Experimentation IT Modeling Optimization 
CIO Business Intelligence
FEBRUARY 22, 2024
With the aim to accelerate innovation and transform its digital infrastructures and services, Ferrovial created its Digital Hub to serve as a meeting point where research and experimentation with digital strategies could, for example, provide new sources of income and improve company operations.
Ontotext
JULY 24, 2023
I did some research because I wanted to create a basic framework on the intersection between large language models (LLM) and data management. LLM is by its very design a language model. The meaning of the data is the most important component – as the data models are on their way to becoming a commodity.
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CIO Business Intelligence
FEBRUARY 14, 2024
That’s not just about the cost of preparing a larger data set than you need, which takes expertise that’s still uncommon and commands a high salary, but also what you’re teaching the model. But the usual laundry list of priorities for IT hasn’t gone away.
O'Reilly on Data
JULY 28, 2020
Without clarity in metrics, it’s impossible to do meaningful experimentation. Identifying the problem. The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. The worst case scenario is when a business doesn’t have any metrics.
CIO Business Intelligence
MARCH 7, 2023
OpenAI’s text-generating ChatGPT, along with its image generation cousin DALL-E, are the most prominent among a series of large language models, also known as generative language models or generative AI, that have captured the public’s imagination over the last year. That’s incredibly powerful.” The second is for project staffing.
Domino Data Lab
SEPTEMBER 10, 2020
Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant. This means many projects get stuck in endless research and experimentation.
CIO Business Intelligence
MAY 23, 2022
Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex. The cloud is great for experimentation when data sets are smaller and model complexity is light. Potential headaches of DIY on-prem infrastructure.
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