Schedule 2019

CYPHER 2019 - 18-20th Sep | Bangalore.
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  • Day 1

    Sep 18, 2019

  • As machine intelligence matures and gains adoption, the role of the knowledge worker is evolving. Genpact’s take has also evolved along with the industry, combining domain expertise with services and technology in order to deliver Augmented Intelligence. Join Vikram Mahidhar SVP - Artificial Intelligence Solutions at Genpact for an in depth understanding of Augmented Intelligence
    Hall 1: Keynotes & Panels

  • Getting your first job in Data Science is difficult. You’ve been applying to jobs, but they keep rejecting you. You don’t know what to do and how you could differentiate yourselves amidst the pool of candidates? In this talk, we’ll be going through different tips and techniques you could use to find that elusive Data Science jobs. They’ve worked for me and probably will work for you too!
    Hall 2: Knowledge Talks

  • Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
    Hall 3: Tech Talks

  • Innovation has been the cornerstone of all human evolution. Numerous evidences have been documented about how early humans used inventive approaches to survive in unpredictable environments and made steady progression into culturally advanced ecosystems. Likewise, companies today are faced with unprecedented changes in their business environment owing to volume of data and overwhelming technology disruptions. Surviving and leading in this new business landscape requires companies to continuously re-think and re-design their business models in line with the right leadership (emotional intelligence) and ‘responsible AI’. Through this session, the speaker presents his thought leadership in leveraging organizational capability and cultivate the culture of innovation and collaboration to stay ahead
    Hall 1: Keynotes & Panels

  • There are a lot of megatrends that have surfaced in the last couple of years like Digital Transformation, Big Data, AI, Blockchain, Customer Experience, Personalization, Industrial IoT, or Digital Factory, and all of that. These mega trends have led our clients to invest a huge amount of money in different initiatives, so they had created different centers of excellences, invest in a lot in talent, they are hiring data scientists, data engineers, technology folks and many more. They are investing a lot in creating the infrastructure to support these use cases or initiatives, but these clients have not seen a lot of success. They are sharing with us a lot of frustrations in the sense that they are not seeing a lot of tangible RoI, they are not seeing a lot of adoption from the end business users, and even hiring and engaging the talent that they are recruiting right now is a big pain point. Knowledge management is a big pain point. Governance is a big pain point. So, how do we solve for all of that? Having heard all of these frustrations from our client organizations, we would be sharing some frameworks and case studies which had helped our client network to overcome these challenges.
    Hall 3: Tech Talks

  • While the last decade has given us giants such as Flipkart, Ola, Inmobi, and MakeMyTrip, the next decade belongs to platform companies built upon the data foundation. In this talk, we reflect upon the last decade, look at the current state, and provide a blueprint to win in the platform ecosystem. We will look beyond medium posts, Coursera classes, and StackOverflow chatter, to ask honest questions and explore what we can learn from stalwarts in China and Silicon Valley - the ones who wrote the textbook on winning with data.
    Hall 1: Keynotes & Panels

  • With data science community thriving to decode the so called black-box notion attached with Machine Learning based solutions, Deep Learning poses an even bigger challenge. While building any Machine Learning or Deep Learning based solutions, domain expertise plays a huge role in the selection of features and the subsequent explaining of the model’s mechanisms. Across the research community, there has been some bit of success in feature engineering of numeric and textual data, however replicating that success for visual content is still at a nascent stages. This talk provides an in depth look at some of these challenges of improving AI’s explainability and highlights some of the possible techniques to overcome those challenges.
    Hall 2: Knowledge Talks

  • The Fourth Industrial Revolution or Industry 4.0 considered as an opportunity as well as a challenge offers huge potential to advance economic growth, enhance global manufacturing output and human well-being, to safeguard the environment and to achieve the 2030 agenda for Sustainable Development Goal (SDG) set by United Nations Industrial Development Organization (UNIDO). At Oracle, we work towards building smart industry solutions to build a cloud assisted smart factory (CaSF) system leveraging the potential of Autonomous Database, Analytics Cloud, Machine Learning, Blockchain and Artificial Intelligence that would revolutionize production processes, increase the level of efficiency, security, reliability and enhance living standards. With Oracle’s AI Tech Stack, we build statistical models, means, and forms of intelligent manufacturing, thus not just transforming the industry but disrupting the space.
    Hall 2: Knowledge Talks

  • While lot of advancement is happening in the field of Machine Learning, real life experiences of implementing AI/ML products in field and at Scale are still limited. The challenges of transferring huge amount of data from field to one central computing infrastructure is prohibitively costly specially when we look at Telecom Networks. Building such costly infrastructure consisting of fat data pipes negates the cost case and expected RoI. Also both in Telco and IoT context, often the data is sensitive and private which imposes additional restriction on data transfer. The first part focusses on such challenges from real-world large scale implementation experiences. The talk then moves to on the emerging solutions for such challenges by employing Federated learning whereby the volume of data transfer is drastically reduced and all of the limited data moving across the network is effectively secured as only computed weights are shared through a secure aggregation mechanism. The talk also shares benefits of such methodology with some indicative comparison of accuracy and benefits The talk is followed by a technical session in the afternoon , where Data Scientists will guide a technical audience to code a FL algorithm on simulated data and compare performances of Centralized vs Federated models
    Hall 1: Keynotes & Panels

