Abstract: Enterprises suffer from speed to deliver Machine Learning models at scale as Data Scientists and ML Engineers are constrained by the compute resources at their disposal. With the need to iterate models to product optimal business outcomes, data scientists and ML Engineers need flexibility with compute resources to accelerate the build and deployment of ML models.
At Tredence, we firmly believe in sustaining analytics at scale at enterprise level to drive better returns from the overall data & analytics platform investment. Our Easel Platform is a Workbench for ML Engineering with choice of different programming languages and provision compute resources at ease.
Bio: Changa has 20 years of Consulting experience implementing data and analytics solutions for cross sector clients with focus on reducing costs, improving operational efficiencies and adoption across different business functions. He has seen the transition from database solutions, packaged analytics to self-service advanced analytics over the last 2 decades.