Machine Learning, Artificial Intelligence and DevOps Consultants
Get a Full Stack Machine Learning System tailored for you!
How Does This Work?
Why Would You Need This?
Integrating an end-to-end machine learning system is a complex task. To start off, there are too many engineering complexities with unrelated data modules being the most difficult-to-deal with problem.
Lots Of Human Effort Required
Data Engineer? ML Engineer? DevOps Engineer? Data Scientist? Full Stack Web Developer? Data Analyst?
You just have to deal with too many people and could incur unbearable costs.
Machine Learning Models Never Go Into Production
The biggest mistake people make with regard to machine learning is thinking that the models are just like any other type of software. Once a model is built and goes live, people assume it will continue working as normal. However, while machine learning machine learning is designed to get smarter over time, models will actually degrade in quality—and fast—without a constant feed of new data. Known as concept drift, this means that the predictions offered by static machine learning models become less accurate, and less useful, as time goes on. In some cases, this can even happen in a matter of days.