  • We now live in a Data driven era, and industry is excited about data that has led to many buzz words such as Analytics/Advance Analytics, ML, DL, AI, RPA, etc. In this presentation, we discuss about what data driven culture in an organisation means and whether data science and these buzz words are enough to drive the culture? We will discuss about the hall marks of a data driven culture. We then break down into what are the characteristics of a data driven culture in an organisation. The presentation is based on successful case studies of the author who implemented data driven culture in organisation.
    Hall 2: Knowledge Talks

  • Unifying consumer information across multiple channels has long been advocated as the first step in reinforcing this brand promise. While this is correct, it extends well beyond creating a unique ID or implementing a CRM solution. Insights drawn from web analytics must be used to shape search engine marketing (SEM), content marketing, social media, blogging, and email strategies for generating interest in a product. Performing analytics on this wide source of data across multiple dimensions will assist retailers in offering tailored shopping experiences and communicate to consumers that loyalty is valued regardless of channel
    Hall 1: Keynotes & Panels

  • This session will provide a glimpse of Aditya Birla Group DNA’s efforts to deploy AI/ML solutions in production. It will showcase industrial applications for computer vision (perceiving the seen) and forecasting techniques (foreseeing the unseen). The technical session will do a deep dive into the technical challenges encountered and how they were overcome.
    Hall 3: Tech Talks

  • Applications of Artificial Intelligence (AI), support human innovations that benefit society, and show promise in multiple industries to enhance individuals’ quality of life, improve safety and drive environmental sustainability. These applications can recognize patterns, draw inferences and automate decision-making, thereby, turning large amounts of data into valuable insights and actions. Modern agriculture is being transformed by digital technologies, including AI. This includes innovative digital tools that process massive amounts of information offering farmers customized insights into their operations. Here, I will present how AI is transforming agriculture into Intelligent Agriculture.
    Hall 2: Knowledge Talks

  • Rapid digitalization has led to incredible innovations across industries today. The payments industry is one such industry. But given India’s scale, complexity and historically flaky infrastructure the payments industry sees a significant number of transactions fail causing significant opportunity loss and customer dissatisfaction. Improving success rate of transactions is a critical business goal which everyone in the ecosystem has this includes Merchants, Payment Processors and Banks. Conventional methods based on a reactive approach of responding to alarms triggered by static thresholds are insufficient in a high volumes, high velocity competitive industry. Attend this session to understand the application of Big Data and Artificial Intelligence techniques to interpret the available data in real time and automate decision making to improve payment success rates.
    Hall 1: Keynotes & Panels

  • The talk is followed by keynote speech of Kaushik Dey, where Data Scientists will guide the audiences to implement a Federated Learning Algorithm on anonymized real data and compare the performances of Centralized vs Federated models. The session will cover the following aspects of the topic: o Introduction to de-centralized model – federated learning o Technical architecture of federated learning o Introduction to PyTorch o Illustration of data generated from 4G eNodeB o Business importance of predicting network KPIs o Developing centralized models of throughput degradation based on 4G network performance KPIs o Develop FL models using deep neural nets o Performance comparison of centralized and de-centralized models o Challenges and open areas of Federated learning
    Hall 3: Tech Talks

  • Predictions about AI often suggest there will be significant disruption to existing companies. Balancing that, there are more examples of forward-thinking companies gaining considerable benefit from incorporating AI in their business processes. This talk will focus on how AI offers solutions to a previously unsolvable class of important business challenges. And in doing this, how AI can offer a non-disruptive path to greater efficiency and profitability for existing companies who take the lead in adopting this new innovative technology.
    Hall 1: Keynotes & Panels

  • As we all know BFSI is a data intensive sector. Data is growing at an exponential rate and is expected to cross hundreds of Zettabytes by 2025. Many banking, financial services and insurance (BFSI) players have turned data worshippers. Analytics is enabling the BFSI to become smarter in managing the myriad challenges it faced. The economic crisis of 2008 changed the face of banking industry and now analytics is helping banks becoming smarter in managing these challenges. Data has changed the way BFSI operate. Analytics has become the key determinant in matters pertaining to core BFSI operations, risk projection and customer relationship management. Ms. Dyuti Lal from Nikhil Analytics will be specifically emphasizing on Analytics in Banking Financial Services and Insurance (BFSI) sector along with its use cases. A few decades ago, banking processes were transformed by IT systems. These days, it is the analytics that is facilitating banks and financial services to make them compliant, which is undoubtedly putting them one step ahead of their competitors. Analytics helps to analyse enormous datasets to uncover market developments, consumer likes, data interactions, and other key insights which assist in strategic planning in BFSI organizations.
    Hall 2: Knowledge Talks

