DeepSingularity: The Road to Technological Singularity

DeepSingularity: The Road to Technological Singularity

Modern technology has unlocked the data fabric of analytics with the potential of machine intelligence in day-to-day life. The field of Computer Science Engineering has contributed significantly to the development of various mathematical models and algorithms since the inception of earlier Konrad Zuse programmable computers. DeepSingularity LCC, is a global leading company in providing consultancy services for SAP, Big Data Analytics, data science, machine learning, deep learning, and IoT solutions. In the recent times, Enterprise Data Warehouse and SAP NetWeaver Business Warehouse have become intertwined for executive decision support systems based on running many data science and IoT platforms. Apparently, SAP holds the Guinness Book of World Records for building the largest big data warehouse with 12.1 PB big data running on SAP HANA (High-performance analytics appliance).The SAP solutions provider company handles projects integrating SAP HANA/SAP S/4 HANA with petabyte-scale data warehouses such as AWS RedShift and Google’s BigQuery data science platforms requiring extensive data ingestion, data processing, data analytics, and programming.

Dr. Ganapathi Pulipaka is the CEO, CDO, Chief Data Scientist, SAP Technical Lead of DeepSingularity LLC, a premier SAP and Artificial Intelligence consulting firm. The SAP and AI solutions provider company also publishes advanced technical books under the publishing house name High performance computing Institute of Technology. “DeepSingularity specializes in building products powered by machine intelligence, deep learning, and machine learning frameworks integrating SAP platforms,” says Dr. GP.

Dr. GP is a PostDoc Research Scholar in Computer Science Engineering in Machine Learning, Big Data Analytics, Robotics, Artificial Intelligence, Doctor of Computer Science from Colorado Technical University, CO with a PhD in Business Administration in Information Systems, Data Analytics, and Enterprise Resource Management, California University, Irvine, CA. His doctoral dissertation SAP HANA and in-memory computing to resolve biggest business conundrums was published as ‘Big Data Appliances for In-Memory Computing’ on Amazon as a book in 2015, and currently trends as #1 bestselling book.

GP’s international career began as a programmer, tuning terabytes of data for off-the-charts dynamic performance of the systems. He has been responsible for successfully implementing around a vast number of SAP projects for Fortune 100 corporations and various other clients for many years. He was responsible in building Global CoE for SAP S/4 HANA, SAP HANA, ERP, CRM, HANA, SRM, SCM, PLM, PPM, and BW NetWeaver products. Further commenting on his immense years of experience from working with ace companies, GP adds, “I consulted and worked on behalf of Big 4 consulting firms in the world; Capgemini US LLC, Deloitte for fortune 100 corporations with heterogeneous business processes in processing extreme scale data for Aerospace, energy, utilities, retail, high-tech, life sciences, healthcare, chemical industry, FDA regulated corporations, banking, media, service, manufacturing, and financial services.”

GP comments, “I love to tweet practical machine learning, deep learning, IoT implementations, SAP applications, extensive research reports, and project implementations with Python, R, Java, ABAP, and TensorFlow that people can benefit from.”  During his PhD and PostDoc, Ganapathi wrote hundreds of research papers with big data tool installations, practical machine learning project implementations for publishing with the Universities as part of his academic research programs. His hunger, passion, and drive for high-performance, next generation technologies such as SAP, Big Data, and Artificial Intelligence has allowed him to bring his thoughts into a book that introduced #1 Bestselling Author (Big Data Appliances for In-Memory Computing) on Amazon to the world. GP is a public Keynote Speaker, and Top #3 Global Machine Learning (Artificial Intelligence/Leader) for 2017 recognized by KCore Analytics. He has also been featured as one of the Top 20 SAP CXO Leaders for 2017 in SAP Special Annual Edition CIO Review Magazine.

He has been ranked as #5 Top Data Science Influencer by Onalytica for 2018.

Under GP’s guidance and expertise, DeepSingularity has become a globally recognized, leading information technology and data science organization that provides SAP consulting for large-scale, medium, and small businesses with methodologies and accelerators to meet the growing demand of end-to-end integration of SAP Enterprise systems on SAP NetWeaver and SAP S/4 HANA Platforms. The company has extensive experience in technical development aspects, functional areas, SAP upgrades, master data management, governance, data migration through SAP S/4 HANA data migration cockpit, data architecture, project, and communications management.

Dr. GP has won several awards in his career. To name a few, Development Project manager (won Levi Strauss and Co Jeff Gordon Award in 2011) for On-time and On-budget delivery of the projects, ABAP developer (won HP Award of Excellence for performance as a Senior SAP Technical Lead Consultant consecutively in 2002 and 2003), successful implementation of North America’s first SAP CRM 7.0 project and SAP Pinnacle Award.

The largest supercomputers in today’s world can operate petascale volumes of big data with 1015 floating point operations per second. ECP (Exascale Computing Project) aims to perform exascale supercomputing operations with 1018 calculations per second, (Zettascale performs 1021 per second and Yottascale performs 1024 operations per second).  Neural Network architectures in combination of data parallel and pipeline execution of deep learning algorithms have produced tremendous results on FPGAs with on-chip buffers with a series of breakthroughs in the recent times.  The innovations of JupyterLab and high-performance computing have revolutionized the field of life science, genomics, and bioinformatics. The JupyterLab can bring the acceleration to the computation in near real-time, allowing the enterprises to see the results as soon as they type on JupyterLab with Python. Running petabyte-scale big data on data science platform can spawn the data chunks onto multiple nodes of HPC.  However, to produce most optimized results, different phases of machine learning and deep learning petabyte-scale data science projects require training, evaluation of machine learning model performance and testing, hyperparameter tuning, and deployment into the production in a high-performance computing environment to build the real-time predictive applications for production. The future of high-performance computing hinges on JupyterLab by performing extreme parallelization computing operations with ipyparallel plugin that can distribute Python code and complex mathematical calculations onto the clusters of multiple engines in HPC environments.

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