OperAI works with partners in achieving operational efficiency by speeding up and streamlining operational processes combining the new emerging technologies, including quantum technology along with their mathematical theoretical frameworks while involving people from inception to implementation of these processes. OperAI uses a dual approach to help develop and implement innovative solutions. Previous solutions have been considered as breakthroughs spanning automation to discovery. 
 Climate change has created unprecedented challenges requiring to speed up adaptation and mitigation processes across sectors, including agriculture. According to the recent IPCC report climate change impacts and risks are becoming increasingly complex and more difficult to manage. Comprehensive, effective, and innovative responses can harness synergies and reduce trade-offs between adaptation and mitigation to advance sustainable development. OperAI is helping, with its partners, to address some of the complexity and speed up some of the processes to catch up with mitigation efforts.  

Source: IPCC Report (2022)
Urgent climate actions needed to avoid tipping points concluded a recent study. It also points out that limiting warming to well below 2°C and preferably 1.5°C is not safe as 1.5°C, a goal that was made during the 2015 Paris Agreement on climate change by the year 2030. A global temperature above 1.5°C may trigger multiple climate tipping points. 

The location of climate tipping elements in the cryosphere (blue), biosphere (green) and ocean/atmosphere (orange), and global warming levels their tipping points will likely be triggered at. Summary Map by Earth Commission/Globaïa.
​OperAI has been working with strategic partners to help develop innovative methodologies that will help to harness synergies and speed up operational processes to adapt to and mitigate climate change within a sustainability perspective.
OperAI has expertise addressing complexity that helped in speeding up processes in R&D, such the use of fractal geometry with machine learning to automate and speed up description.  Some of this work was published in Fractals and Complexity in Nature events by World Scientific. Another work, which involves speeding up processes to adapt to and mitigate climate change was published by Taylor & Francis Group. 
OperAI publishes also original articles and chapters (as above) as well as books spanning big data, machine learning, IoT, AI to edge analytics.
Book on Edge Analytics by OperAI
“" "Both edge analytics and fog analytics involve pushing intelligence and processing capabilities down closer to where the data originates."
​Automation World (2018)​
Codes and examples of the edge analytics book above are available at the software development platform GitHub. These codes and examples include deep learning and reinforcement learning cases.
Image Explorer App 
Signal App
Text App 
Some of OperAI edge applications in the pipeline 
The need to carry out real-time analytics and inference processes at the edges is driven by the unprecedented rapid proliferation of IoT devices along with the increase of machine-to-machine communication (M2M). 

OperAI supports intelligent operations involving People, Processes, IoT, 5G, Quantum and M2M technologies to drive innovation, which in turn speeds up and streamlines operational processes within a sustainability perspective. OperAI uses a dual approach for a better outcome. 
  • OperAI helps to carry out real-time analytics and streamline operational activities by leveraging Big Data, Machine Learning and Artificial Intelligence, Quantum Computing using different platforms. 
  • ​OperAI focuses on operational data in the cloud and the edges to process and analyse data streams on real-time, whether as a signal, image or textual data.     
  • OperAI works with partners in industry, research and academia in the realm of industry 4.0 for more accurate, efficient and environmentally friendly operational processes.
  • OperAI has expertise on preparing and developing Big Data/ML/AI applications and tools that helped in speeding up discovery and delivery processes, with results considered as breakthroughs. These tools are used in research and development (R&D), including international research organisations and development agencies as well as industries. 
Industry 4.0 refers to the current fourth industrial revolution where new technologies are all merging together, linking the real world to the digital world, involving big data, automation, ML and AI. It is named as the fourth generation in subsequent to the third industrial revolution on electronics and information technology to automate production. Industry 4.0 includes the Industrial Internet of things (IIoT), edge-to-cloud analytics, including fog analytics and cognitive analytics. The IIoT devices are poised, today, to increase production efficiency by optimizing and streamlining operations in the industrial equipment and machinery space. While the second industrial revolution refers to the use of electric power in the production system, the first refers to the mechanization of production using water and steam power. 
OperAI co-develops IoT tools and AI applications involving cognitive analytics with people in the loop.
80% 
of employees are keen to learn artificial intelligence skills and
are comfortable with AI in the workplace
​(TechRepublic 2019).
​
80%
of Business and Tech Leaders mentioned that
AI creates jobs and increases productivity,
(BusinessWire, 2015).

OperAI publishes books and articles in support of AI applications in the cloud and the edges. The books contain information on different techniques spanning  BIG Data, ML to AI techniques as well as market trends. ​

Machine Learning at Work

CA$9.95

The book introduces ML and its potential along with some ML applications using Spark and R platforms combined. While Spark has the possibility to scale and speed up analytics, it harness R language‘s machine learning capabilities beyond what is available on Spark or any other Big Data system. R and Spark can share codes and different types of data and carry out powerful large scale machine learning capabilities. Machine learning with Spark and R language combined can not only speed up but also light up Big Data Discovery.