  • 80% of any machine learning project involves data collection and preparation so that you could train machine learning models. This session would focus on how AWS makes it easy to collect and process this data and provide your data scientists with the tools they need to train and deploy predictive models using this data
    Hall 2: Knowledge Talks

  • Advances in communication technologies, devices connected to the internet and data analytics are occurring at a much quicker pace than at any other time in history. As a result, many believe we are now living through a fourth industrial revolution, referred to as ‘industry 4.0’ (i4.0). As Industry 4.0 continues to change the way we interact with the world around us, new challenges arise.  This talk will explain the critical components of INDUSTRY 4.0, the new business models and the technology evolving. How organisations are rethinking all their business and architectural process. What are recipes needed to identify which projects are i4.0 ready and how to conduct successful pilots and scale them.
    Hall 1: Keynotes & Panels

  • Suppose that a customer who has given a high rating about a mobile phone writes the following review about the product: The front camera of the phone is excellent! Truly speaking, this is the best front camera I have experienced so far. From this review, we can understand two things. First, the customer holds a positive opinion about the phone. Secondly, the front camera of the phone is the targeted feature on which the opinions have been expressed in the review. In this workshop, we will be particularly interested in discovering patterns as indicated in the second case. We will discuss a framework that enables us to first discover the targets on which the opinions have been expressed in a review and then determine the polarity of the opinions. This kind of detailed analysis helps us to discover the components or features of the products which the customers have liked or disliked and thus help us to better summarize the information.
    Hall 3: Tech Talks

  • AI is here, call it buzz, cause it a bubble, we are smack in the middle of an AI revolution. While there is a strong view building about consumer AI applications, there still seems to be some scepticism about AI for enterprises, primarily due to the lack of clarity and focus on how AI can actually deliver value for enterprises. At BRIDGEi2i, we believe it is important to have a non-fragmented view of the AI ecosystem and a “Value Roadmap” for AI in the enterprise context. As CxOs, it is important to understand where the enterprise is in the transformation journey and define value accordingly. This talk will throw light on how to look at the enterprise AI ecosystem and build the right roadmap for value.
    Hall 1: Keynotes & Panels

  • According to a recent Gartner report, Augmented Analytics is set to be one of 2019’s top strategic technology trends. Augmented Analytics leverages ML/AI techniques to transform how analytics content is developed, consumed and shared. It automates data preparation, insight discovery and sharing. It also automates data science and ML model development, management and deployment. In this session, Chandra will talk about how Augmented Analytics is helping democratizing AI with unbiased decisions and ways it will transform the entire analytics workflow to modernize and drive digital transformation and innovation.
    Hall 2: Knowledge Talks

  • The proliferation of biological, clinical, medical, regulatory, commercial and customer data as they relate to the pharma, biotechnology and medical devices industries promises to transform how life science companies will research, develop and commercialize products and therapies in future. The explosion in data coupled with AI/ML technology presents an unprecedented opportunity to unlock value through data science and analytics and thereby deliver smarter and more personalized solutions to patients and consumers. This presentation will discuss the need for verticalization of horizontal AI/ML technologies for the life sciences and profile the opportunities for advanced analytics to transform every aspect of the product value chain – R&D, regulatory affairs, safety, medical affairs, sales, marketing and market access.
    Hall 1: Keynotes & Panels

  • The most common question marketers ask now is - “Did my ad campaign cause the user to convert and generate more revenue for my brand or would that have happened anyway?” The Complexity of the ad-tech ecosystem is constantly growing with brands running marketing activities across multiple channels, new targeting capabilities, and formats. Due to this, traditional digital marketing measurement metrics like cost per click, return on investment, cost per conversion, etc just scratch the surface while measuring the impact of marketing strategies. Lack of a mathematical approach to differentiate between correlation and causation leads us to look at the incremental lift as a metric to measure the impact of marketing strategy. In this session, we will introduce incremental lift measurement, talk about common challenges faced in calculating incremental lift and different approaches to tackling them in the digital marketing ecosystem. Our methodologies cover concepts of various A/B testing environment setups, randomization, bias handling, delivering outputs at scale and how we use these outputs for marketing strategy planning and optimizations helping us achieve higher campaign efficiency.
    Hall 3: Tech Talks

