- Innovation
- Project Management
- Product Management
- Functional Design
- BPMN
- UML
- Concept Mapping 
- Knowledge Modeling 
- Knowledge Graph Visualization 
- Artificial Intelligence Solutions 
- Machine Learning Solutions
- Business Intelligence
- Business Architecture
- Strategy 
- Consultancy
- Open Data
- Hydro-Quebec
- Electro-technical data
- Generative Artificial Intelligence
- Natural Language Processing
- Natural Language Understanding
- Named Entity Recognition 
- Text Classification 
- Information Extraction 
- Data Science
- Knowledge Graphs 
- Knowledge Engineering
- Ontology Engineering 
- Data driven project 
- Data strategy
- Data governance
- Data Architecture 
- Data literacy

Senior Consultant

Upwork

Data Professional

Consultancy, Leadership, Innovation, Research & Development in Europe and North America

*The LLMs Skills Dictionary
*The Science Skills Dictionary
*The Technical Writing Skills Dictionary
*The Scientific Research & Writing Skills Dictionary
*The Innovator Skills Dictionary
*The FinTech Skills Dictionary 
*The Project Management Skills Dictionary
*The Solutions Architect Skills Dictionary
*The Knowledge Graphs Skills Dictionary
*The Network Science Skills Dictionary
*The AI Skills Dictionary
*The Data Scientist Skills Dictionary
*The Data Engineer Skills Dictionary
*The Machine Learning Engineer Skills Dictionary
*The BI Skills Dictionary
*The NLP, NLU, & NLG Skills Dictionary
*The Cognitive CS Skills Dictionary
*The CDO Skills Dictionary
*The Data Professional Skills Dictionary
*The R & Python Data Skills Dictionary
*The Cognitive Science Skills Dictionary
*The Philosophy Skills Dictionary
*The Linguistics Skills Dictionary
*The Logic, Reasoning and Critical Thinking Skills Dictionary
*The Mathematics Skills Dictionary
*The Education Skills Dictionary
*The Communication Skills Dictionary
*The Power BI Skills Dictionary

Author

*The Network Science Handbook for Canada Government
*The Network Science Handbook for US Government
*The Network Science Handbook for European Commission

*The Statistics & Probability Handbook for Canada Government
*The Statistics & Probability Handbook for US Government
*The Statistics & Probability Handbook for European Commission

*The Data Science Handbook for Canada Government
*The Data Science Handbook for US Government
*The Data Science Handbook for European Commission

*The Data Engineering Handbook for Canada Government
*The Data Engineering Handbook for US Government
*The Data Engineering Handbook for European Commission

*The Machine Learning Handbook for Canada Government
*The Machine Learning Handbook for US Government
*The Machine Learning Handbook for European Commission

*The SQL Handbook for Canada Government
*The SQL Handbook for US Government
*The SQL Handbook for European Commission

*The Power BI Handbook for Canada Government 
*The Power BI Handbook for US Government
*The Power BI Handbook for European Commission 

*The Project Management Handbook for Canada Government 
*The Project Management Handbook for US Government 
*The Project Management Handbook for European Commission 

*The AI Handbook for Canada Government,
*The AI Handbook for US Government
*The AI Handbook for European Commission

*The NLP, NLU & NLG Handbook for Canada Government
*The NLP, NLU & NLG Handbook for US Government
*The NLP, NLU & NLG Handbook for European Commission

*The Cognitive CS Handbook for Canada Government
*The Cognitive CS Handbook for US Government
*The Cognitive CS Handbook for European Commission

Author for the public service

EUROCAINUS - Foster Collaborations between Europe, Canada, and United States with Artificial Intelligence

