Olivier Fontana

Vice President Marketing

I am a marketing, product and business leader with a broad technology and business experience in organizations and groups ranging from tens to thousands of people. I have led or supported the growth of multiple products and services in the AI, IoT, cloud (SaaS and API), software, hardware, integrated systems, and consulting markets. A serial in-house entrepreneur, I bridge the gap between complex technologies and nascent markets by driving product marketing and go-to-market from strategy to execution.

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The four pillars of an effective and robust forecasting solution
Want to forecast cash flow, demand, sales, or resource allocation, check these core pillars for building an effective forecasting solution
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Optimize hospital bed allocations with reinforcement learning-trained AI
Learn how this open-source solution leverages Project Bonsai AI agents to optimize hospital bed allocation.
How to select the best CDP implementation strategy for your needs
A right CDP implementation strategy allows organizations to integrate multiple sources of information about potential and existing customers
data science technologies choice matrix
When are Autonomous Systems the best solution for your data-driven business challenge?
Not sure if Autonomous Systems fit your use case? Neal's decision matrix can help you select the best technologies and approach to take.
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Digital twins vs. simulations: the quick cheat sheet
Digital twin is a simulation whose states are updated to accurately reflect their real-life value. Learn more about digital twins vs simulations.
Machine Teaching, the not-so-secret AI weapon
Machine Teaching leverages human process expertise to help design Autonomous Systems AI agents with high explainability
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The smart factory: Industry 4.0 use cases technology enablers
Industry 4.0 mostly refers to the shift to a new kind of smart factories and manufacturing end-to-end processes. Check out the use cases here.
Process manufacturing example
Improve specification drift control robustness with AI
Learn how process manufacturing is susceptible to specification drift and how AI helps in improving specification drift control robustness
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Learn more about Autonomous Systems in 3 minutes
Want to learn more about Microsoft Project Bonsai, AI agents trained with Deep Reinforcement Learning, and Autonomous Systems? Start here.
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Optimize system-level efficiency of your extruder with AI
Learn how AI helps process manufacturing optimize system-level efficiency of their extruder with examples
Brain simulation
Advanced simulations, the key to successful Deep Reinforcement Learning-based AI deployments
The 5 common simulation strategies to train AI agents: Physics-based, custom software, off-the-shelf software, custom AI, and digital twins.
Plastic bottle manufacturing process
5 points to consider before starting your deep reinforcement learning autonomous system project in process manufacturing optimization
Here we discuss 5 points to consider before starting DRL based autonomous system project in process manufacturing optimization
4 reasons to use an ai-based simulator
Top 4 reasons to use an AI data-based simulator vs. a physics-based one for your process manufacturing reinforcement learning projects
The specificity and complexity of process manufacturing means an AI simulator is often the best solution to implement reinforcement learning AI agents...
Optimizing plastic extrusion with AI: Self-service demonstration
Microsoft Project Bonsai toolchain open new possibilities for process manufacturing improvements. This blog gives a step-by-step guide to plastic ext...
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How to improve process manufacturing productivity with real-world AI solutions
The availability of IoT-driven, real-world data combined with the latest AI techniques opens the door for practical AI use cases to help...
How to transition from digital transformation to digital acceleration
Digital acceleration is a paradigm shift that enables organizations to realize the full potential and ROI of their digital transformation.  
Preprocessing inputs for AI - Reducing complexity and increasing explainability
The one tip to decrease AI agent complexity and increase explainability
AI capabilities also brings complexities with it, which requires more advanced AI design skills as well as more training data
Transitioning from digital transformation to digital acceleration: What history teaches us.
Digital acceleration is a paradigm shift that enables organizations to realize the full potential and ROI of their digital transformation.  
What is the Microsoft Project Bonsai AI toolchain?
Understand Microsoft Project Bonsai in perspective with three other key concepts: Autonomous Systems, DNN, and DRL
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Top 3 AI use cases for supply chain optimization
Inventory management, production planning, warehouse storage and retrieval - here's how you can use AI to optimize your supply chains.
Designing an AI agent to optimize extruder operations: A real-life example
Here's how Neal and Microsoft worked with PepsiCo to design an Autonomous System by training an AI agent with DRL and Machine Teaching.
Approach to developing RL models
7 steps to design and deploy a Project Bonsai-based AI agent for an extruder
Learn the essential 7 steps an AI agent is required to take while designing and deploying an extruder using Project Bonsai
How PepsiCo makes the perfect Cheetos with the help of Autonomous Systems
Learn how Neal Analytics built an Autonomous System for Cheetos production yield optimization using Microsoft Project Bonsai
Autonomous systems design steps
Key elements for designing and deploying a successful reinforcement learning-trained AI solution with Microsoft Project Bonsai
Learn how Microsoft Project Bonsai toolchain helped the customers design and deploy AI agents with for robotics, process optimization, and more
supervised learning vs. DRL
Understanding the difference between supervised and reinforcement learning for deep neural networks
Learn the major differences between supervised learning and reinforcement learning, and how RL helps solve complex business problems using AI
Reduce waste and optimize yield
How AI can help food manufacturers reduce waste and optimize yield 
Learn how AI-powered technologies help food manufacturers improve manufacturing processes by reducing waste and optimizing yield
Accelerating Deep Reinforcement Learning training by applying AI Teacher/Student strategy to simulations  
Leveraging teacher-student or knowledge distillation training strategy helps AI practitioners achieve faster simulations with similar simulator accura...
Bringing AI from research labs to real-life scenarios with Deep Reinforcement Learning
Deep Reinforcement Learning leverages advanced process simulations and a trial-and-error approach to AI training
Understanding Autonomous Systems video series
Video describing where to use Autonomous Systems and how they work by leveraging Machine Teaching, Deep Reinforcement Learning & simulations.
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National retail chain leverages AI at the edge to detect stockouts
A retailer deployed StockView running a vision AI model on an Azure Stack Edge to visually detects empty spots on shelves.
Robot in production line
Is your business ready for predictive maintenance?
What do you need to leverage an AI-powered predictive maintenance solution? Learn what types of data are used, and how to scope your plan, in this art...
What is the difference between ML and AI?
Machine learning and artifical intelligence are related, but different. Learn about key use cases for AI and ML with these examples.