Microsoft Reactor

With all the brouhaha surrounding Low Code and No code AI and ML models, and cloud platforms coming up with out-of-the-box AI or AIaaS (AI as a service) offerings, will it define and shape the future of Data science and Machine learning industry as we know it? Companies who successfully scale AI efforts in the next five years will undoubtedly leverage end-to-end AutoML, from data ingestion to model monitoring. But where does that place the skilled data analytics, ML engineers and other experts in the complex field of statistics, computer vision, NLP, Mathematics which are the building blocks for AI. This table talk, we will throw the floor open to strengths and limitations of AutoML and how well it bodes with the principals of responsible and ethical AI, model explainability, fairness etc. With AI and ML making giant advances, this is a very engaging and intriguing discussion for hard core technical as well as non-technical people.

Speakers:
Priyanka Hemant Shah, Microsoft AI MVP
10 + years of experience in analysis, design and development of client/server, web based and n-tier application in both Java and C#.net technologies as well as UI Frameworks like Angular, Mobile development with NativeScript etc.
Speaker at Microsoft and other technical events for Microsoft Technology stack and ML.NET, Conversation AI, Bot Framework, Azure Cognitive services like speech to text, Face recognition, Custom vision. Working as a solution architect with emerging market equities for building multi-platform and hybrid landscapes using Data science, Azure cloud, Azure IoT , Power BI, data warehouse, machine learning and various other technologies. Working with Fund managers in Data Science and Analytics with Python, ML.NET, web scraping tools and methodologies for sentiment analysis, sales predictions etc. Currently leading innovation technology team with projects architected with Elastic Search, Bot framework, text analytics, spam detection, machine learning algorithms related to neural word embedding, topic modelling like word2vec, LSTM for text completion, auto caption generation. Solution architect for micro services deployed with Azure Kubernetes cluster, Signal R, Azure Cognitive services and more. Active blogger on AI/ML topics.