Founder

AIAO - Atlantic Intelligence Amplification Organization
*Target: "How clever are we in the Atlantic Ocean? - Well, ... !"
*Atlantic Ocean Stakeholders: coastal countries, fishing and aquaculture industries, maritime transportation and shipping companies, environmental and conservation organizations, tourism and recreation, research and academic institutions, international organizations, indigenous communities.
*Deliverables: reports, data products, recommendations.
*Business Cases Examples:
- How apply AI on oceanographic data to better protect Atlantic ecosystems?
- How apply AI to optimize economic transactions between stakeholders of the Atlantic Ocean?
- How apply AI to promote and protect cultural inheritance from the Atlantic Ocean stakeholders?
- How apply AI to historical data from the Atlantic Ocean to optimize management strategies?
- How relate AI methods and data to globalize Augmented Intelligence strategies to the benefit of all stakeholders of the Atlantic Ocean?
*Missions:
+ Develop Augmented Intelligence / Amplify Intelligence for Atlantic Ocean stakeholders.
+ Apply AI to enhance collective intelligence in Atlantic Ocean.
+ Relate intelligences for the Atlantic Ocean.
+ Enhance relationships between stakeholders of the Atlantic Ocean.
+ Model and measure relationships between entities related to the Atlantic Ocean.
*Tasks:
1. Identify and categorize the stakeholders of the Atlantic Ocean according to their industries, activities, goals, etc.
2. Build corpus and knowledge bases about the Atlantic Ocean from open data and official organizations.
3. Apply sentiment analysis to discover and model problems and business opportunities.
4. Apply network analysis on knowledge graphs and build ontologies related the Atlantic Ocean.
5. Report metrics and statistical results from exploratory analysis, (un)supervised machine learning, and generate PM plans.
*Fields: Artificial Intelligence ; Augmented Intelligence ; Atlantic Ocean ; Atlantic Intelligence.

ANLPL - Applied Natural Language Processing Labs
*Target: "How valuable are the language technologies in your company? - Well, ... !"
*Stakeholders: researchers and scientists, engineers and developers, data scientists, product managers, industry partners and clients, academic institutions, funding agencies, end users.

*Business Use Cases Examples:
- Which are the sentiment expressed in your text data like your customer reviews or your socia media posts? (sentiment analysis)
- How do you organize your text data into categories? (text classification and categorization, content tagging, spam filtering, information retrieval)
- What kind of information can we find into your text data? (Named Entity Recognition, Information Extraction, Entity Linking, Knowledge Graph Extraction, Ontology Learning from Texts)
- How do your stakeholders interact automatically with your company? (chatbots, virtual assistants, Conversational Artificial Intelligence)
- How do you report efficiently text data to stakeholders? (text generation and summarization)

*Missions:
+ Promote applications of NLP in industry.
+ Design business cases of NLP in industry.
+ Plan NLP projects for industry.
+ Collect and report best practices in applications of NLP in industry.
+ Present state of the art of NLP in industry.

*Tasks:
1. Score, model, rank and categorize sentences with sentiment analyzes algorithms.
2. Categorize and classify texts according to semantic and stylistic features.
3. Analyze and relate texts according to Wikimedia ressources: Wiktionary, Wikipedia, Wikidata, etc.
4. Design augmented intelligence business cases with large languages models.
5. Report informations from texts with data visualization and dashboards.

PACLL - Pure, Applied & Computational Logic Labs
*Target: "How reliable and trustworthy are your reasonings and the reasonings of your systems? - Well, ... !"
*Stakeholders: researchers and scholars, logicians and philosophers, academics and educators, students and graduate researchers, funding agencies, industry and technology partners, standards organizations, professional associations and societies, general public and society.

*Business Use Cases Examples:
- How reliable are your complex systems (formal verification)?
- How logic-based are your decision support systems?
- How represented is your expertises in systems?
- How automated are the reasonings of your systems?
- How integrated and interoperable are your data?

*Missions:
+ Promote logic wherever it is possible.
+ Extract and model reasonings.
+ Unify logic paradigms.
+ Support symbolic artificial intelligence.
+ Compare and relate logic studies.

*Tasks:
1. Build a knowledge base about logic.
2. Formalize research about reliability and trustworthiness.
3. Automate logic-based reasonings with knowledge graphs and semantic triples.
4. Report advantages and limits about logic-based systems.
5. Relate logic knowledges with large language models.