The book contains 10 chapters, the first chapter highlights ML quests, chapter 2 provides a detailed historical perspective, chapter 3 shows how ML works by introducing conceptual frameworks of ML, chapter 4 lists some of the metrics used to assess the performance of ML types. Chapter 5, 6 and 7 focus on different types of ML including supervised, unsupervised and reinforced learning. Chapter 8 and chapter 9 introduce the ML implementation platforms of R and Spark with their different libraries including Spark MLlib. Chapter 10 provides different walk-through ML examples using both R and Spark ML techniques.
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Addressing Big Data Challenges

CA$4.95

Big Data has shown its tremendous potential to transform industries, such as healthcare and insurance industries, and to empower artificial intelligence and machine learning at an unequivocal scale. However, there are concerns that Big Data may lose much of its usefulness, potentially generating new unintended consequences if epistemological (explanatory frameworks of knowledge generated) challenges are not addressed. Big Data has grown tremendously rapidly leading to data to outpace concepts. Conceptual investigations and mathematical theoretical frameworks are to theory formulation what methodology is to Big Data gathering and Big Data analytics. A lack of conceptual frameworks to address epistemological challenges of Big Data may slow progress in innovations and delay the development of Big Data's prospective applications according to recent reports and publications on Big Data. The book recalls the vigilance of Gaston Bachelard regarding the precision in making decisions based on the data alone.
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Working with Big Data

CA$9.95

Big Data's rapid growth has definitely created radical shifts with new opportunities, leading organisations and companies to shift their activities towards more data-driven decisions with the help of machine learning (ML) techniques and artificial intelligence (AI). A shift from stand-alone traditional desktop computing to embrace a more comprehensive strategy such as Mesh App and Service Architecture (MASA) strategy banking on Big Data to allow more dynamic connection of people, processes, things and services that are supporting today’s increasingly intelligent (AI) digital ecosystems, according to Gartner (2017).


The book refers to the different techniques and tools used to address the ambiguity and uncertainty as well as scaling challenges that helped to transform, analyse, and reveal hidden patterns. The identified patterns were used, in turn, successfully in the discovery process. The tools are presented, in this book, within the perspective that these tools will lend themselves to also scale as the technologies and methodologies evolve.
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AI at the Edges

CA$14.95

This book is about a new paradigm shift in the current analytics landscape where the edges and the fog are becoming the new interface for analytics insights and for hosting new artificial intelligence applications. According to Automation World: “Both edge analytics and fog analytics involve pushing intelligence and processing capabilities down closer to where the data originates.”
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Readers' feedback on the books

Excellent book! (Machine Learning)
​
"Excellent book, didactic, concise with a global view of the ML universe. I'm using it, with others, to prepare my introductory ML course. I also strongly recommend reading for executives who wish to have a comprehensive, well-written, concise overview of ML."

​Fantastic book, Big Data of Things!
​
"Amazing book, goes from overview to details for most aspects of Big Data if not all, if you are a beginner to Data Sci, AI, Machine Learning, this book will help. Some content are mathematical, programming or scripting experience could enlighten you better. Overall time well spent, highly recommend it."
Services & Products
Reviews & Comments
Team Members

​Operational AI to speed up processes of
production & protection at the edges,
​with people in the loop.
​
OperAI co-develops also tools within the sustainability perspective 
​by including e-waste in the process of making new products with its 
recycling partners.
IoT deployment can help to curb global warming by cutting carbon emissions by 15%.
​OperAI has been selected among the first projects of the first NextAI Canada Montréal program, in 2018 as an edge AI-based analytics platform. It has been then launched officially with the support of the Government of Québec early 2019.

OperAI has also joined the Evolution of Networked Services through a Corridor in Québec and Ontario for Research and Innovation program, known as ENCQOR, early 2020 and Réseau Montréal  recently.
OperAI has also partners working on the development of IoTs in Québec. 

OperAI  is expanding in its partnership to include new partners to speed operational process and help to adapt to and mitigate climate change. 
​OperAI will be looking for members to join OperAI team. Looking for new members with math and/or engineering background and experience in programming and prototyping. ​
OperAI will be looking for developers to help with designing and developing applications in the cloud and the edges.  The tasks include:         
         - Writing and compiling codes in R, Python, Spark, C++ and Q#.
         - Debugging codes by testing and fixing errors, if any.
         - Simulations and testing programs in real situations before their launch.
         - Deployment and launch of applications, including mobile apps.


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