TechCrunch
DECEMBER 27, 2023
The New York Times is suing OpenAI and its close collaborator (and investor), Microsoft, for allegedly violating copyright law by training generative AI models on Times’ content. All rights reserved.
AWS Machine Learning - AI
NOVEMBER 27, 2024
Across diverse industries—including healthcare, finance, and marketing—organizations are now engaged in pre-training and fine-tuning these increasingly larger LLMs, which often boast billions of parameters and larger input sequence length. This approach reduces memory pressure and enables efficient training of large models.
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TechCrunch
MAY 30, 2022
Strong Compute , a Sydney, Australia-based startup that helps developers remove the bottlenecks in their machine learning training pipelines, today announced that it has raised a $7.8 ” Strong Compute wants to speed up your ML model training. . ” Strong Compute wants to speed up your ML model training.
CIO
OCTOBER 29, 2024
In 2018, I wrote an article asking, “Will your company be valued by its price-to-data ratio?” This will require the adoption of new processes and products, many of which will be dependent on well-trained artificial intelligence-based technologies. Stolen datasets can now be used to train competitor AI models.
TechCrunch
MARCH 16, 2021
But she’s identified a problem that most people managers will all too clearly understand: training and tools to be a great manager are at a shortage. Dulski explained that there are some tools for managers, like surveys from Gallup and Glint, and there are training options, like executive coaches.
TechCrunch
APRIL 8, 2021
Frustrated, she decided to try training engineers to meet her team’s hiring standards by mentoring at a local coding bootcamp. For one, they wanted to offer personalized training to help people not just learn to code, but to become “exceptional” software engineers. 13 investors say lifelong learning is taking edtech mainstream.
Xebia
MARCH 13, 2025
Unfortunately, the blog post only focuses on train-serve skew. Feature stores solve more than just train-serve skew. In a naive setup features are (re-)computed each time you train a new model. This lets your teams train models without repeating data preparation steps each time. You train a model with these features.
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