Phinnovations - AI & Philosophy for Innovations
*Target: "How deep is your innovation idea? - Well... !"
*Stakeholders: entrepreneurs and innovators, business and corporations, investors and venture capitalists, government and policy makers, research institutions and academia, customers and consumers, industry associations and trade organizations, non-governmental organizations, intellectual property and legal experts, society and communities.

*Deliverables: reports ; data products ; data analysis ; dashboards.

*Business Cases Examples:
- How can AI be leveraged to identify innovative business opportunities?
- How can AI boost creativity and ideation leading to innovations?
- How can AI be used to organize customer-centric innovation?
- How can AI be used to optimize planning and operations of innovation?
- How can AI support risk analysis and management in innovation processes?

*Missions:
+ Analyze and reveal philosophical deep roots and foundations of innovations.
+ Apply artificial intelligence and machine learning algorithms to measure and predict innovation.
+ Advice stakeholders of innovations according to their business outcomes.
+ Document innovations with scientific methods and algorithms.
+ Build knowledge bases of innovations with Natural Language Processing, Natural Understanding, Conversational Artificial Intelligence.

*Tasks:
1. Structure history of innovations.
2. Categorize and model innovations as a dataset of features for supervised machine learning tasks.
3. Relate innovations and industries.
4. Predict impacts of innovations as a supervised machine learning task.
5. Generate diffusion plans of innovation according to constraints.

*Fields: Innovation ; Philosophy ; Artificial Intelligence ; History ; Strategic Management

MLI4AI Labs - Machine Learning Innovation for Augmented Intelligence
*Target: "How innovative is your use of machine learning? - Well... !"
*Clients: machine learning researchers and developers, data scientists and analysts, technology companies and AI Solution providers, business leaders and decision makers, end users and customers, regulatory bodies and ethical commitees, academia and research institutions, public and society.

*Deliverables: reports ; data products ; datasets for machine learning ; augmented intelligence architecture.

*Missions:
- Recommend the best practices in Machine Learning based Innovation for Augmented Intelligence.
- Relate Machine Learning, Artificial Intelligence and Augmented Intelligence for Augmented Cognition Innovation.

*Fields: Augmented Intelligence ; Innovation ; Machine Learning ; Artificial Intelligence ; Applied Research

Aidomena - Expert Systems for Artificial Intelligence & Data Driven Innovation by Data Professionals
*Target: "How data driven is your expertise? - Well, ... !"
*Stakeholders: chief data officers, data professionals, IT departments and administrators, business leaders and executives, end users and stakeholders departments, customers and clients, regulatory authorities and compliance experts, AI solution providers.

*Deliverables: experts systems ; Jupyter notebooks ; data products ; knowledge bases ; data pipelines ; data architectures ; solutions architectures.

*Missions:
- Collect data and build knowledge bases for Data Driven Innovation.
- Design Expert Systems for Artificial Intelligence & Data Driven Innovation by Chief Data Officers & Data Professionals.
- Model skills and knowledge for chief data officers and data professionals.
- Design data models for experts systems.
- Analyze knowledge graphs to foster innovations.

*Fields: Expert systems ; Data Governance ; Data Science ; Data Strategy ; Data Literacy ; Machine Learning ; Machine Learning Engineering ; Data Architecture ; Ontology Learning From Texts ; Supervised Machine Learning ; Unsupervised Machine Learning ; Reinforcement Learning ; Computer Vision ; Artificial Intelligence ; Data Production ; Data Management ; Data-Driven Innovation ; Data Engineering ; Cognitive Computing ; Cognitive Informatics ; Natural Language Processing ; Natural Language Understanding ; Natural Language Generation ; Automated Reasoning ; Knowledge Engineering ; Data Mining ; Educational Data Mining ; Machine Learning Design ; Named Entity Recognition ; Machine Translation ; Information Extraction ; Computational Linguistics ; Ontology Engineering ; Cloud Computing ; Recommender Systems ; Computer Science ; Machine Learning Development ; Network Analysis ; Graph-Powered Machine Learning ; Graph-Powered Analytics ; Semantic Web ; MLOps

Relatin - Relationships driven AI
*Target: "How relatable is your AI? - Well, ... !"
*Stakeholders: data providers, AI researchers & developers, technology companies and AI solution providers, business organizations and end users, regulatory bodies and ethical commitees, public and society, academia and research institutions.

