More than 20 years of experience delivering projects in crucial technology fields such as edge AI and cloud AI, autonomous driving, robots, the Internet of Things, smart cities, and artificial intelligence. published numerous publications in the field of AI and related fields.

Morethan 17+ years of IT Experience with good analytical, technical, and troubleshooting skills.

Extensive hands-on and in-depth experience in the DevOps, Cloud, Big Data, Machine Learning and Infrastructure Architect, Automation and Continuous Integration and deployment.

I am a highly self-motivated, proactive and result oriented individual who takes charge and ownership of challenging tasks and continuous learner

Key takeaways from this session:
The topic will cover machine learning methodologies used for forecasting and anomaly detection to identify events of interest with snippets of relevant applications, approaches, available tools and techniques.

• Data Scientist with 20 years of experience in solving business problems using data
• Background in Economics, Econometrics and Finance – Worked with Genpact, GE Money, GE Capital, CIT, BNY Mellon and currently with Wells Fargo
• Domain expertise in Marketing, Collections, Operations, Balance Sheet Forecasting and Cyber Security
• Hobby: Building stock trading algorithms (Economics + Finance + ML)

Why do we need machine learning interpretability? Scope of machine learning interpretability methods – Local v/s Global, Model dependent v/s Model agnostic. What are the resources available to implement in Python, and the drawbacks and extensions of these methods.

Dhiraj is an experienced data science practitioner having rich experience in the retail industry. He holds a master's degree from the Indian Institute of Science (IISc), Bangalore. Dhiraj has worked on a range of techniques like propensity modeling, forecasting, ranking, etc. across domains like Customer, Pricing and Real Estate. He is passionate about creating and deploying end-to-end data science products for business use cases. He is particularly keen on topics like causality, MLOPs, Design Thinking, Statistical Analysis, Deep learning, and Machine Learning at the edge.

Key takeaways from this session Intro about NLP, Capabilities of NLP, Scope of NLP, Importance of virtual Assistant in current er

What is the difference between chatbot and conversational AI?
Image result for What is conversational AI

Traditional chatbots are built on logic rules and deliver answers based on the keywords that are already incorporated or written in the system. Chatbots won't answer questions that aren't within their algorithmic parameters. While conversational AI is built on natural language processing and response.

I have a B.Tech. and am currently attending IIM Kashipur to get my Executive MBA. working for almost a decade in the data science and artificial intelligence industries. Have done extensive NLP and computer vision work and research.

First I will elaborate on reinforcement learning- how different it is from supervised and unsupervised learning. Then I will take an example and illustrate how to build an industry application and what are the tips and tricks.

Dr.Subarna Roy is a level-3 certified data-science thought leader with 22 years of experience in designing and delivering state of the art AI-ML solutions for MNCs in various domains, e.g., banking, retail, CPG, healthcare, and public sectors. She has a PhD in applications of machine-learning and game-theory. Recently, she has implemented smart replenishment

solutions leveraging forward looking technologies in AI like reinforcement learning in the supply chain domain. She led one of the high impact engagements for IBM to build an AI based fraud detection system from scratch for the Central board of indirect taxation and customs(CBIC) ,Government of India. She also successfully implemented AI and game-theory based dynamic pricing strategies for CPG , manufacturing and oil & gas majors. Earlier she led data-science and predictive analytics engagements for companies like HSBC and TCS.

She is an IBM Quantum ambassador. She was awarded one among the top-10 data-scientists in India in the year 2018.

With the advent of platforms like Chat GPT most organizations are cautious with the use of Generative AI as the risks are not yet fully explored.

Some of the key concerns are around data privacy, accuracy of the results, cybersecurity, regulatory guidelines, among others. Part which must be emphasized is how to ensure the development and implementations of AI systems in reliable and ethical way, enabling organizations to maximize their value.

With over 18+ years of experience, Himanshu currently is managing 170+ consultants and engineers in the area of Digital Strategy, Technology Strategy, and Cloud consulting.

As a seasoned consultant, Himanshu has advised many clients across the globe on IT Strategy, Digital Strategy, IT Operating Model, Cloud Strategy, Cloud adoption, and business and technology transformation.

