AI News

SEPTEMBER 29, 2024

With its predictive capabilities, AI ensures that applications scale efficiently, improving performance and resource allocation—marking a major advance over conventional methods. Additionally, they accelerate time-to-market for AI-driven innovations by enabling rapid data ingestion and retrieval, facilitating faster experimentation.

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AI Weekly

AUGUST 20, 2024

Companies are presented with significant opportunities to innovate and address the challenges associated with handling and processing the large volumes of data generated by AI. This massive collection of information, which is commonly referred to as "big data," is essential for business leaders. Can't make it?

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AWS Machine Learning Blog

OCTOBER 2, 2024

This allows enterprises to track key performance indicators (KPIs) for their generative AI models, such as I/O volumes, latency, and error rates. Opensearch Dashboards provides powerful search and analytical capabilities, allowing teams to dive deeper into generative AI model behavior, user interactions, and system-wide metrics.

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DataRobot Blog

SEPTEMBER 13, 2022

What Do Data Scientists Do? Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. What data scientists do is directly tied to an organization’s AI maturity level. Operationalization.

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The MLOps Blog

FEBRUARY 5, 2023

It can be used to perform complex data processing tasks such as windowed aggregations, joins, and event-time processing. Apache Spark : An open-source, distributed computing system that can handle big data processing tasks. Azure Stream Analytics : A cloud-based service that can be used to process streaming data in real-time.

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The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously. SIMT describes processors that are able to operate on data vectors and arrays (as opposed to just scalars), and therefore handle big data workloads efficiently.

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