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. Do you think teams should have embedded ML engineers?
eglobalis
JULY 2, 2024
The main point here is that we are talking about NPS, but no individual metric can supply all needed information; therefore, I called this article “360 Degree Revolution” since all metrics plus data supply your organization with a much better reality check than anything else.
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C3Centricity
FEBRUARY 18, 2019
Before summarising what I presented, I’d like to share some of the ideas and takeaways that I discovered about digital marketing and the impact of AI (artificial intelligence) and ML (machine learning). Luckily I was taking a far more practical approach to digital marketing, AI and ML, which I am happy to say was met with enthusiasm.
Intercom, Inc.
JANUARY 12, 2021
Although they may seem like strong opinions, many of these tips echo the main tenets of software engineering: work with you’ve got, design solutions as needed, don’t repeat yourself, and keep it simple, stupid! 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. We use this technique in the initial exploring phase to find what the common topics in the data.
Intercom, Inc.
JUNE 17, 2019
Here is a short list: Provide a README with your submission – where’s the main entry point? Although most of the production code we write is Ruby or Javascript, we recently received a great submission written in ML (we’d heard of ML but never written any). This might sound daunting, but it shouldn’t be. Where are the tests?
Aquire
JULY 1, 2021
Machine learning (ML). Conversational applications use ML to better understand human interactions. The application uses ML to learn and finetune responses over time. Here are the main benefits of conversational AI: Achieve more personalized and easy interactions. What do humans mean? Enter conversational AI.
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