There has been tumultuous excitement in the Computer Science field surrounding the potential of applying machine learning algorithms to the commercial problems at enterprise scale over the last few decades. Solving complex conundrums and HPC at web scale will need a new enterprise-grade operating system and hardware data flow computing with ASIC or FPGA chips to minimize the data movements and maximize the shorter communication paths to reboot the fourth industrial revolution and execute more parallel instructions on a single semiconductor chip die.
The data lifecycle management of IoT requires unlimited number of threads to spawn on an operating system at the same time simultaneously with guaranteed QoS. All the IoT operating systems such as Android Things, Arm Mbed OS, Embedded Apple iOS and macOS, Google Brillo, Green Hills Integrity, Nucleus RTOS, RIOT OS, RTOS, Windows, WindRiver VxWorks, or Linux targeting Raspberry Pi or BeagleBoards, Intel Edison IoT Boards, and Arduino platforms can provide the performance based on the number of cores in the machine without unlimited threads.
However, regardless of an exponential increase in the hardware resources to deliver the multitasking capabilities and memory management, corporations have hit the wall on Moore’s Law plagued with communication delays requiring precision programming in C and C++ through Open MPI and heavy parallelprogramming which does not seem to be the norm for developing regular applications. Therefore an operating system with unlimited threads with a limited number of cores with new architectural design can scale parallel programming through machine learning and deep learning algorithms built into the operating system natively can take the industrial evolution to the next level.
A Giant Arm of Technology
SAP has been the third largest software company in the world from 1970 with 335,000 customers in 190 countries with an estimated revenue of $30b in 2018. Deep Singularity LLC rated as the top 20 SAP solution providers for the last year ranked alongside Lenovo a $43.3b company in 2017 by CIO Review SAP Special Annual Edition. The company was rated as the top 10 SAP and AI solution providers for 2018 by Mirror Review Special Annual Edition.
According to its CEO, Dr. Ganapathi Pulipaka, “To me, being part of the most innovative company comes with the ability to program advanced machine learning algorithms”. The company engages in advanced algorithms through Python, ABAP, and consume SAP technology stack for SAP IoT SIM management, SAP Cloud platform for IoT integration, SAP S/4 HANA, SAP HANA, SAP Leonardo IoT, detecting patterns from structured and unstructured big data, applying deep learning algorithms in Python, TensorFlow, R, ABAP, connectivity to Google’s BigQuery and Amazon Redshift.
The human multitasking ability is limited to dual-task division labor based on the research results from the neuroscience researchers at Duke University in Durham, North Carolina. In today’s world, the operating systems cannot create countless threads to perform the operations between the software and the hardware beyond the capability of the machine RAM.
Dr. Ganapathi Pulipaka is the CEO, CDO, Chief Data Scientist, and SAP Technical Lead of DeepSingularity LLC, a premier SAP and artificial intelligence consulting firm. He is also a PostDoc Research Scholar in Computer Science Engineering in Big Data Analytics, Machine Learning, Robotics, IoT, Artificial Intelligence as part of Doctor of Computer Science program from Colorado Technical University, CO with another PhD in Information Systems, Data Analytics, and Enterprise Resource Management, California University, Irvine, CA.
GP – A childhood prodigy
Dr. Ganapathi Pulipaka, the CEO –
“When I was 16 years old, I started writing programs in C, C++, VC++, and Java. At this stage, as a freelancer, I implemented a vast number of commercial projects for clients (significant customers include Air Force Academy and State Electricity Board in C and C++). Programming competitions take an entirely different approach from building enterprise grade software systems. Mathematics and programming correlate, while the former aims at solving an abstract problem, the latter requires a particular skill set and programming framework to develop design patterns in object-oriented programming and address the specific complex commercial conundrum.
I see programming patterns everywhere in day to day life. I just see it. My international career began in SAP working at energy company processing terabytes of data through ABAP programming. In the world of SAP scaling and deploying terabyte systems was an everyday phenomenon couple of decades ago, while for corporations without SAP enterprise systems, data at this scale is just unheard. My background is in Computer Science with a professional skillset has prepared me to always look for patterns in data and performing extractions to provide new meanings and insights through algorithms. I have been a technology leader in solution architecture and application development of SAP systems and application development management, basis, and consulting delivery services offering expertise in delivery execution and executive interaction.
During Ph.D. and PostDoc, I wrote around 400 research papers with a vast number of big data tool installations, SQL, NoSQL, practical machine learning project implementations, data analytics implementations, and statistics for publishing with the Universities as part of academic research programs. Many projects I implemented for Fortune 100 corporations run my code in their production systems successfully as of today on their terabyte enterprise systems in the industry space of Aerospace, manufacturing, IS-AFS (Apparel footwear solutions), IS-MEDIA (Media and Entertainment), ISUCCS (Customer care services), IS-AUTOMOTIVE (Automotive), IS-Utilities, retail, high-tech, life sciences, healthcare, chemical industry, banking, and service management.”
His book Big data Appliances for in-memory computing published in 2015 that trends currently as #1 on Amazon provides enterprise strategies for C-Suite executives surrounding big data, machine learning, and analytics at scale before they choose a tool or framework for implementation. The book also provides around 60 practical projects implemented by Fortune 100 corporations including Lockheed Martin.