Shell is an international energy company that aims to meet the world’s growing need for more and cleaner energy solutions in ways that are economically, environmentally, and socially responsible. Powering Progress sets out their strategy to accelerate the transition of our business to net-zero emissions. It has four main goals in support of their purpose, generating shareholder value, achieving net-zero emissions, powering lives and respecting nature. It is underpinned by their core values and focus on safety.
Digital technologies are improving the experience of their customers and making Shell a more efficient business. This digital transformation of the energy industry is improving efficiency and safety, and it is facilitating the increased use of renewable energy. Shell is actively working on a range of digital technologies, including robotics, 3D printing, cloud computing, advanced analytics, Blockchain, High Performance Computing and AI/ML. From machine learning to computer vision, deep learning to virtual assistants and autonomous vehicles to robotics, Shell has been focused on a range of technologies that have supported advances in AI. Many of the algorithms behind AI and machine-learning systems are not new but limited volumes of accessible data have hampered their application. Easy access to vast data volumes at Shell is making many AI algorithms smarter. Shell is developing and deploying Machine Learning / Deep Learning solutions, developing MLOps practices and applying engineering principles to scale and productionize developed solutions for different business units – Upstream, Downstream, Integrated Gas, Power, and New Energies. In this talk, Prakalp and Naveen will share their experience, learnings and the processes they follow in delivering AI technology solutions in collaboration with external technology providers.
Be sure to stick around for Q&A with Prakalp and Naveen!
Far too often, agencies, organizations, and consulting firms are running data and AI projects without taking the right steps to ensure their success. Agile and iterative approaches have become adopted best practices for application development projects, but why don’t we have something similar when it comes to advanced analytics, big data, and AI projects? Without using best-practices approaches that standardize steps for data preparation, data identification, and data protection, it’s hard to achieve the success you’re expecting, wasting money and resources, leading to failure. This session will walk through the Cognitive Project Management for AI (CPMAI) methodology to provide the foundation needed for project success, especially as you incorporate advanced analytics and AI projects. The CPMAI methodology is the established best practice for AI & ML projects, and increasingly will be demanded by organizations and agencies that plan to develop, procure, and deliver AI and advanced analytics projects. This session will provide real world examples of how CPMAI methodology allows individuals to gain the skills needed for AI project success. Be sure to stick around for Q&A at the end!
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