Schedule

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  • Day 1 - Keynotes

    21st May 2021

  • Machine learning and artificial intelligence have long been heralded as the future of transformative technologies. They have been touching every aspect of our life and changing the world around us. When it comes to the med-tech world, the potential for AI and ML technologies is enormous and reaches every corner of it, from diagnostic and imaging technologies to therapeutic applications and robotics.AI can enhance the surgeon’s or practitioner’s capabilities and give him/her “super human” perception, dexterity, and information with which to make better decisions during diagnosis, treatment delivery, surgery and post-operative care.

  • 87% of AI/ML projects never make it to production. Have you as an organization grappled with it? Do you want to know - How to boost performance of the data science team for your organization? How to build end to end ML lifecycle to deliver actionable insights consistently? How to keep your AI models relevant and impactful? Then tune in to our Women in Data as they take you through PS Bodhi – The Cloud-agnostic enterprise-ready scalable AI-ML platform that covers end-to-end workflow from development to production.

  • Selecting the right technology investments will determine enterprise success in the next couple of years. Businesses can leverage cloud, SaaS solutions, IoT, Data & automation to gain agility, scales, and efficiency.

  • One of the most important requirements in Indian agriculture, is to measure crop yields across hundreds of millions of farms, accurately, timely and at high resolution. RMSI Cropalytics specializes in the measurement of crop yields using AI/ML combined with remote sensing, advanced agri-modelling and meteorological domain expertise. Our experts have trained our machines to look at satellite imagery, identify the crop and predict the crop yield in each visible farm. This is scalable, across millions of farms in multiple districts of India benefitting the government and agri-inputs companies. Join us for a discussion on how AI can be applied in the agriculture space to reduce agrarian distress.

  • The presentation will focus on the significant impact of Analytics with Robotics process Automation. Ericsson prepared a solution to convert and process data using NLP technologies and automate the extracted output to reach the end business users using Robotics Process Automation technologies. The solution also helps to sense the pulse of the meeting using sentiment analytics. We will also discuss about the implementation, deployment solution and metrics which was saved by the business.

  • World of sports has evolved by leaps and bounds. It is significantly impacted by the Artificial Intelligence. Both data analytics and artificial intelligence are being used in sports in a substantial amount in audience engagement and viewing experience through graphics, stats, replays, AR/VR, in creating a strategy for games, help coaches through ream time analysis and also in Match predictions, Player predictions and so forth. With lot of synergies it has with the demand of the sports fans, clubs, coaches and players, there is not an iota of doubt that it will immensely flourish this domain.

  • At a high level shall cover what is AI emphasizing on healthcare. Briefly talk about the challenges of leveraging AI in healthcare. Finally discuss how the industry can adapt to AI.

  • Many say that AI is the last innovation to grace the earth, and I agree. Every single industry is being revolutionized by AI and superior data analytics methods whether that be extremely cheap diagnostic systems or automating business processes that have been manual for several years. This sets the ground for why schooling children from a young age on analytics and AI is crucial. In today's day and age, anyone can have the next best technology in mind, access information provided by millions of others on a topic, whether that be a 15-year-old or a 30-year-old, everyone has the same potential, but not aggressively promoting AI education and development early is taking that chance away from young students. These young students are the ones that will be making up the next generation and educating them on analytics and AI early will prove to be a boon. More importantly, educating women in AI has the power to increase female participation in tech by multitudes, AI having applications in every field from political science to fashion.

  • We often hear of ethics being used for risk mitigation methods in AI design and development. Although necessary to the success of AI, risk mitigation only covers half the potential use of ethics when it comes to practically applying high level ethical values to the concrete context of AI systems. If fully utilised, ethics can become a powerful tool used to enable human-centric innovation that both aligns with current regulations and elevates an AI system into a position of competition in the marketplace.

  • As virtually all industries are adopting Machine Learning at a rapidly accelerating pace, successful deployments and effective operations have emerged as the major bottleneck to getting expedient value from ML systems. Learn why MLOps is emerging as one of the hottest topics.

  • Day 2 - Workshops

    22nd May 2021

  • This session will take you through Manisha's journey as a technology leader and starting up LogiNext, a global SaaS product that is redefining the way enterprises use technology to manage their supply chain.

  • Data Science & Data Analytics has become the need of the hour in the education industry. Teacher data literacy is directly related to student outcomes. Teachers today need to collect, analyse and interpret data on a regular basis while teaching in the classroom. This data can also inform a teacher about her/his teaching methods and bring an improvement in her/his performance.

  • Automation is the key to become more efficient in industrial processes or corporate business metrics. The key to automating a process involves an in-depth understanding of time series data across different verticals. Time-series data analysis helps in finding the issues in the system and also forecast the values to prepare for the future. Time series forecasting is used in many domains like weather forecasting, stock market, energy demand, retail etc. In this workshop, we are going to learn about time series data and its characteristics. We will learn about some of the baseline methods and deep learning techniques to predict time series data.

  • In this workshop, we will know about medical images, their types and formats. We will also clear our misconceptions about various types of deep learning problems. We will learn what are binary, multiclass & multilabel classifications and how they differ from each other. We will also see how classification is different from semantic segmentation and instance segmentation with a hands-on example on each type. Further, you will know about Sequential and Functional model architectures, how to build basic model from scratch and use existing tf.keras model api, train model from scratch and use transfer learning.

  • Celebrating Tech Leaders Driving Disruption & Innovation in Data Science & AI

  • In order to create a more gender-inclusive AI industry, it's important to address the skill gap that exists today and this cannot happen overnight. Bridging the demand-supply gap starts with education that focuses on building necessary lifeskills that make our future workforce job-ready. Moreover, building and nurturing an innovator mindset will transform the way our children think about and choose their career paths. This is specifically so the case with girls and women, who look for a tribe that they can belong to. Creating that conducive environment from them to thrive it at a very young age, in order to create this tribe and to feel belonged, and eventually lead these tribes is critical.

  • Education/training, background Key traits needed for success in AI How do you build an AI business? What are the challenges? How has it been being a woman in AI? Why is it important to have more women in AI? Lessons learned