Key takeaways from your session: AI/ML has not only changed the way business is operating but also the software development lifecycle. In standard software development, it was assumed to build a small prototype for testing and then just extrapolate it if it's working. Such extrapolation doesn't work in AI/ML. You have to start thinking about the scale from day 1, even during MVP (Minimum Viable Product) development. Scaling is a huge beast that can't be tamed in the later stages. Let's discuss various challenges you face if you ignore scale from day 1, what frameworks can you use to scale your thought process, as that of your business, while designing an A/ML solution/product.

Nitin is a Machine learning and Artificial Intelligence (AI) professional with a decade of Industry experience. He is currently working as Head of Cloud AI Industry Solution Services team in Google India to lead AI solutions/products development for Google's strategic enterprise customers. He also advises on building enterprise level AI strategy and developing AI capabilities in large organizations. Analytics India Magazine awarded him as '40 under 40 Data Scientists' in 2021. He also co-authored 2 advanced ML courses including MLOps on Coursera for Google Cloud. He also delivers the AI/ML segment of the Mini-MBA program at Rutgers Business School, New Jersey.

Coding standards to be used as a practice
Security checks are to be in place
Vulnerabilities are to be verified while delivering a solution

Director - SAP BTP | Digital Transformation Engineering
o Director - SAP Solution Architect for Machine Learning, MDG, Fiori, S/4HANA and uXLoader for integration between Excel & SAP, AWS, Azure with M/s. Utopia India Pvt. Ltd., Bangalore
o Principal Solution Architect for System Automation, Mobility and Analytics with M/s. Stellium Consulting Pvt. Ltd, Bangalore

Renewable power generation is penetrating at a rapid rate and more and more residential houses would have solar power generation in coming years. There are various challenges like placement of solar panels, sizing of the solar plant, accurate prediction of production and consumption which needs to be addressed and AI/ML can be leveraged to solve these problems.

In this talk, various problems are discussed where AI/ML can bring value in solving some of these open questions. More specifically, the talk would focus on problems related to consumption forecast and associated challenges and possible directions to explore in this field.

He is currently working with Enphase Energy and solving problems related to solar industry using AI/ML

Experienced technology specialist and hands on data scientist with 17+ years of experience in Machine Learning, artificial Intelligence, Data Analytics, Fleet Level Analytics, Fault Detection and Diagnosis, Soft Sensor Development, Machine Learning, Energy Efficiency, Advanced Optimization, Data Based Model Identification and Modelling & Simulation. A Professional with excellent technical, analytical and leadership skills.

As the AI/ML models get increasingly accurate over time using the most sophisticated Deep Learning Models, they are also becoming increasingly difficult to understand as to how do they arrive at a certain prediction/recommendation. This lack of transparency can lead to technical, ethical, legal, and trust issues. This talk discusses classical and modern techniques used to add interpretability/explainability to all kinds of models in the light of their strengths and limitations with real life examples from the industry.

Amit Sharma is Sr Director, Data Science with Global AI Accelerator (GAIA) leadership team at Ericsson.

He is responsible for building solutions using AI and ML technologies to build data driven solutions towards intelligent automation in the telecom domain. Prior to this, he was leading the AI/ML capability for ADP in the HR/Talent domain.

With over 22 years of experience in product development and technology leadership, Amit is extremely passionate about design, architecture, data and loves to solve real world problems with technology.

Dynamic Pricing (aka Revenue Management) was introduced in the airline industry, but is being used in many industries now including travel, hospitality, retail, e-commerce etc. In this talk I show the core components of a DP engine, and how Supervised Machine Learning as well as Reinforcement Learning are increasingly being used in DP. We also discuss some examples of real life use cases of DP.

Rajesh is working as Associate Director in Data Science & Analytics at redBus. He has over 15 years of experience in the industry in Databases, Data Warehousing & Business Intelligence, and Data Science. He is also pursuing his doctoral studies at ISB Hyderabad in Quantitative Marketing.

Artificial intelligence/machine learning (AI/ML) has the potential to generate huge business value for semiconductor companies at every step of their operations, from research and chip design to production through sales. Semiconductor companies have made significant investments in AI/ML talent, as well as the data infrastructure, technology, and other enablers, and Scaling up for a the AI revolution. Lets look at the AI eco system that Semi conductor companies are enabling , - Image / Video Processing – Autonomous vehicles , eCommerce Applications, Remote connectivity, Healthcare , Proteomics etc.. - Natural Language Processing - text analytics, sentiment analysis, chatbots etc.. - Recommendation systems - personalized advertising, product recommendations, etc.. - Threat Analysis Systems – Cyber security, Surveillance camera systems etc..

