Key Strategies to Develop Successful AI Products

Abstract: Companies are increasingly adopting artificial intelligence, as it helps them reduce human error, cut repetitive tasks, and predict demand. And for most of them, it has become an integral part of their operations. While AI is powering its way into the present and future of computing technologies, there are significant challenges to overcome. And major part of it goes to setting up an infrastructure where you can run AI tools. Only after building a robust AI infrastructure, you can reap the benefits of AI and ML models. Today, for all the successful products like ChatGPT, Weather Forecasting, Smart Replies, or Smart Recommendation, backbone is best infrastructure for data storage, processing, modelling, Monitoring and Response time.

Speaker Biography

She has a masters degree from IIT Kanpur in the field of Industrial and Management Engineering. She is currently working as a Senior Data Scientist at Bosch. She has a 9+ years of hands on experience in implementing and deploying AI models in domains like manufacturing, engineering, sales and supply chain. She has contributed to develop multiple AI products like anomaly detection engine, root cause analysis tool, time series forecasting, sound rating prediction and automating the spray layout design. She has presented a paper on ‘Order Management for Indirect Materials’ in ‘Third International Conference on Business Analytics and Intelligence, IIM Bangalore’ conference. She has also presented paper on ‘Ensemble of Time Series forecasting in Complex Structure’ in ‘5th World Machine Learning and Deep Learning Congress, Dubai’ conference. Her work on how Predictive Analytics can increase awareness and the collaboration in business planning was demonstrated in conference organized by Institute of Business Forecasting & Planning in Europe.