Intercom, Inc.
OCTOBER 13, 2020
This post is based on that talk, and details our journey from early experimentation to release, as well as some valuable lessons we learned about how to implement machine learning (ML) in a real-world product. However, for smaller companies interested in delivering successful ML products, a lean approach can bring a lot of rewards.
Intercom, Inc.
JUNE 17, 2019
Interview assignments have become a common component of the hiring process for engineering roles. These technical problems, also known as email screeners or, as we call them at Intercom, take-home tests, are a useful way to initially evaluate the technical ability of candidates applying for engineering positions. Take-home test ??.
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Intercom, Inc.
SEPTEMBER 9, 2022
Building ML products requires balance – it’s pointless to start with the problem if the solution is unattainable, but you shouldn’t start with the tech if it can’t meet real customer needs. ML teams tend to invest a fair share of resources in research that never ships. And you don’t need as much hand engineering of features.
Intercom, Inc.
JANUARY 12, 2021
From premature optimization to over-engineering solutions for your product, it’s easy to get caught up in making technology decisions that slow you down instead of speeding you up. These things are usually artifacts of some engineer’s Objectives and Key Results (OKRs). The top ten technical strategies to avoid.
Callminer
JULY 18, 2019
Is topic modeling supervised machine learning (ML)? We have built a powerful set of tools that can build unsupervised ML topics, but as you know any unsupervised still needs some human intervention, just not in creation. What’s an “engineered category”? The goal is to find the topics in data.
DMG Consulting
MAY 8, 2020
AI-enabled WFM solutions leverage machine learning (ML), an AI technology that is effective at finding patterns. ML is being used to identify outliers or deviations when validating models and forecasts in an iterative learning process, as well as to automatically identify the algorithm best suited for each set of forecasting criteria.
Intercom, Inc.
MAY 22, 2019
I started in technology at Salesforce – I was their first female engineer and learned early on how valuable it can be to build a company from the perspective of your customer. Paige: What advice do you have product teams working on ML? Paige: It was really fun watching the teams here at Intercom build their first ML product.
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