  • Regardless of the size and nature of businesses, rapid adoption of Machine Learning and Artificial Intelligence is a must-win battle for enterprises. Non-adoption of those can put them in disadvantageous trajectories. Enterprises fail to capture the true potential of ML and AI because of:  Suboptimal approaches due to lack of contextualization of ML and AI to their businesses.  Slow adoption due to the complexities involved in connecting to different data, tools, technologies, techniques, and, nuances of deployment at scale.  Lack of disciplined approaches, operating in silos, and limited collaboration amongst functions and teams.  Ineffective traditional methods to institutionalize the experiences and learnings as well as significant dependencies on human talent. An analytics eco-system that is self-sufficient within an organization and enables all the stake- holders to strive towards a common goal is a solution. In this talk Satyamoy will dwell on the different components of an analytics ecosystem and how Analyttica is operating at the confluence of “Analytics experience”, “Data science expertise”, and “Technology excellence” to create significant value for enterprises in their ML & AI endeavors by via an adaptive analytics ecosystem.
    Hall 1: Keynotes & Panels

  • Talk about 3 different use cases of data science at Shaadi.com, like identifying bad photos, detecting fraud users and recommending profiles to users.
    Hall 2: Knowledge Talks

  • Day 2

    Sep 19, 2019

  • AI is gradually gaining a significant position in cybersecurity both on the offensive and defensive sides. This talk will introduce the applications of AI in cybersecurity defensive strategies against the continually growing amount of cyber-attacks, data breaches. On the offensive side, the talk will review possible implementations of AL/ML by adversaries in reconnaissance, infiltration, and exploitation of vulnerable systems.
    Hall 1: Keynotes & Panels

  • Artificial Intelligence (AI) is today’s most exciting technological frontier. Systems that are capable of simulating intelligent behavior are already being used to combat fraud, help smart speakers answer you back and make it easier to hail a ride-share when you want it. And it is just getting started. In the future, everything from autonomous transportation systems to advanced robotics will be made possible through AI. With so many exciting possibilities, no industry wants to miss out on AI’s true potential. What this means is that right now is an excellent moment to build AI capabilities and skills at your organisation. This panel will focus on 3 key areas: - Problems AI is solving at scale today - Building data capabilities to leverage the full potential of AI - Bridging the existential crisis of technology skill gaps
    Hall 1: Keynotes & Panels

  • An insurance company wants to identify photo-journalists at risk, an oilfield services company wants to identify personnel at risk at their work locations, a chemical company wants to improve plant growth - and all of them are adopting innovative techniques to solve these problems, using the emergence of AI & Deep Learning. It has now become possible for organizations to exploit rich unstructured data to gain insights. These include video stream data from CCTV and drone footage, audio data from customer calls, and text from social media interactions. This talk will describe in technical detail how image classification, object detection, and segmentation techniques are being used to solve such a diverse range of problems. It will also expand on the common roadblocks encountered such as lack of sufficient training data, incompleteness of architectures, which often lead to poor accuracy.
    Hall 3: Tech Talks

  • Anurag will talk about his experiences in helping clients implement AIML models in production. He will talk about the challenges faced and how different companies go about it.
    Hall 2: Knowledge Talks

  • What are the top barriers to AI adoption? Some of them are lack of skills to design, implement, and maintain AI solutions, and no clarity on where to use AI effectively. Come and learn about Genpact’s Cora Pre-trained AI Accelerators to address the critical barriers to AI adoption in the enterprise. Join Amaresh Tripathy, SVP and Global Business Leader for Analytics in Cypher2019 to learn about Cora Pre-trained AI Accelerators
    Hall 1: Keynotes & Panels

  • Managing customer churn is vital to business performance. As is researched and proven, a 5% improvement in customer retention rates increases profitability by 25 to close to 95%, as it is many times more expensive to acquire new customers than it is to retain an existing one. Understanding controllable and uncontrollable drivers of customer churn is essential in order to increase customer engagement and designing short- and long-term retention strategies. Below is a “Customer Retention Framework” we developed for one of our F1000 clients in the Logistics domain: Define Churn: As a first step, we defined churn for our client, keeping their business in context and the category of customers they wish to retain. Identify Customer Segments: We used RFM-LP (Recency, Frequency, Monetary Value, Longevity and Profitability) as the key metric to segment their customers into Bronze, Silver and Gold. This metric was used as a proxy to “Loyalty”. Develop a Hypothesis Map: There are multiple reasons, controllable and uncontrollable, why customers switch. We leveraged our experience working in the industry, secondary research and client (business and field) interviews to generate a list of potential churn triggers. We call this collection of hypotheses a hypothesis map. This map should be MECE (mutually exclusive and collectively exhaustive) and is the basis for predictive model(s) to be developed in the next phase of the project lifecycle. Build a Predictive Model: Leveraging the hypothesis map, Evalueserve developed a data-mart which had all the relevant data harmonized and in the required format. A predictive model was then developed to calculate a risk score for all the customers. The model does not only help the client identify customers at-risk of attrition but also understand the triggers behind the same. Create an Action Item Matrix: Actionable insight is key to a successful analytics project. For this purpose, we developed an action item matrix (a framework that brings together the results of the model developed and specific KPIs such as probability of churn and customer lifetime value) to help our client prioritize retention efforts and determine ROI for that effort. The matrix further helped them in identifying up-selling and cross-selling opportunities. Operationalization Strategy: Evalueserve helped the client in designing a retention framework and then in designing a pilot to test out the same. Once the pilot was deemed successful, the framework was rolled out enterprise wide. In my presentation, I will focus on (i) Problem Formulation (ii) Solution Design (iii) Insights to Action Framework and (iv) Operationalization Strategy.
    Hall 3: Tech Talks

