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

Intercom, Inc.

We are at the start of a revolution in customer communication, powered by machine learning and artificial intelligence. At Intercom, we have taken advantage of these technologies relatively early. It’s also easy to over- or under- invest in the technology. The cupcake approach to building bots.

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The new dawn of Machine Learning

Intercom, Inc.

In the past five years, we’ve seen neural network technology really take off into its own. We wanted to know what’s up with this surge, so we’ve asked our Director of Machine Learning, Fergal Reid , if we can pick his brain for today’s episode. It’s all about artificial intelligence and machine learning.

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Machine Learning for Product Managers – A Quick Primer

Mind the Product

Currently, there are thousands of products, apps, and services driven by machine learning (ML) that we use every day. As was reported by Crunchbase, in 2019 there were 8,705 companies and startups that rely on this technology. According to PWC’s research, it’s predicted that ML and AI technologies will contribute about $15.7

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The Significant Roles of Artificial Intelligence in The Education Sector

The Product Coalition

Artificial intelligence is the most suitable choice to succeed in all challenges in learning different things. AI technology in education develops your learning method properly. AI-powered learning techniques help teachers to examine the grasping power of learners.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations.

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Large Language Models for the Rest of Us

The Product Coalition

AI is having its Cambrian explosion moment (although perhaps not its first), led by the recent developments in large language models and their popularization. link] Veterans in the NLP space are anxious about how suddenly every problem is an LLM problem. This meme sums it up nicely. Boom, you’re off to a great start.

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The Strategy Stack: Connecting Business, Product, and Technology Strategy

Roman Pichler

To ensure that the right technologies are applied, you’ll benefit from using a technology strategy. The company took the strategic decision to heavily invest in artificial intelligence and now uses AI to help Office users be more productive. [1] Similarly, the technology strategy is directed by the business strategy.

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MLOps 101: The Foundation for Your AI Strategy

Many organizations are dipping their toes into machine learning and artificial intelligence (AI). Download this comprehensive guide to learn: What is MLOps? How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects? Why do AI-driven organizations need it?

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5 Things a Data Scientist Can Do to Stay Current

And more is being asked of data scientists as companies look to implement artificial intelligence (AI) and machine learning technologies into key operations. Fostering collaboration between DevOps and machine learning operations (MLOps) teams. Collecting and accessing data from outside sources.

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Trusted AI 102: A Guide to Building Fair and Unbiased AI Systems

The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. Numerous high-profile examples demonstrate the reality that AI is not a default “neutral” technology and can come to reflect or exacerbate bias encoded in human data.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.

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Data Science Fails: Building AI You Can Trust

The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology.

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A Tale of Two Case Studies: Using LLMs in Production

Speaker: Tony Karrer, Ryan Barker, Grant Wiles, Zach Asman, & Mark Pace

Join our exclusive webinar with top industry visionaries, where we'll explore the latest innovations in Artificial Intelligence and the incredible potential of LLMs. We'll walk through two compelling case studies that showcase how AI is reimagining industries and revolutionizing the way we interact with technology.

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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.

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Generative AI Deep Dive: Advancing from Proof of Concept to Production

Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage

Executive leaders and board members are pushing their teams to adopt Generative AI to gain a competitive edge, save money, and otherwise take advantage of the promise of this new era of artificial intelligence.