CIO
AUGUST 7, 2023
And, in fact, McKinsey research argues the future could indeed be dazzling, with gen AI improving productivity in customer support by up to 40%, in software engineering by 20% to 30%, and in marketing by 10%. It does not allow for integration of proprietary data and offers the fewest privacy and IP protections.
TechCrunch
MAY 10, 2022
-based companies, 44% said that they’ve not hired enough, were too siloed off to be effective and haven’t been given clear roles. As a result, most machine learning tasks in an organization are bottlenecked on an oversubscribed centralized data science team,” Molino told TechCrunch via email.
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Erik Bernhardsson
DECEMBER 15, 2020
I had my first job as a software engineer in 1999, and in the last two decades I've seen software engineering changing in ways that have made us orders of magnitude more productive. Mediocre software exists because someone wasn't able to hire better engineers, or they didn't have time, or whatever.
CIO
JULY 14, 2023
In the shaper model, you’re leveraging existing foundational models, off the shelf, but retraining them with your own data.” A general LLM won’t be calibrated for that, but you can recalibrate it—a process known as fine-tuning—to your own data. Every company will be doing that,” he adds. “In
Mentormate
JANUARY 11, 2022
Sure, you might get lucky and find the right person with the right skills in the right geography, but it’s not realistic to scale up and retain a larger engineering organization that way. Only the largest engineering organizations have the scale to make this kind of continuous investment. Where Did All the People Go?
Cloudera
JULY 24, 2023
In their effort to reduce their technology spend, some organizations that leverage open source projects for advanced analytics often consider either building and maintaining their own runtime with the required data processing engines or retaining older, now obsolete, versions of legacy Cloudera runtimes (CDH or HDP).
Netflix Tech
MARCH 7, 2024
Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.
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