Marktechpost

DECEMBER 3, 2024

Whether users need data from structured Excel spreadsheets or more unstructured formats like PowerPoint presentations, MegaParse provides efficient parsing while maintaining data integrity. Check out the GitHub Page. All credit for this research goes to the researchers of this project.

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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

NOVEMBER 15, 2023

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.

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Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning.

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Pickl AI

AUGUST 6, 2024

Summary : This comprehensive guide delves into data anomalies, exploring their types, causes, and detection methods. It highlights the implications of anomalies in sectors like finance and healthcare, and offers strategies for effectively addressing them to improve data quality and decision-making processes.

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Pickl AI

OCTOBER 17, 2024

Summary: The ETL process, which consists of data extraction, transformation, and loading, is vital for effective data management. Following best practices and using suitable tools enhances data integrity and quality, supporting informed decision-making. What is ETL? ETL stands for Extract, Transform, Load.

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Pickl AI

JULY 8, 2024

This blog explains how to build data pipelines and provides clear steps and best practices. From data collection to final delivery, we explore how these pipelines streamline processes, enhance decision-making capabilities, and ensure data integrity. What are Data Pipelines?

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TransOrg Analytics

AUGUST 21, 2024

Example: Amazon Implementation: Amazon employs integration of information interfaced by its online shopping platform, Alexa conversations, and usage of Prime Video service, among others. Tools Used: AWS glue for data integration and transformation. Reduced redundancy: 45% lessened in identical customer profiles.

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Pickl AI

APRIL 14, 2023

With the exponential growth of data and increasing complexities of the ecosystem, organizations face the challenge of ensuring data security and compliance with regulations. The same applies to data. It also fosters collaboration amongst different stakeholders, thus facilitating communication and data sharing.

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The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

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Pickl AI

AUGUST 1, 2023

The key sectors where Data Engineering has a major contribution include IT, Internet/eCommerce, and Banking & Insurance. Salary of a Data Engineer ranges between ₹ 3.1 Data Storage: Storing the collected data in various storage systems, such as relational databases, NoSQL databases, data lakes, or data warehouses.

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Unite.AI

NOVEMBER 11, 2024

By leveraging ML and natural language processing (NLP) techniques, CRM platforms can collect raw data from disparate sources, such as purchase patterns, customer interactions, buying behavior, and purchasing history. Data ingested from all these sources, coupled with predictive capability, generates unmatchable analytics.

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