*Deliverables: data products ; knowledge models ; semantic triples.

*Business Cases Examples:
- How can AI enhance customer relationships management?
- How can AI optimize supply chain management?
- How relate entities from a text and a knowledge base?
- How extract and measure relationships between entities?
- How can AI enchance ethical relationships?

*Missions:
- Qualify relationships according to relationships taxonomies.
- Promote relationships driven AI.
- Classify relationships with machine learning and AI.
- Measure relationships between entities.
- Design systems of relationships.

*Fields: data relationships ; feature relationships ; network relationships ; user relationships ; ethical and social relationships.

Polyckqalai - Quality Control & Optimization of Innovation, R&D With AI
*Target: "How much quality is your AI? - Well, ... !"
*Stakeholders: research and development teams, data scientists and AI experts, quality control professionals, R&D managers and executives, operations and production teams, customers and end users, regulatory bodies and compliance officers, suppliers and partners, executives and decision makers, industry associations and standards organizations.

*Missions:
Quality Control & Optimization of Innovation, R&D With Artificial Intelligence in North America & Europe

*Fields: Data Quality Analysis ; Quality Datasets Extraction & Definition ; AI & ML based Quality Control of Legal Writing, Technical Writing, Scientific Writing, Academic Writing (Technical reports, Scientific Papers) ; Supervised Machine Learning ; Unsupervised Machine Learning ; Outliers detection ; MLOps

Ksalami
*Target: "How well is your biomechanics? - Well, ... !"
*Stakeholders: biomechanics researchers, data scientists and AI experts, athletes and individuals, healthcare professionals and physical therapists, equipment manufacturers, coaches and trainers, regulatory bodies and standards organizations, academic institutions and funding agencies, professional associations, general public and society.

*Missions:
- Understanding and improving Biomechanics with Machines & Artificial Intelligence

*Fields: Computational Biomechanics ; Robotics ; Artificial Intelligence ; Machine Learning ; Computer Vision ; Biomechanics ; Research & Development of Global Stretching Environments (GSE) ; Artificial Intelligence Based Biomechanical Recommendation.

Jazzai - AI for Jazz
*Target: "How jazzy is your world? - Well, ... !"
*Stakeholders: musicians, bands and ensembles, festivals and venues, record labels and producers, educators, critics and journalists, fans and audiences, historians and archivists, funding organizations and sponsors, government and cultural institutions.

*Deliverables: reports ; data products ; data visualization ; dashboards ; datasets for machine learning.

*Business Cases Examples:
- How can AI be used to analyze jazz archives?
- How can AI support jazz improvisation?
- How can AI be used to support marketing strategies of jazz?
- How can AI can be used to extend jazz limits?
- How can AI and Computer Vision enhance jazz performances?

*Missions:
- Promote Jazz with Artificial Intelligence.
- Model skills and knowledge about jazz.
- Analyze trends in jazz.
- Promote Jazz events using artificial intelligence.
- Share knowledge about jazz.

*Fields: Jazz ; Artificial Intelligence ; Musicology ; History ; Cultural studies

MLOpsBA - MLOps of Business Administration · Self-employed
*Target: "How optimized is your business administration thanks to machine learning? - Well, ... !"
*Stakeholders: data scientists and machine learning engineers, IT operations team, business analysts and decision makers, data engineers, DevOps engineers, compliance and security professionals, end users and customers, executive management and C-Suite, external partners and vendors, regulators and industry standards bodies.

