Customer Service is likely one of the most complex and frustrating parts of your business, but it doesn’t have to be. Machine Learning is making strides in automating and improving parts of the Customer Service (CS) stack quickly, like auto-routing tickets to the right agent or improving your knowledge base. Our Vertical Spotlight on Customer Service will give you all the information you need to get started.
All of our vertical spotlights use our Machine Learning Vertical Framework: we analyze unique use cases, leadership, domain specific problems, and model tradeoffs.
If your neural nets are getting larger and larger but your training sets aren’t, you’re going to hit an accuracy wall. If you want to train better models with less data, I’ve got good news for you.
Dataset augmentation – the process of applying simple and complex transformations like flipping or style transfer to your data – can help overcome the increasingly large requirements of Deep Learning models. This post will walk through why dataset augmentation is important, how it works, and how Deep Learning fits in to the equation.
Source: Case Engineering
Diagnostics is part of the core of healthcare — research suggests a third of all Healthcare AI SaaS companies are tackling just this sector.
Machine Learning can automate parts of the diagnostic stack, aid doctors in deciding how to interpret tests, and greatly reduce errors in communication. This post will walk through popular use cases, the challenges inherent in applying ML models in diagnostics, and some of the tradeoffs to be made in model selection.
For every dollar of fraud that financial services companies suffer, they incur $2.67 in costs to their business. With more entry points in the digital age and increasingly sophisticated attackers, tackling fraud manually is quickly fading to irrelevance: but Machine Learning offers a promising way to automate the process, as well as surface more nuanced fraud patterns.
This post will walk through the challenges of applying ML models to fraud detection, popular applications, and tradeoffs to think about in model selection.
Slack is one of the fastest growing companies of all time, and there’s a good chance it’s also the messaging app that you use for work. The Algorithmia Slack Client lets you integrate Machine Learning into your Slack channels – both as slack commands and as bot users – giving you more firepower on top of the already vibrant ecosystem of third party Slack integrations.