  • Akhil will covering the subsets of: Big Data verses Deep Data Behavioural Data via customer interface - app & web Data captured via IoT devices - smart TV, lock, sensors, voice assistants,etc. Insights on design architecture and customer behaviour Impact on customer and business innovation. What next?
    Hall 2: Knowledge Talks

  • Courts in most states of the US provide judges with an algorithm-based risk-score that predicts the likelihood of a criminal committing a repeat offence. Judges use this score as an input while deciding the quantum of punishment given to the criminal. However, data shows that the algorithm predicts higher risk for a black criminal compared to a white criminal who has committed the same offense and has a similar history of crime. Google’s image search algorithm could not differentiate between gorillas and dark-complexioned human beings, producing many offensive search results. Google has now “switched off” the search for gorilla, chimpanzee and similar labels in their image search algorithm. Traditionally, we tend to label these kinds of issues as “algorithmic biases” and try to fix the algorithms. But the reality is that it is the inherent bias in data on which the model is trained, which in turn is a result of past human behaviour, that has led to these erroneous results. We call this “inductive bias”. While the above examples are extreme cases, inductive bias can creep into ML models that we develop for day-to-day business purposes, leading to wrong insights and counter-productive actions. For instance, in the developing countries, auto insurance premium prediction models tend to recommend higher premiums for customers living in less-affluent neighbourhoods. The origin of inductive bias lies in human bias and results don’t seem suspicious at first sight. But as ML and AI become mainstream in business and society, from potentially large business losses to unfair punishments and even loss of lives in extreme cases, inductive bias may be wreaking havoc without us realizing it. How do we overcome inductive bias? The solution to overcoming inductive bias lies in the emerging field of Data Ethnography, a cross-section of Data Science and Ethnography. Just like Ethnography provides insights into human behaviour, Data Ethnography provides insights into the behaviour of data that is being fed into the model and helps fix any inherent biases. Just like Excel and SQL, Data Ethnography is going to be an essential skill for Data Scientists and Data Engineers in the immediate future.
    Hall 1: Keynotes & Panels

  • Organizations of all sizes across industries are trying to leverage Data and Analytics to gain a competitive edge. For that, they need to “Institutionalize Analytics”. What that means is three things – (1) Analytics has to be a part of and aligned with the overall Corporate Strategy, (2) key decision makers take decisions based on data-driven insights rather than gut and instincts and (3) analytics projects should be operationalized in a manner they generate tangible ROI. In order to achieve the above, companies have made large investments in people, processes, technologies and consultants. However, a majority of these companies have failed in their first few initiatives only. Why? In this Key Note, he will share Best Practices to successfully institutionalize analytics within your organizations.
    Hall 1: Keynotes & Panels

  • Businesses are collecting more and more data every day. Vast amounts of third-party data is also publicly available. It is almost impossible for businesses to analyze all the data available at their disposal. Enterprises need a better system to generate insights across various aspects of the business, easily analyze different scenarios and get prescriptive recommendations. ‘Autonomous analytics’ much like ‘Autonomous cars’ helps in identifying anomalies as they occur, uncover opportunities as they unfold, find root cause and suggest necessary actions. These analytics are adaptive, instantly evaluate outcomes and help re-align or auto-correct to achieve desired outcomes based on the business situation.
    Hall 3: Tech Talks

  • The nature of our business warranted a platform which can improve connect with our clients with relevant message, through the right channel, at the right time. This required a continuous learning engine powered by single source of truth at the customer level. Our automated analytics platform is updated with customer data, the industry data and the market data harmonized at customer level to power a deep learning ensemble algorithm which predicts the next best action for every customer. The integrated platform triggers communication through digital mode to the customers with the most relevant advice. This ensures we improve customers' digital experience and recommend the best next action by identifying their individual needs and behaviours. Additionally, the platform powers information to the management team to take timely strategic decisions as well as helps the ground team with relevant information to help optimize their productive bandwidth.
    Hall 2: Knowledge Talks

  • A deep dive into two very diverse application areas where ABG DNA team is leveraging advanced analytics. Learn about the use of advanced algorithms and technologies required to make the best quality fashion available to you; producing high quality fibres for textile to ensure durability of the fabric or coming up with new recipes of colours to introduce different shades and weaves, or designing outfits to initiate trends. Analysis of high frequency industrial IoT data that has pushed analytics to the edge of manufacturing processes to produce better products touching more areas of your lives, from cola cans to the guerrilla glass on your smartphone.
    Hall 2: Knowledge Talks