*Deliverables: reports ; documentation ; data architectures ; solutions architectures ; data products ; datasets for machine learning ; machine learning models ; microservices.

*Business Cases Examples:
- How can MLOps be integrated in business operations?
- How to optimize synergies between MLOps and business administration processes?
- How can MLOps can enhance efficiency and effectiveness of business administration?
- How to adress challenges of MLOps according to varying business environments? 
- How can machine learning-driven decision-making processes enhance business administration?

*Missions:
- Design MLOps pipelines to optimize business administration.
- Compare MLOps technologies.
- Support resolution of technical MLOps challenges.
- Measure evolution of MLOps trends.
- Define knowledge-based MLOps processes.

*Fields: MLOps ; Business Administration ; Business Intelligence ; Artificial Intelligence ; Machine Learning

Customai - AI for Customer Analytics
*Target: "How intelligent is your customer analytics? - Well, ... !"
*Clients: business executives and managers, data scientists and analysts, marketing professionals, customer service and support teams, IT and technical teams, customers, legal and compliance teams, sales teams, finance and operations teams, regulatory and industry bodies.

*Deliverables: data and machine learning solutions ; data products ; data visualisations ; dashboards.

*Business Cases Examples:
- How can AI be used to enhance customer segmentation and personalized marketing strategies?
- How can AI be used to improve customer satisfaction?
- How can AI leverage customer feedbacks and customer preferences to increase sales?
- How can AI be ethical in customer analytics?
- How can AI be used to improve customer churn prediction and retention?

*Missions:
- Promote AI in Customer Analytics.
- Design Customer Analytics models and architecture.
- Articulate Artificial Intelligence and Competitive Intelligence with Machine Learning.
- Plan business intelligence strategies with AI and MLOps.
- Design Visualizations and Marketing Optimization Dashboards.

*Fields: Artificial Intelligence ; Customer Analytics ; Sentiment Analysis ; Ontology Engineering ; Predictive Analytics.

Emergethics - Emerging Ethics from New Technologies
#Clients: business leaders and executives, employees, customers and clients, investors and shareholders, regulatory and government bodies, industry associations and professional bodies, suppliers and business partners, local communities and society, non-governmental organizations, media and public opinion.

#Business Cases Examples:
- How trace, measure and consider ethics evolutions for the business administration?
- How emerging ethics should evolve with emerging technologies?
- What are the ethical implications and challenges of AI-driven globalization?
- How do business should address ethics with scalability according to categories, markets and levels?
- How to apply business operations with full ethical considerations?

#Missions:
- Analyze emerging ethics.
- Model skills and knowledge related to ethics.
- Advice industries in ethics.
- Design and develop frameworks for ethics analysis.
- Measure ethics evolutions.

#Fields: Sentiment Analysis ; Ethics ; Web Science ; Social studies ; Metrology

Epistanai - Epistemology for Business Administration
*Stakeholders: business leaders and decision makers, researchers and academics, consultants and experts, regulatory and standardization bodies, business educators and trainers.

Business Cases Examples:
- How can epistemological principles be applied with AI to improve business administration?
- How can epistemological frameworks help to mitigate risks and biases according to ethical concerns about AI?
- What epistemological approaches can be employed to assess AI applications in business administration?
- How can epistemology inform decision makers in knowledge management for business administration?
- How can the integration of epistemological principles and frameworks in operations, DevOps, MLOps of AI applications can improve business administration?

Missions:
- Promote the study and application of epistemology in business administration.
- Design and deploy epistemology programs in private and public companies;
- Advice stakeholders for Key Performance Indicators (KPI) from Epistemological strategies;
- Apply artificial intelligence, machine learning and data mining methods and algorithms to recommend epistemological programs in business administration.
- Deploy epistemic logic based knowledge systems.

Skills : Epistemology ; Artificial Intelligence ; Business Administration ; Business Analytics ; Text Analytics.

