Quelle est la meilleure façon de choisir l’IA et les plates-formes informatiques de pointe?

"AI and edge" are not useful criteria. What kind of AI? Which edge? How do you even answer those questions with infinite variations on each? Do not buy "AI and edge". Figure out what problem you are trying to solve, and then look for an integrated end-to-end solution. A properly designed system will incorporate and balance the key variables of cost, resilience, network capabilities, latency budgets, and so on. Part of the solution may be on the device. Part of the solution might be on-premises. Part of the solution might be in a near-premises facility. Part of it might be in a centralized cloud. In summary: Look for solutions not ingredients.

Selecting AI and edge computing platforms isn't simply a matter of technical prowess; it's a culinary art. Think of your goals as the recipe, guiding you toward the right ingredients. The success of the dish lies in the blend of flavors - technology must align with your organizational needs and vision. Your chosen platforms should not just solve problems but enable growth, turning challenges into opportunities. Like a master chef, your choices should create a harmony that resonates with the unique taste of your business.

Imagine constructing a skyscraper. The quality and quantity of the materials you choose, coupled with the architectural planning, are paramount. In the realm of AI and edge computing, your data and resources are those building blocks. Assessing them isn't merely a matter of inventory; it's about understanding how each component contributes to the structure. Will they stand up to the demands? Are they adaptable to changes in design? Like the strongest steel, your chosen platforms and tools must not only fit the blueprint but offer resilience, scalability, and a future-ready stance. Aligning these elements will turn the vision into a towering success.

Comparing AI and edge computing platforms should be developer-driven, even though costs, licenses, and business-risks etc. need to be considered and management will be involved in some parts of the process

Comparing AI and edge computing platforms is an executive decision requiring precision and foresight. Consider it a strategic alignment, analyzing compatibility, functionality, usability, reliability, and cost as you would with a key business alliance. Look for platforms that sync with your existing frameworks and languages. Test their performance rigorously, just as a seasoned investor scrutinizes opportunities. The final selection must echo your vision, serve current needs, and exhibit the flexibility to grow and evolve. It's an investment in the future.

Choosing the right AI and edge computing platforms isn't about merely following trends; it's about learning from the wisdom of those who have embarked on similar journeys. Think of it like a master class with the greatest chefs. You don't just learn their recipes; you understand their methods, their mistakes, their triumphs. You explore reviews, tutorials, and join forums not just to gather information but to find inspiration. Your choices are then guided not just by what others have done, but how they've thought, how they've approached their unique challenges. The path has been trodden, but it's your footprints that will make the journey yours.

In the unfolding narrative of selecting AI and edge computing platforms, experimentation and iteration become the climax of your story. Imagine yourself as an inventor in your workshop, toiling over a small-scale prototype. Like Thomas Edison with his countless trials to invent the light bulb, your journey is marked by trial and error. Testing on different data sets, devices, and scenarios, collecting feedback, fine-tuning your solution - it's a persistent pursuit. Each iteration is a step closer to innovation. Your path to the optimal platforms and tools isn't a straight line; it's a rich tapestry woven with creativity, resilience, and scientific rigor. The masterpiece emerges not from a single choice but a continuous dance of discovery.

Define the KPIs (latency, CPu usage, memory, ...) and benchmark 2-3 of the most suitable solutions (from the prior research) in a setup / scenario that is as close as possible to the real case, especially using the hw-sw stack on which it is supposed to run later on --> this will pay off especially when you scale your solution