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Building Resolution Bot: How to apply machine learning in product development

Intercom, Inc.

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.

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How to prepare for engineering interview assignments

Intercom, Inc.

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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Enhancing Enterprise Software Capabilities with AI and Machine Learning

Customer Think

For all living memory, enterprise software has been the engine that powers modern business. Both Artificial intelligence (AI) and machine learning (ML) are losing their futuristic status to becoming an essential part of […] Nonetheless, the enterprise software landscape is changing significantly.

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The new dawn of Machine Learning

Intercom, Inc.

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.

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Machine Learning Development: A Comprehensive Review of Booktest and Testing Tools

Lumoa

Think of this as a casual chat where we unravel the complexities of ML testing, making it digestible for everyone, regardless of their technical background. Because ML systems aren’t just coded; they’re trained. When we talk about ML systems, we’re referring to software that learns and adapts based on data.

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10 technical strategies to avoid when scaling your startup (and 5 to embrace)

Intercom, Inc.

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.

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How much historical data is needed for AI to improve forecast accuracy in workforce management?

DMG Consulting

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.

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