Remove Deep Learning Remove Optimization Remove Unstructured Data
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TensorFlow Object Detection — 1.0 & 2.0: Train, Export, Optimize (TensorRT), Infer (Jetson Nano)

Analytics Vidhya

Train, Export, Optimize (TensorRT), Infer (Jetson Nano) appeared first on Analytics Vidhya. Part 1 — Detailed steps from training a detector on a custom dataset to inferencing on jetson nano board or cloud using TensorFlow 1.15. The post TensorFlow Object Detection — 1.0 & 2.0:

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. From automating tedious tasks to unlocking insights from unstructured data, the potential seems limitless. Ive seen this firsthand.

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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO Business Intelligence

All industries and modern applications are undergoing rapid transformation powered by advances in accelerated computing, deep learning, and artificial intelligence. The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data. Through relentless innovation.

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The Rise of Unstructured Data

Cloudera

Here we mostly focus on structured vs unstructured data. In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else.

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An AI Data Platform for All Seasons

Rocket-Powered Data Science

One example of Pure Storage’s advantage in meeting AI’s data infrastructure requirements is demonstrated in their DirectFlash® Modules (DFMs), with an estimated lifespan of 10 years and with super-fast flash storage capacity of 75 terabytes (TB) now, to be followed up with a roadmap that is planning for capacities of 150TB, 300TB, and beyond.

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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

Many people are confused about these two, but the only similarity between them is the high-level principle of data storing. It is vital to know the difference between the two as they serve different principles and need diverse sets of eyes to be adequately optimized. Data Warehouse.

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The DataOps Vendor Landscape, 2021

DataKitchen

Monte Carlo DataData reliability delivered. Data breaks. Observe, optimize, and scale enterprise data pipelines. . Validio — Automated real-time data validation and quality monitoring. . DataMo – Datmo tools help you seamlessly deploy and manage models in a scalable, reliable, and cost-optimized way.

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