Healthcare and life science organizations are moving towards digital transformation to decrease the cost of care, improve collaboration, make data-driven clinical and operational decisions, and enable faster development of new therapeutics and treatment paths. Identifying the right cloud technology to reach these goals can be challenging, and many organizations lack the internal resourcing and expertise to assess, build, and deploy their own solutions. As a highly regulated and complex industry, various global compliance requirements can further complicate healthcare and life science organizations' moves to implement digital initiatives. To help customers accelerate their transformation, we are introducing AWS for Health, an offering of curated AWS services and AWS Partner Network solutions used by thousands of healthcare and life sciences customers globally. AWS for Health provides proven and easily accessible capabilities that help organizations increase the pace of innovation, unlock the potential of health data, and develop more personalized approaches to therapeutic development and care.

FORMULA 1 (F1) turns 70 years old in 2020 and is one of the few sports that combines real-time skill with engineering and technical prowess. Technology has always played a central role in F1; where the evolution of the rules and tools is built into the DNA of F1. This keeps fans engaged and drivers and teams always pushing as races are won and lost in tenths of a second. With pit stops from well over a minute to under 2 seconds, 5g cornering and braking, speeds up to 375 KPH, and racing in 22 countries, no sport has been as dynamic in its evolution and embrace of new technology. FORMULA 1 seeks to innovate continuously and some of the latest innovations are going to enhance the experiences of its growing base of over a half a billion fans, and an improved understanding of what happens on and off the track through the power of data and analytics, by bringing the split-second decisions made by drivers and teams to the viewers. With 300 sensors on each race car generating 1.1M data points per second transmitted from the car to the pit, the fan experience has downshifted from reactive to real time, which accelerates the action on the track.

We have been discovering new and better ways of doing things since the beginning of civilization. Today, modern companies face an extremely competitive environment where innovation is absolutely necessary to avoid falling behind or becoming irrelevant. The old phrase, "If it ain't broke, don't fix it" no longer applies. Enterprises must optimize existing technologies and explore new solutions to survive. This is especially important in how companies contract with customers and procure services with suppliers.

Artificial Intelligence (AI) has massive potential to drive disruptive innovations affecting most enterprises on the planet. However, most enterprises are struggling to succeed with AI . Simply put, AI and Data are siloed in different systems and different organizations. Enterprise data is siloed across hundreds of systems such as data warehouses, data lakes, databases and file systems that are not AI-enabled. Popular machine learning frameworks such as TensorFlow, PyTorch, and SciKit-Learn don't do data processing.

Does this sound familiar: experiencing performance and productivity issues with your data science and data engineering efforts on Apache Spark? See how Viacom, HP, and Hotels.com Faster performance, scaled data processes, simplified infrastructure, streamlined workflows, and greater collaboration. Ready to discover how unifying your data science, engineering, and analytics can benefit your company?

As Kevin Rands, (a contributor at CIO.com) has stated in his blog "Data is the lifeline of any business", and truly said, it remains the heart of businesses for life sciences and pharmaceutical industries. A lot still relies upon data while conducting research which is mostly done through traditional methods. As many top notch business persons and influencers of the industry quote the importance of data and real-time information, use of such tools to provide solutions to these problems is still missing in the industry. A recent wave of innovation in analyzing the data and patterns is gaining prominence with the industry veterans, to come up with better solutions that are disrupting the data and analytics services. Slacking off the old and traditional methods to artificial intelligence and machine learning techniques would save up lots of research time amongst the life science professionals.

Facebook's long-term roadmap is focused on building foundational technologies in three areas: connectivity, artificial intelligence and virtual reality. We believe that major research and engineering breakthroughs in each of these areas will help us make more progress toward opening the world to everyone over the next decade. Our work in AI is helping us move all these projects forward. We're conducting industry-leading research to help drive advancements in AI disciplines like computer vision, language understanding and machine learning. We then use this research to build infrastructure that anyone at Facebook can use to build new products and services.