CCA4AI - Cloud Computing Architectures for Augmented Intelligence
#Clients: cloud service providers, augmented intelligence developers, application developers, data scientists and researchers, business and organizations, end users, regulatory bodies and compliance experts, IT departments and administrators, cloud security providers.

#Deliverables: data architectures ; solutions architectures ; data products ; knowledge bases.

#Missions:
- Design solution architectures of cloud computing solutions for augmented intelligence.
- Design data architectures of cloud computing solutions for augmented intelligence.
- Compare cloud computing solutions and architectures for augmented intelligence.
- Report scientific research about cloud computing solutions and architectures for augmented intelligence.
- Recommend cloud computing providers according to augmented intelligence requirements.

#Fields: Solution Architecture ; Data Architecture ; Artificial Intelligence ; Augmented Intelligence.

Faintech - FinTech & banking with AI
#Target: "How do your assets grow thanks to artificial intelligence? - Well, ... !"
#Clients: financial institutions, fintech startups, regulators and government agencies, data scientists and AI experts, customers, IT and technical teams, compliance and risk management teams, investors and venture capitalists, industry associations and standard bodies, academic and research institutions.

#Deliverables: reports ; data products ; datasets for machine learning.

#Business Cases Examples:
- How can AI be used to reduce financial risks for both individuals and institutions?
- How can AI optimize financial services?
- How can AI personalize and recommend financial products to improve customer experience?
- How ethics should be considered when applying AI in FinTech?
- How Large Language Models of Conversational AI will transform FinTech industry?

#Missions:
- Promote AI in FinTech.
- Report trends of FinTech with AI.
- Design AI models for FinTech.
- Measure FinTech evolutions with AI.
- Design and plan FinTech process with AI.

#Fields: FinTech ; Artificial Intelligence ; Natural Language Processing ; Predictive Analytics ; Ontology Engineering

Public Sector Network (Certified B Corp)

Proposals of professional certificates and trainings for Public Servants in: Analytics, Artificial Intelligence Engineering, Artificial Intelligence, Artificial Intelligence in Risk Management, Artificial Intelligence Project Management, Automated Reasoning, Cognitive Computing & Cognitive Informatics, Conversational Artificial Intelligence, Data Analysis, Data Architecture, Data Engineering, Data Governance, Data Science, Data Visualization, Deep learning, DevOps, Information Retrieval, Innovation Management, Knowledge Graphs, Knowledge Modeling, Large Language Models, Machine Learning, MLOps, Natural Language Generation, Natural Language Processing, Natural Language Understanding, Ontology Engineering, Ontology Learning, Open Data, Project Management, Risk Analysis, Software Architecture, Software Engineering, Solution Architecture, Technical Leadership, Text Analytics & Text Mining, Transformation Management, Trustworthy Artificial Intelligence, Writing.

Senior Training Consultant for Public Servants

#Stakeholders: Civil servants of government of Canada & USA.
#Trainings: AI Transforming Project Management in the Public Sector - Digital Government & CX - Harnessing AI’s Power in the Public Sector

Senior Consultant/Trainer for Public Servants in AI Project Management

#Stakeholders: Civil servants of government of Australia, Canada, New Zealand, United Kingdom, USA.
#Target: "Building a better public sector, together : Public Sector Network is a social learning platform that exists to help government around the globe break down silos, collaborate, and work together for better outcomes for citizens." (https://publicsectornetwork.com/) 

#Deliverables: documents ; reports ; Jupyter Notebooks ; data products ; data models ; data pipelines ; data architecture ; solution architecture ; knowledge models ; concepts maps ; datasets for machine learning ; machine learning models.