  • While the desire to take advantage of artificial intelligence (AI) remains high, we still see constraints in the levels of adoption. The problem does not lie in the technology itself, but rather on the roadmap to scale up AI adoption in large organizations.
    Hall 1: Keynotes & Panels

  • What happens when a race team has all the insights to make fast, accurate decisions, working as a fully connected unit that adapts to complex, shifting conditions? Podium-winning performances and an instinctive racing team.
    Hall 3: Tech Talks

  • Manu will talk about his experiences with using some of the recent advances in text analytics/ NLP/ NMT including BERT, GPT2 and XLNet
    Hall 3: Tech Talks

  • The most successful modern businesses are data driven. They augment data analytics with technological advances in AI to be faster, more efficient and more innovative than their competitors. Many of our clients know they need to tap into AI’s massive potential, but don’t have the in-house expertise or don’t know where to begin or have trouble scaling and realizing value from their solutions. We exist to help customers figure out where to start, how to create and democratize enterprise AI solutions that deliver desired business results. We help customers apply ML, DL and advanced data sciences in Computer Vision, NLP and Enterprise Knowledge domains through design-led interaction surfaces.
    Hall 1: Keynotes & Panels

  • As analytics folks we’ve been used to a world where we build models, provide insight, drive action. However, a new way of delivering that’s become more apparent is where we build Boxes. Boxes that come with their own challenges to build, deliver, integrate, maintain. Boxes that need to work with other boxes, that need to be accepted by the people using them. In this talk I’ll share our experience of making the shift – the hell yeahs and the oh hell moments, the change management we’ve had to drive. Expect toons.
    Hall 1: Keynotes & Panels

  • In order to leverage data insights to guide strategic decisions, companies need to build a strong analytics team. Understanding the key traits of an effective predictive analytics professional will help you identify the right people to upskill within your organization. Understanding what motivates your analytics professionals will help you attract them and knowing how best to support your data scientists will help you retain them. By having a better understanding of your data scientists, you’ll be able to attract, groom and grow your analytics team.
    Hall 1: Keynotes & Panels

  • While failures of software engineering projects had been understood and contained, data science is exposing project failures that are causing some to pause. Some say 90%+ of Data Science projects never see the light of the day. In this techno-strategic talk, we look at on-the-ground reasons why and provide a blueprint to avoid your project becoming one of that statistic. Build Different. Be Different.
    Hall 3: Tech Talks

  • A look at the future evolution of Analytics as pertains to business decision-making. With the advent of information age, humans have become very adept at quickly processing complex data signals to take better decisions. Decisions which benefit from at scale/more accurate/real-time data inferences over subjective human-intuitive insight are bound to change fundamentally and are prime candidates for productization. Enterprises must adapt to this new paradigm as they place bigger bets on data-based decision making and invest in people and technology to reap greater rewards from analytics.
    Hall 2: Knowledge Talks

  • Solving Time Series has really become important for almost all industries be it Retail, Finance, Manufacturing or any other industry. Traditional statistical techniques has been promising to solve time series with a reasonable learning. But in the current world of AI, Neural Network architectures have shown capability to learn latent behaviour of the time series which in turn has improved the learnability manifold times. The complexity lies when there are multiple time series to be solved as it is not possible to train individual models for each time series due to computational constraints as well as time complexity. The session aims to talk about the Neural Network architecture for solving multiple time series with a fully connected network and analyse the learnings model gathered with the architected network. It also gives a flair of the art of optimal data loader creation and effective batching to ensure maximum cross learning across time series.
    Hall 2: Knowledge Talks

  • Every year over 1.3M people die on roads. In recent years the fatality and collision rates have only gone up, reversing a several decade long downward trend. So even while cars are becoming safer, this recent increase is largely attributed to distraction caused by the use of mobile phones while driving. Zendrive, a company committed to making roads safer, is using the same smartphone data to understand unsafe driving behaviors like aggression, adherence to the rules of the road and distraction. Using sophisticated machine learning techniques and massive amount of data (180B miles of data over 60M users) Zendrive has built world’s leading driving behavior analysis platform that has already helped save hundreds of lives. This data and analysis also find a wide variety of usage in insurance, city planning and driver coaching. In his talk Pankaj Risbood, Co-Founder and VP Data, Engineering at Zendrive will take you on a fascinating journey of measuring driving behavior, quantifying risk and creating incentives for drivers to adopt safer driving habits.
    Hall 1: Keynotes & Panels

