AWS Machine Learning Blog

NOVEMBER 17, 2023

Amazon Rekognition Content Moderation , a capability of Amazon Rekognition , automates and streamlines image and video moderation workflows without requiring machine learning (ML) experience. This process involves the utilization of both ML and non-ML algorithms. For more detailed information, refer to the GitHub repo.

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Towards AI

MARCH 1, 2023

The technology behind GitHub’s new code search This post provides a high-level explanation of the inner workings of GitHub’s new code search and offers a glimpse into the system architecture and technical underpinnings of the product.

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Heartbeat

JANUARY 9, 2024

This is brought on by various developments, such as the availability of data, the creation of more potent computer resources, and the development of machine learning algorithms. Deployment : The adapted LLM is integrated into this stage's planned application or system architecture.

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O'Reilly Media

NOVEMBER 19, 2024

The “distance” between each pair of neighbors can be interpreted as a probability.When a question prompt arrives, run graph algorithms to traverse this probabilistic graph, then feed a ranked index of the collected chunks to LLM. One way to build a graph to use is to connect each text chunk in the vector store with its neighbors.

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IBM Journey to AI blog

JULY 18, 2024

Computing Computing is being dominated by major revolutions in artificial intelligence (AI) and machine learning (ML). The algorithms that empower AI and ML require large volumes of training data, in addition to strong and steady amounts of processing power. Distributed computing supplies both.

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Google Research AI blog

FEBRUARY 14, 2023

We’re also progressing towards making our learning algorithms more data efficient so that we’re not relying only on scaling data collection. Further improvements are gained by utilizing a novel structured dynamical systems architecture and combining RL with trajectory optimization , supported by novel solvers.

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The MLOps Blog

APRIL 17, 2023

As an MLOps engineer on your team, you are often tasked with improving the workflow of your data scientists by adding capabilities to your ML platform or by building standalone tools for them to use. Giving your data scientists a platform to track the progress of their ML projects. Experiment tracking is one such capability.

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