There is a lot of hype around AI nowadays. Apart from the big players like Google, Facebook and Apple, AI remains complicated for most companies. This is true because the challenges in using AI lie not only in understanding the algorithms. It is also about the software engineering challenges of rapidly processing massive amounts of data, running training jobs on specialized hardware, orchestrating parallel jobs, visualizing data and output from models and much more. But on an even bigger picture, it also requires a different organisational mindset and workflow when moving the problem statement to the data. This presentation will go into some of these challenges, outline how we have approached them at Peltarion.
Top rated books
- The Book of Why: The New Science of Cause and Effect
- Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World
- Human + Machine: Reimagining Work in the Age of AI
- The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
- AI Crash Course: A fun and hands-on introduction to machine learning, reinforcement learning, deep learning, and artificial intelligence with Python
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Collaboratively harness market-driven processes whereas resource-leveling internal or "organic" sources.