  • Ronald Fisher considered as father of Statistics discovered Discriminant Analysis(DA) in the year 1936 as a scoring model to achieve the largest possible separation between classes. The model involved a linear discriminant equation of input variables called the Z score to classify the individual records. Support Vector Machines(SVM) which was originally invented in 1963 by Vladimir N. Vapnik and Alexey Ya. Chervonenkis has the same goal of achieving the largest separation between classes specifically binary, but can be extended to multiple classes. SVM separates the two classes by drawing the best hyperplane because many hyperplanes can produce the solution. Both DA and SVM use hyperplane as the criterion to separate the classes. While DA finds the best hyperplane that maximizes the distance between the group or class centroids subject to the variance of the hyperplane set equal to one, SVM finds the best hyperplane that maximizes the distance margin between the closest data points to the hyperplane on either side of it. The talk will focus on comparing and contrasting DA and SVM as classifiers from the perspective of Analytics. The similarities, differences, explanatory power, and accuracy will be delineated threadbare for both DA and SVM so that analytics professionals can leverage on their strengths for better insights in the context of classification. The entire philosophical convergence between SVM and DA will be demonstrated with excellent and relevant data sets.
    Hall 3: Tech Talks

  • Quantum Computing is a promising computing paradigm that has made good advances in recent years with latest hardware platforms including Google’s 72 qubit Bristlecone processor and trapped ion quantum computer with 160 stored and 79 processing qubits from IonQ. There are many emerging and proven applications of Quantum Computing including molecular modelling for faster simulations leading to development of more efficient products, cryptography for more secure encryption techniques, financial modeling, weather forecasting, particle physics modelling for complex physics simulations and artificial intelligence. In the last few years, researchers have investigated leveraging quantum computing to improve classical machine learning algorithms. Ideas range from running computationally expensive algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. In this talk, I will give a systematic overview of the emerging field of quantum artificial intelligence, along with current approaches and technical details and will highlight the opportunities and challenges for practical AI applications.
    Hall 2: Knowledge Talks

  • https://www.analyticsindiasummit.com/about/awards/
    Hall 1: Keynotes & Panels

  • Day 3

    Sep 20, 2019

  • This talk is about exploring the main question that we are trying to answer, then finding the answer and lastly looking at the relevance of AI in that answer which leads us to the monetary value attached to it !
    Hall 1: Keynotes & Panels

  • Organizations today are using complex AI algorithms and immense computational power to enable their teams and organizations to make smarter decisions. Employees across business verticals with varying skill sets and maturity should have access and be able to absorb the abundance of information and intelligence with confidence to make actionable decisions. How do we help everyone achieve more — humans and machines working together to enhance efficiency, profitability and drive tactical and strategic decisions at scale.
    Hall 2: Knowledge Talks

  • The talk will highlight the need to rethink learning, creating more room to do innovation and experiments in corporate world. Two interesting stories will be narrated to bring out the state of affairs currently in industry and academics. I will draw from my experience, strategy and tactics for continuous learning and building culture of experimentation –intensive contemplation, going down the rabbit hold and multiple trails. Finally, concluding with the _ONE_ skill that’s dwindling and stopping the experimentation to flourish.
    Hall 1: Keynotes & Panels

  • Services have continued to dominate the IT, BPO, and  KPO landscape for the Indian outsourcing ecosystem. Services played to the strengths of Indian ecosystem due to significant young population that was trainable, adaptable, and scalable. The Analytics ecosystem followed the foot steps and provided initial success. However, as the industry matured there has been an increasing trend of outsourced Analytics to in-sourced Analytics plus customers are expecting more. How do you play to the new dynamics? TEG Analytics pivoted to a Product first strategy that allowed TEG to deliver significantly higher value to its customers and employees. Arvind will share his organizations journey that has been the cornerstone of their recent growth. You will get to hear  Trends in the Analytics industry  Managing Change  Developing Product mindset  Lessons Learned  Keeping your head above the water
    Hall 1: Keynotes & Panels

  • Insurance has been a traditional, century old business affair and has largely been unperturbed through the immense technological disruptions & advancements that have been made in the last decade or so. It has to take a leap of faith to evolve out of the traditional models of insuring property, casualty and commercial line items through leveraging AI & emerging technologies such as distributed ledger blockchain in order to revamp its presence to customers My talk will focus on my work in niche microinsurance based product offerings which are tailor made for a focussed audience group, such as: - Easy-to-purchase flight delay insurance using blockchain architecture (ref.​ fizzy.axa​) focussing on millenials and frequent flyer groups - Usage based motor insurance as per the driving behaviour & risk score of the user - Parametric event interruption insurance (such as rains during cricket match etc.) I shall specifically stress on how are we building active machine learning workflows in designing the experience these products cater to their users while also touching upon the architecture which makes it feasible for implementation and monitoring production in the long run.
    Hall 2: Knowledge Talks

  • o Impact of Analytics in Manufacturing o With slow down in Automobile / Manufacturing how it has driven more need for Analytics o How IOT & Cloud has helped accelerate the journey o Is Analytics in Manufacturing only for high labor cost country o Data Product Approach - From POCs to Scale o Demystifying - Edge computing , IOT o Power of Visualization – From Shop floor to Top floor o Predictive Analytics reality
    Hall 2: Knowledge Talks