#Tasks:
- Deliver public sector trainings in data management, data visualization, advanced analytics, AI, ML, NLP for communities of practice of government stakeholders.
- Define a benchmark of specific public sector skills in AI and advanced analytics for the benefit of communities of practice of government stakeholders;
- Identify the most relevant public sector open data for communities of practice of government stakeholders;
- Analyze public sector data from Wikipedia and Wikidata for communities of practice of government stakeholders;
- Analyze and categorize public sector scientific research data for communities of practice of government stakeholders;

#Fields : Data Management ; Analytics ; Data Science ; Artificial Intelligence ; Power BI ; Public Sector

Senior Consultant/Trainer for Public Servants in AI, ML, NLP/NLU/NLG, Data Science/Engineering

#Stakeholders: Civil servants of government of Canada & USA.
#Trainings: Data Management and Analytics - Power BI for Government - Beginner : If you have a keen interest in Excel and are looking to upskill into the world of Power BI, this course is for you!

Senior Consultant/Trainer for Public Servants in Data Visualization with PowerBI (Beginner Level)

#Stakeholders: Civil servants of government of Canada & USA.
Trainer: *Digital Government & CX - Citizen-Centered AI - Driving Responsible Innovation in Public Sector with NLP and ChatGPT Digital Government & CX : Empowering Citizens with Responsible AI

Senior Consultant/Trainer for Public Servants in AI, Conversational AI, Natural Language Generation

#Stakeholders: Civil servants of government of Canada & USA.
#Trainings: Data Management and Analytics - Power BI for Government - Intermediate : Take your Power BI skills to the next level and master data analysis with advanced techniques and insightful visualizations.

Senior Consultant/Trainer for Public Servants in Data Visualization with PowerBI (Intermediate)

SIA Innovations Inc.

# For SIA Innovation - Platinum Business Partner & IBM Build Lab Team : Project MIRE - A multi-industry recommender system to interpret and answer requests for proposals with IBM technologies.
- As client engineering lead, initiate the technical dimensions project with the president of the company who focused on business dimensions.
- Test the feasibility of the project in defining data models and functions in Python.
- Design the first data architectures and solution architectures of prototypes.
- Collaborate with IBM Build Labs to define a full commercial solution.
- Lead the development team in Data Science, Data Engineering, ML, NLP.

#Fields: Data Science ; Data-drive Solution Architecture ; Data Architecture ; Project Management ; Data Team Leadership ; Microservices ; Machine Learning ; Artificial Intelligence ; Text Classification ; IBM Cloud Pak for Data ; IBM Cloud Pak for Business Automation ; IBM Watson Discovery ; IBM Watson Knowledge Studio ; IBM Watson Knowledge Catalog.

Senior Team/Client Engineering Lead

#For Hydro-Québec & IBM : Project MORIS - A search engine querying technical documentations to reduce the workload of Hydro-Quebec technicians with IBM technologies.
- Collect and convert data in RDF/OWL and to SQL databases with API from SAP systems, Open Data (Wikipedia & Wikidata) and expert knowledge bases. 
- Optimize search engine application of IBM Watson Discovery with semantic resources and machine learning models of entity and relations recognition from IBM Watson Knowledge Studio.
- Apply predictive analytics to users data to improve UX and to prioritize semantic resources and ML models.
- Apply statistical data governance principles to prioritize semantic resources optimizing the search engine.
- Design and implement ontology learning pipelines for IBM Watson Knowlede Studio Type systems;

#Fields: Search Engine Optimization ; Data Engineering ; NLP ; AI ; ML ; Ontology Learning from Texts ; Wikipedia API ; Wikidata API ; Electropedia API ; SAP API ; IBM Cloud Pak for Data ; IBM Cloud for Business Automation ; IBM Watson Discovery ; IBM Watson Knowledge Studio ; Project Management ; Data Team Leadership ; Data Architecture ; Solution Architecture ; Recommender Systems ; Text Classification ; Information Retrieval ; Natural Language Understanding ; Named Entity Recognition ; Data Science.

