How Businesses Leverage Machine Learning for Better Decisions & Predictions – Practical Case Studies
Abstract
Businesses and enterprises are sitting on a rich trove of complex dark data that has exponential possibilities & opportunities. Big Data, Data Science & Machine learning methods have begun to play a more enabling role for businesses to find insights, and make better decisions & predictions on these large and complex data corpora. However, there should be a well thought out big data strategy, data science & machine learning workflow in place to continuously mine these rich trove of data and identify the opportunities. In this talk, Raghavan will share his experience about the big data strategy, data science & machine learning process that he has architected and implemented across multiple industries such as AdTech, Capital Markets, Pet, Credit Union, and payment industry with some practical case studies.
Bio-data
A Principal Technical Architect with strong business acumen and technical experience in Big Data, Cloud Computing & Traditional IT Projects. Possess more than 20 years of design, development, and management experience in leading complex projects and high-caliber teams in USA, Germany and India.
Achievements
• Interim CTO of one his clients & helped them to secure $20 M VC funding
• Designed Energy Drilling data model that became an important Intellectual Property for his client
• Designed & architected a proprietary data lake architecture for capital market industry
• AWS Big Data Speciality Certified
• AWS Architect Associate
• TOGAF 9Certified
• PMP Certified
• CSM Certified
• Oracle SOA Architect Certified
• All Oracle Java certifications
Specialities
• Turn data into opportunities
• Build MVP products & complex prototypes
• Strategy, roadmap, design & architecture
• Manage highly disparate teams
• Design scalable microservice architecture
• Design & build cloud-based solutions
• Big Data Strategy & Roadmap
• Data Lake Architecture
• Big Data modelling & engineering
• Micro services
• Cloud – AWS
• Enterprise Architecture
Technology Skills
Hadoop ecosystem, Spark, Java/Spring, Kafka, Python, R, Scala, Elastic Search, Neo4j, Cassandra, MongoDB, Traditional RDBMS, Docker, AngularJS, NodeJS, AWS (ECS, Lambda, DynamoDB, Redshift, EMR, Kinesis, IoT, S3, etc)