He has also set up this team from the ground up and is experienced in incubating multiple service offerings under consulting business unit.

Key takeaways from this session:
Importance of Data Science COE
Key components of a DS COE
Steps to establish a DS COE (Building DS COE)
Benefits and impact
Best Practices
Success stories

Sanat Das is the Founder and Director of Navonmesh Labs, a startup company that rethinks wellness solution development using AI and machine learning. With more than 25 years of experience as a data science leader, she worked with Accenture, Oracle, and Infosys in multiple analytics leadership roles. She has two master’s degrees: an M.Sc in Mathematics from Utkal University and an M.S in Computer Science from the University of Massachusetts, Lowell, USA. She believes in creating business value with the right leverage of 'Business Knowledge', 'Data Science', and ‘Technology'.

Key takeaways from this session:
SCM Optimization, Optimized Inventory, network, workforce, holding cost, etc. Explore how native mathematics can solve some of the extremely important issues in SCM

Ph.D. Candidate(Neurosciences) in Data Science by Education, a Data Scientist by Passion and Profession, and a Leader by experience. Blended 15 years of work experience in Technical, Functional, and Leadership skills in the areas of Data Science, Machine Learning, and Deep Learning with a strong Research mindset, which helped me set up frameworks and solutions from scratch. An Influencer, Motivator, and Supporter of all the Good Causes.

Key takeaways from this session:
Emphasize the importance and potential of last-mile AI ensuring continuous value harvesting from AI models

Akshay Kulkarni Artificial Intelligence and Machine Learning Evangelist, an Author, a Speaker, and a Mentor.

He has consulted with several Fortune 500 and global enterprises in driving AI and Data Science-led strategic transformation. Akshay has a rich experience of building and scaling AI and Machine Learning businesses.

He is a regular speaker at major AI and Data Science conferences and has published 8+ books on Data Science/Machine learning. He has delivered 300+ tech talks on data science and AI at various conferences, technical institutions, and community-arranged forums. Also a well-known speaker at O'Reilly AI Conferences.

He is passionate about teaching and acts as a Data Science Mentor at various organizations. He is also a visiting faculty (Industry expert) at a few of the top graduation and business institutes/universities in India.

Adarsha S is a Data Science and ML Ops Leader. He is working on creating world-class ML Ops capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside of the organization to solve problems through training programs and always wants to stay ahead of the curve. He has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains.

- Understanding NLP
- NLP techniques and approaches
- Practical applications and use cases of NLP

Karthik has overall 10+ years of rich experience in the Data Science domain where he worked with companies like Mu Sigma, Infogain & Amazon. He has solved a diverse set of business problems for various Fortune 100 clients leveraging his experience in Machine Learning (ML), Text Analytics, Natural Language Processing (NLP), Deep Learning using scripting languages like SAS, R, Scala and Python.

Karthik graduated from R.V. College of Engineering (Bangalore). He started his career at Mu Sigma as a Data Scientist by building various statistical and ML models to solve business problems for over 3.5 years. He then joined Infogain as a Sr. Analytics Consultant to setup their Analytics Centre of Excellence (CoE) which he built from scratch to scale it up to 10 member team. He spent his last 5 years at Amazon as a Pricing Analytics SME and built the Analytics team which created various pricing strategies with the help of ML model. Thereafter, he was heading the Alexa AI (Knowledge) for ANZ region to help Alexa get smarter and engage better through various NLP techniques.

While working with numbers and solving business problems keeps his inquisitive soul excited at work, he thoroughly enjoys mentoring young professionals/students. Currently he heads the Data Science org at Imarticus Learning, where he built the entire Data Science org from the scratch (currently 15 members) which consists of Data Scientists, Data Science Coaches & Program Managers.

Key takeaways from your session:
what is Qlora and its mechanism and models

Sanjana Ganesh a senior seasoned data scientist at continental automotive , where she leverages her deep understanding of machine learning and artificial intelligence to drive innovation and efficiency . She holds an M.Tech degree and has rich background in various research areas including NLP, time series modelling , AI for inspection, Ai for supply chain and AI for ship planning .