Seasoned Software Professional with +19 yrs of extensive experience in building complex products and managing large site with cross functional teams. I have delivered multiple products over the years by building teams from ground up and managing R&D. Worked extensively with Global teams, Finance, HR, and Field support units. Full responsibility of Organizational Strategy, Execution and Site Management” I have developed Imaging products in Healthcare and Semiconductor domains. Started career as a software Engineer and worked in various roles with increasing responsibilities. Proven experience of developing people and creating product software following the toughest standards . In-depth global work experience (US ,China, Europe, Israel) and strong Technology background. Product innovation and execution are my key strengths. I have pitched new ideas to VC teams and have 3 patents in new business area including AI & Machine learning .

Understand how investment banks are making Artificial Intelligence central to their market offerings and operations. How AI is impacting the structure of a bank and helping them navigate and become resilient in trying times of high inflation and costs, criticism against energy financing and market volatility.

Kovid Bhardwaj has close to 17 years of global experience working in investment banking and consulting in mergers and acquisitions, corporate finance, derivatives pricing and valuations, credit and market risk, operations, accounting and regulatory topics. He is an expert in leveraging strategic studies, digital approaches, statistics and machine learning to derive key actionable business insights, help transform business models and remodel processes. Currently he is working with Ernst and Young in their strategy and transactions practice. In his prior role he was the global chair for machine learning for risk and finance he is in-charge of evaluation, execution and providing guidance on methodologies and implementations for strategic transformational projects in credit risk, market risk and accounting. His areas of interest are leveraging ML to solve interesting problems in corporate and investment banking for pricing derivatives, client performance and risk managment. He is passionate about data.

Kovid is a CFA charterholder from CFA Institute, USA and MBA from Indian Institute of Management, Lucknow.

Though automated testing has been around for decades, there are several problems plaguing the scale of adoption. This talk will address how to improve automation using several techniques of machine learning ranging from predictive analytics to self-healing to cognitive analytics.

Mallika is a Managing Director at Accenture and leads the Quality Engineering capability Innovation for Cloud First. With 23 years of IT industry experience, she holds 9 patents and is a published author of "Why Humans Matter More Than Ever".

Conducting Machine learning Proof of Concept (PoCs) is a very common practice these days due to the recent AI boom. And afterward, if (very fortunately) you get good achievement in the PoC, you may want to put the PoC system into production. However, while a lot of knowledge has been shared about exploratory data analysis and building predictive models, there is still not much knowledge on how to put them into practice, especially in production.

In this session, we will examine what is needed technically during the transition from PoC to production operations.I will share a checklist with you to go through to understand if you are ready for production. I hope that this session will help you to make your machine learning PoC not only transient but also create value through production.

Abhinav is 2nd Vice President at Northern Trust and leads the data science in Alternatives Investments domain. He has previously worked with Google, Fidelity Investments and Fractal Analytics. He had recently applied for a patent while he was working with Fidelity and its under processing with USPTO.

How domain information can reduce error rates in domain agnostic deep neural network

The topic will cover the relationship between NLP and ML, and how NLP is along with ML is helping organizations in improving efficiencies across various disciplines.

Kiran is a seasoned IT professional with 25+ years of experience in delivering value to Fortune500 companies, with MBA in MIA from USA and PG Diploma in AI&ML.

Data Extraction using NLP

22 years of experience in IT Industry, has worked in several management roles as well as senior technical roles, especially focused at Analytics, Data Science and Artificial intelligence. Leading engagements and providing AI ML Technology solutions for Cross industry domain. My Technology areas would include Predictive Analytics (Both traditional ML and DNN), Deep learning neural networks (ANN, RNN, LSTM, CNN), Computer vision, Reinforcement learning, Virtual agents, Biometrics, Image processing and Natural Language processing. I have written many AI project proposals for different diamond clients in my career and got many projects. I play major role in getting AI ML projects to the organization and also taking the complete responsibility of end-to-end deployment starting from getting the projects to the organization.

An intelligent accident management system powered by AI technologies can help save lives of several road accident victims. Also, it can help estimate the extent of debris at the accident spot, clear the debris quickly and manage the smooth operation of traffic.

Has 19 years of experience in data science and quant research. Have handled key data science projects which have directly impacted client's business metrics.

What is Responsible AI
Why Responsible AI is important
Why Now
Responsible AI principles
How is the Industry Addressing AI (Regs, Consortiums & Frameworks)
How are the key Technology players Addressing Responsible AI
What technology/solution is available to support Responsible AI (start-ups & other solutions)
Putting Responsible AI into Practice [The HOW]

I am a Principal Engineer with demonstrated history of working in the telecommunications industry. I have got skills in Edge Computing, AI, RPA (Sikuli, Automation Anywhere), IoT, Alexa, openStack, Software Defined Networks, Cloud Computing, 5G, 4G (LTE), 3G - Universal Mobile Telecommunications System (UMTS), RAN and Front End Web Development.

I have completed TWO Nano degrees in Artificial Intelligence from Udacity, SDN course from Princeton University, R Programming from John Hopkins University, IoT certifications from UCI and Front End Web Development Nano Degree from Udacity as well.

Apart from this I like mentoring and also an activist on social issues.