Google wasn’t an AI company from the start. PageRank in itself didn’t provide it with any defensibility or moat against new entrants. Anyone with a better algorithm could have outperformed it eventually.
What made Google an AI company was the huge click stream data asset showing them what links a user clicks on after different search queries. This data helped Google build a more accurate search engine product (A), which in turn helps them acquire more users (B), which in turn results in their having even more user data (C).
This kind of positive feedback loop is hard for competitors to break into.
Machine learning models provide your end-users with intelligent personalisation, and your business with disruptive innovation. These two combined through the right strategy helps your business build defensibility against your competition.
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. That said, getting your models into production is the fundamental challenge in machine learning. With our MLOps expertise we are well positioned to use the set of proven principles aimed at solving this problem in a reliable and automated way, so you can operationalize your machine learning models faster. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you’re trying to crack.
We can help you:
- Apply DevOps best practices to machine learning
- Build production machine learning systems and maintain them
- Monitor, instrument, load-test, and operationalize machine-learning systems
- Choose the correct MLOps tools for a given machine learning task
- Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware