Artificial Intelligence has been the epicenter of technological advancements in recent years, but the practicalities of implementing AI at scale often remain obscured behind the hype. This session aims to clear the fog and provide practical, experience-based insights on the subject.
We initiate our exploration with Google, a forerunner in successfully implementing AI at a planet-scale across various applications such as Gmail, Maps, Photos, and more. Extracting lessons from Google’s experience can provide an understanding of key strategies and potential pitfalls in large-scale AI implementation.
Despite the high prevalence of AI projects, a staggering 87% do not make it past the prototyping stage. We delve into the reasons behind this, exploring common roadblocks, misconceptions, and strategies for effective scaling of AI projects.Further enhancing your learning, we’ll discuss a variety of industry use cases that illustrate both successful and unsuccessful AI implementations.
Key Takeaways:
- Lessons from Google’s large-scale AI implementations.
- Understanding the common pitfalls and strategies to successfully scale AI projects.
- Best practices and lessons from industry use cases of successful and failed AI implementations.
- Strategies for mitigating the gap between AI hype and reality, focusing on practical execution.
