capabilities and challenges for building a scalable ML pipeline.