Tom Tunguz
JULY 8, 2020
This modern architecture for data analysis, operational metrics, and machine learning enables companies to process data in new ways. Various roles in your organization, like data scientists, data engineers, application developers, and business analysts, can access data with their choice of analytic tools and frameworks.
User Pilot
MAY 10, 2023
TL;DR AI marketing involves leveraging AI technologies like machine learning, deep learning, etc., There are four groups of marketing AI apps today: standalone machine learning, standalone task automation, integrated machine learning, and integrated task automation apps. AI-powered marketing tools.
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Tom Tunguz
DECEMBER 18, 2019
Look no further than AWS Re:Invent where Amazon announced an entire suite of Machine Learning tools that compete with nearly every player in the ecosystem in every level of the stack. Data engineering is the new Customer Success. Data engineering is the new Customer Success. True but hard to judge.
ReSci
JANUARY 23, 2018
Author: Avi Sanadhya, ReSci Platform Engineering Team At Retention Science we deliver personalized marketing campaigns powered by machine learning to drive a deeper level of customer engagement. Our AI engine, Cortex, is responsible for billions of predictions daily and hundreds of millions of personalized emails each month.
SaaStr
OCTOBER 31, 2019
It was around that time about 12 years ago that Jeff Bezos launched AWS, and some of you may remember that, when he did this, Wall Street analysts were looking at him and saying, “Why would you take what’s already a very unprofitable business and drive it further into the red by investing in this AWS initiative?”
Intercom, Inc.
OCTOBER 14, 2019
Products like Amazon Web Services (AWS) and the rise of engineering talent globally have reduced the barrier of entry for software startups in recent years. Buyers aren’t locked into long-term purchases the same way they were with on-premises solutions. There’s more competition and more choices for software buyers.
Tom Tunguz
NOVEMBER 8, 2023
Most sophisticated data teams run like software engineering teams with product requirement documents, ticketing systems, & sprints. Whether it’s data being used inside applications, feeding machine learning models, or downstream analysis, companies are increasingly reliant on this data, and that’s not changing.
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