InfoQ Culture Methods
MARCH 7, 2024
When testing machine learning systems, we must apply existing test processes and methods differently. Machine Learning applications consist of a few lines of code, with complex networks of weighted data points that form the implementation.
Xebia
APRIL 2, 2024
After months of crunching data, plotting distributions, and testing out various machine learning algorithms you have finally proven to your stakeholders that your model can deliver business value. For the sake of argumentation, we will assume the machine learning model is periodically trained on a finite set of historical data.
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InfoWorld
FEBRUARY 15, 2024
Machine learning (ML) is becoming an increasingly important part of the modern application stack. Whether it’s large-scale, public large language models (LLM) like GPT or small-scale, private models trained on company content, developers need to find ways of including those models in their code.
Perficient
FEBRUARY 27, 2023
Machine learning is now being used all around the world and its helping analytics team greatly in saving costs and improving business decisions. A Machine learning project starts with Raw data and Ends with a web application that can predict outcomes and generate insights from raw data.
O'Reilly Media - Data
DECEMBER 12, 2019
For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Not least is the broadening realization that ML models can fail.
AWS Machine Learning - AI
MARCH 28, 2024
Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl , C4 , Wikipedia, and ArXiv. The resulting LLM outperforms LLMs trained on non-domain-specific datasets when tested on finance-specific tasks.
Altexsoft
JULY 23, 2020
When speaking of machine learning, we typically discuss data preparation or model building. The fusion of terms “machine learning” and “operations”, MLOps is a set of methods to automate the lifecycle of machine learning algorithms in production — from initial model training to deployment to retraining against new data.
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