IBM Journey to AI blog

SEPTEMBER 3, 2024

Beyond architecture, engineers are finding value in other methods, such as quantization , chips designed specifically for inference, and fine-tuning , a deep learning technique that involves adapting a pretrained model for specific use cases. In this context, data quality often outweighs quantity.

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phData

OCTOBER 11, 2024

To help, phData designed and implemented AI-powered data pipelines built on the Snowflake AI Data Cloud , Fivetran, and Azure to automate invoice processing. phData’s Approach phData implemented Optical Character Recognition (OCR), which was performed using the open-source tool Paddle (Parallel Distributed Deep Learning).

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DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

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Iguazio

FEBRUARY 8, 2024

LLM models are large deep learning models that are trained on vast datasets, are adaptable to various tasks and specialize in NLP tasks. They are characterized by their enormous size, complexity, and the vast amount of data they process. Continuous monitoring of resources, data, and metrics.

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Pickl AI

DECEMBER 16, 2024

This step includes: Identifying Data Sources: Determine where data will be sourced from (e.g., Ensuring Time Consistency: Ensure that the data is organized chronologically, as time order is crucial for time series analysis. databases, APIs, CSV files).

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phData

JUNE 7, 2023

As computational power increased and data became more abundant, AI evolved to encompass machine learning and data analytics. This close relationship allowed AI to leverage vast amounts of data to develop more sophisticated models, giving rise to deep learning techniques.

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The MLOps Blog

MARCH 21, 2023

Olalekan said that most of the random people they talked to initially wanted a platform to handle data quality better, but after the survey, he found out that this was the fifth most crucial need. And when the platform automates the entire process, it’ll likely produce and deploy a bad-quality model. Allegro.io

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AWS Machine Learning Blog

MARCH 15, 2024

First, you need to address the data heterogeneity problem with medical imaging data arising from data being stored across different sites and participating organizations, known as a domain shift problem (also referred to as client shift in an FL system), as highlighted by Guan and Liu in the following paper.

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The MLOps Blog

AUGUST 11, 2023

Internally within Netflix’s engineering team, Meson was built to manage, orchestrate, schedule, and execute workflows within ML/Data pipelines. Meson managed the lifecycle of ML pipelines, providing functionality such as recommendations and content analysis, and leveraged the Single Leader Architecture.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. Data pipelines must seamlessly integrate new data at scale. Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources.

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