Senior Data/ML/AI Scientist / Engineer / Architect with IBM products

Rocket Science Development

Ranking, Classification and Recommendations for L'Oreal Canada with Amazon Catalog Data / Products

Client : L'Oréal Canada - with Amazon Data

Skills Domains : MLOps ; Machine Learning Engineering ; Machine Learning Development ; Machine Learning Production (Regression, Classification, Recommendation) ; Data Analytics ; Data Science & Machine Learning Solutions in Google Cloud Platform ; Natural Language Processing with spacy ; Deep Learning with transformers (Bert) ; Computer Vision ; Conversational Artificial Intelligence (chatbot)

Senior Machine Learning Engineer / Senior Machine Learning Developer

Bell

$2.4M (Total Investment) - $1.2M (Scale AI investment).  "Extending an order’s life cycle through AI" with Scale AI - Canada's AI Cluster, promoting AI

"The Challenge : Bell Business Markets (BBM) offers a wide variety of communications technologies to Canadian businesses, including broadband connectivity through fibre. To meet market expectations, the project aims to reduce installation time for new fiber optic connections by accelerating several steps in the supply chain with artificial intelligence solutions."

Skills Domains : Data Engineering ; Data Analysis ; Data Science ; Predictive Analytics ; Machine Learning (Regression & Classification) ; Time Series Analysis ; SQL queries ; Entity Linking with Wikidata ;  Feature Engineering

Machine Learning Engineer, Data Scientist, Data Analyst

Analytics Specialist

Fonds de recherche du Québec

$ 46.666 - Quality Control, Optimization, Categorization, Classification, Recommendation, Anomaly Detection in Wikipedia

PhD scholarship

HELB - Haute Ecole Libre de Bruxelles Ilya Prigogine

Computer Science ; Communications ; Thesis Supervisor ; E-Learning Webmaster ; Project Manager ;

Assistant Professor

Boosting Business & Competitive Solutions at THE BEST★Strategist★Artificial Intelligence★Machine Learning★MLOps★Natural Language Processing★Data Science★Cognitive Computing★Knowledge Graphs★Bayesian Networks | Drummer

Meet Ludovic Bocken, a creative and visionary researcher and developer who has been driven by the deep desire to innovate in our knowledge society for the last 20 years. Trained in philosophy, linguistics, didactics, cognitive computing, artificial intelligence, machine learning and natural language processing, his passion for designing solutions that serve the public interest has led him to deep dive into research about Wikipedia for which he received several scholarships. Previously, he taught French communication to managers, professional communication, methodologies, and project management for economics students in public relations and business computer science. He developed data-driven e-learning solutions for higher education and universities, as well as working in research and development for the IT industry and several public companies as a data scientist, a machine learning engineer, and a client engineering lead. Today, Ludovic's focus lies on project management and digital leadership models to design the best solution architectures for cognitive cloud computing. With a life that has taken him across three continents - Africa, Europe, and America - Ludovic's diverse background is reflected in his genetics, with ancestors from all over the world. His thirst for adventure and challenge is evident in his goal of completing his first sprint triathlon before turning 50. But Ludovic's biggest dream is to direct a science fiction movie as good as "The Matrix." With his wealth of experience and knowledge, Ludovic is proud to share his expertise with public sector professionals, making him a valuable asset to anyone looking to shape the future. Ludovic also likes to play drums to better understand and enjoy music. He is currently founding several non-profit initiatives using all of his experience and knowledge.

Taplio

Lewis a.k.a. Ludovic Albert François BEECH a.k.a. BOCKEN, PhDs (c) - INTP-T's Linkedin Analytics

Ludovic Albert François BOCKEN, PhDs (c) - INTP-T

Want to drive more opportunities from LinkedIn?

Taplio

.css-1jgreyo{background-image:linear-gradient(284deg, rgba(39,43,72,1) 0%, rgba(33,47,183,1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Lewis a.k.a. Ludovic Albert François BEECH a.k.a. BOCKEN, PhDs (c) - INTP-T's Linkedin Analytics

Ludovic Albert François BOCKEN, PhDs (c) - INTP-T

Want to drive more opportunities from LinkedIn?

Lewis a.k.a. Ludovic Albert François BEECH a.k.a. BOCKEN, PhDs (c) - INTP-T's Linkedin Analytics