  • The panel discussion is aimed at discussing various career options in the fast-emerging field of data science and orchestration of AI/ML. The panelists will share their perspectives on - what are the different roles that make data science initiatives successful - what does it take to start a career in data science - How to transition into a career in data science, if you started career at a different domain - what are the career options do you have as you take on more responsibilities in the context of data science strategy, roadmap and challenges faced while implementing AI/ML. The audience should expect to get better understanding of the data science domain, career options prevalent, skillsets required and roadmap to pursue a successful career in data science at multiple levels of experience.
    Hall 1: Keynotes & Panels

  • Zoomcar is India's largest self drive shared mobility platform. We are working extensively in this field of improving driving behavior, to ensure safety of our customers and encourage better driving. We have IOT enabled cars with dash cams which help us achieve the above. Few features are already rolled out and few will be rolled out in a month from now. The outline of the talk will be: 1. Understanding the self drive scenario in India 2. Current driving patterns and gravity of the problem 3. Tech behind the approach 4. Using computer vision to give real time collision warnings other alerts 5. Creating India's first indigenous Driver score to encourage better driving 6. Impact of the project 7. What's in it for others
    Hall 2: Knowledge Talks

  • Challenges we face in implementing AI /Analytics in healthcare - Use cases where Analytics/AI has been researched successfully - How do Indian companies and research institutes are working towards AI in Healthcare?
    Hall 1: Keynotes & Panels

  • With the buzz around AI and ML there is an increasing tendency for leaders and data scientists to move towards complex problem-solving. Its important to unlearn the tendency to gravitate towards complexity. In this talk we will see why avoiding complexity in ML solutions is a wiser and a quicker way to solve business problems. We can also visit some thumb rules to build pragmatic and useful models. Simplicity and sticking to fundamentals is the next "big" thing in the world of big data.
    Hall 2: Knowledge Talks

  • The world is changing faster than ever before and it's impossible to keep up with everything going on. To stay relevant and competitive, one needs to understand the impact of emerging technologies on the future of work. Gaurav Vohra, CEO & co-founder of Jigsaw Academy, India’s top most analytics learning platform, will give you a walkthrough of the emerging technologies of the present and beyond to help understand what technologies can be instrumental in future proofing one’s career.
    Hall 1: Keynotes & Panels

  • Modern complex AI techniques, such as deep learning are naturally opaque. This talk will demystify Deep Learning and provide insights on how by understanding the Mathematical and Programming Architecture behind it, we can make AI systems Explainable. The talk will illustrate how by understanding the mathematical optimization methods behind deep learning algorithms we can understand the Machine’s decision making process. Banking in simple terms is all about using deposits made by public for lending and investments. When decisions are taken in a Bank using savings from public, it’s quite natural that those decisions need to be explainable. This is a straight forward expectation from all banking regulators. Hence by being able to interpret and explain how an algorithm took a financial decision is key to getting the green signal from regulators on using Deep Learning algorithms for applications in Banking services.
    Hall 2: Knowledge Talks

  • Data Lakes are emerging as a critical data management component aimed at limiting traditional enterprise data silos and enabling agile access to all the data needed for faster decision making. However, if we don’t ensure the trust in the underlying data, and track the trust worthiness of data throughout the lineage, the decision makers will not trust the insights generated from these data. This session will share implementation best practices developed by Unilever on building and operationalizing trustworthy insights and ML models on a data lake.
    Hall 1: Keynotes & Panels

  • Application of Information Technology has been one the most significant revolutions in all the industries and Healthcare Industry is not an exception to this rule. With the advent of new and improved technology, the whole concept of how consumers and service providers of Healthcare can avail effective, efficient, accurate, precise and most importantly cost effective care is changing the dynamics of the industry. Technology is something that touches every customer's life and every single aspect of one’s daily journey and healthcare is no such exception. Elaborating on key initiatives of incorporating technology for better healthcare delivery in India with specific examples from Cloudnine, we will elaborate on :  Who is our customer and why healthcare should not be something which is non-digitalized in the journey for this millennial customer. Digital is so core to our business that, unlike a lot of others, we really didn't look at it (digital) as being a separate entity but instead we brought that on completely in-house  Digitalization in healthcare is crucial to making the transformation to a customer-centric industry and Cloudnine is leading this transformation in many ways  Expanding the rural connect, technology and digitalization will come and start playing a major role in starting to provide quality healthcare in rural India. E-NICU, that Cloudnine is piloting is an example of a step in that direction
    Hall 1: Keynotes & Panels

  • In order to make sense of the unstructured addresses / locations in India, one of the important building blocks is: locality polygons. This talk will cover a technique for the generation of locality polygons using historical GPS data, non - linear multi-classification techniques and Open Source Road Networks data.
    Hall 2: Knowledge Talks

Check last Year's Schedule

Schedule 2018