Machine learning teams need to work with raw data in ad hoc ways to create features to drive model development. Machine learning operationalization teams inherit these ad hoc transformations and translate them into formal data pipelines, which apply these transformations on raw data and apply the model to make predictions. This handoff creates friction, slowing down the process of operationalizing the models, and is at odds with the business need to rapidly deploy models.
Sameer Wadkar and Nabeel Sarwar explain how to seamlessly integrate model development and model deployment processes to enable rapid turnaround times from model development to model operationalization in high-velocity data streaming environments. The features of the system include:
Sameer Wadkar is a senior principal architect for machine learning at Comcast NBCUniversal, where he works on operationalizing machine learning models to enable rapid turnaround times from model development to model deployment and oversees data ingestion from data lakes, streaming data transformations, and model deployment in hybrid environments ranging from on-premises deployments to cloud and edge devices. Previously, he developed big data systems capable of handling billions of financial transactions per day arriving out of order for market reconstruction to conduct surveillance of trading activity across multiple markets and implemented natural language processing (NLP) and computer vision-based systems for various public and private sector clients. He is the author of Pro Apache Hadoop and blogs about data architectures and big data.
Nabeel Sarwar is a machine learning engineer at Comcast NBCUniversal, where he operationalizes machine learning pipelines under the banner of improving customer experience, operations, field, and anything in between. He also oversees data ingest, feature engineering, and the generation and deployment of the AI models. Nabeel holds a BA in astrophysics from Princeton University.
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