AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML workloads at scale.

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Alation

FEBRUARY 20, 2020

For many enterprises, a hybrid cloud data lake is no longer a trend, but becoming reality. Due to these needs, hybrid cloud data lakes emerged as a logical middle ground between the two consumption models. AWS Propelling Hybrid Cloud Environments. The Problem with Hybrid Cloud Environments.

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ODSC - Open Data Science

JUNE 7, 2023

To make your data management processes easier, here’s a primer on data lakes, and our picks for a few data lake vendors worth considering. What is a data lake? First, a data lake is a centralized repository that allows users or an organization to store and analyze large volumes of data.

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AWS Machine Learning Blog

MAY 31, 2024

With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using the Amazon Web Services (AWS) tools without having to manage infrastructure. However, this is beyond the scope of this post.

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Dataversity

MAY 28, 2024

Most enterprises today store and process vast amounts of data from various sources within a centralized repository known as a data warehouse or data lake, where they can analyze it with advanced analytics tools to generate critical business insights.

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Smart Data Collective

AUGUST 9, 2019

However, more mainstream games use big data as well. Fortnite is one of the games that uses big data to offer great service to its customers. Even Forbes Tech Council has written about the benefits of data lakes in Fortnite. Processing and analyzing this data — petabytes worth — must happen somewhere.

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Women in Big Data

OCTOBER 9, 2024

The success of any data initiative hinges on the robustness and flexibility of its big data pipeline. What is a Data Pipeline? A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake.

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AWS Machine Learning Blog

JUNE 20, 2024

Our goal was to improve the user experience of an existing application used to explore the counters and insights data. The data is stored in a data lake and retrieved by SQL using Amazon Athena. Eitan Sela is a Generative AI and Machine Learning Specialist Solutions Architect at AWS.

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SQL Database AWS Machine Learning 101

O'Reilly Media

OCTOBER 19, 2021

In this article, we want to dig deeper into the fundamentals of machine learning as an engineering discipline and outline answers to key questions: Why does ML need special treatment in the first place? ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses.

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AWS Machine Learning Blog

JUNE 13, 2023

Overall, implementing a modern data architecture and generative AI techniques with AWS is a promising approach for gleaning and disseminating key insights from diverse, expansive data at an enterprise scale. AWS also offers foundation models through Amazon SageMaker JumpStart as Amazon SageMaker endpoints.

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IBM Journey to AI blog

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. This article endeavors to alleviate those confusions.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Big data isn’t an abstract concept anymore, as so much data comes from social media, healthcare data, and customer records, so knowing how to parse all of that is needed. This pushes into big data as well, as many companies now have significant amounts of data and large data lakes that need analyzing.

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IBM Journey to AI blog

JULY 17, 2023

sales conversation summaries, insurance coverage, meeting transcripts, contract information) Generate: Generate text content for a specific purpose, such as marketing campaigns, job descriptions, blogs or articles, and email drafting support.

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The MLOps Blog

JANUARY 23, 2023

As data is the foundation of any machine learning project, it is essential to have a system in place for tracking and managing changes to data over time. However, data versioning control is frequently given little attention, leading to issues such as data inconsistencies and the inability to reproduce results.

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Pickl AI

OCTOBER 10, 2024

Data transformation tools simplify this process by automating data manipulation, making it more efficient and reducing errors. These tools enable seamless data integration across multiple sources, streamlining data workflows. What is Data Transformation?

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Data Quality AWS Machine Learning Machine Learning 52

phData

MARCH 22, 2024

They are Ideal for situations where the data is already stored in data lakes and do not intend to load into Snowflake but need to use the features and performance of Snowflake. To learn more about Iceberg tables in Snowflake, read our article: What are Iceberg Tables in Snowflake and when to use them?

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SQL AWS Database Data Lakes 52

phData

FEBRUARY 14, 2023

Qlik Replicate Qlik Replicate is a data integration tool that supports a wide range of source and target endpoints with configuration and automation capabilities that can give your organization easy, high-performance access to the latest and most accurate data. Matillion is not a no-code solution, but rather a low-code solution.

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Data Warehouse Azure AWS Database 59

The MLOps Blog

JUNE 27, 2023

To provide you with a comprehensive overview, this article explores the key players in the MLOps and FMOps (or LLMOps) ecosystems, encompassing both open-source and closed-source tools, with a focus on highlighting their key features and contributions.

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Pickl AI

NOVEMBER 4, 2024

The global Big Data and Data Engineering Services market, valued at USD 51,761.6 This article explores the key fundamentals of Data Engineering, highlighting its significance and providing a roadmap for professionals seeking to excel in this vital field. million by 2028.

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Mlearning.ai

JULY 8, 2023

In this article we’re going to check what is an Azure function and how we can employ it to create a basic extract, transform and load (ETL) pipeline with minimal code. The source and target points can be of any storage service, for instance an Azure Blob Storage container, an AWS S3 bucket or a database system to name a few.

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Precisely

JANUARY 25, 2024

This article explores the nuances of mainframe optimization, outlining the drivers, common patterns, and key methods and tools for effective implementation. Cloud-based DevOps provides a modern, agile environment for developing and maintaining applications and services that interact with the organization’s mainframe data.

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ODSC - Open Data Science

APRIL 3, 2023

Data analysts often must go out and find their data, process it, clean it, and get it ready for analysis. This pushes into Big Data as well, as many companies now have significant amounts of data and large data lakes that need analyzing. Cloud Services: Google Cloud Platform, AWS, Azure.

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The MLOps Blog

MAY 17, 2023

Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. Cloud ETL Pipeline: Cloud ETL pipeline for ML involves using cloud-based services to extract, transform, and load data into an ML system for training and deployment. Happy Learning!

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DagsHub

NOVEMBER 11, 2024

Social media conversations, comments, customer reviews, and image data are unstructured in nature and hold valuable insights, many of which are still being uncovered through advanced techniques like Natural Language Processing (NLP) and machine learning. Many find themselves swamped by the volume and complexity of unstructured data.

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DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging. This article will discuss managing unstructured data for AI and ML projects. How to properly manage unstructured data.

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DagsHub

MARCH 21, 2024

It is suitable for a wide range of use cases, such as data lake storage, backup and recovery, and content delivery. S3 Compatibility : MinIO is compatible with S3 API, which is the standard interface for interacting with object storage in the AWS ecosystem. Note: Use bentoml serve service.py

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ODSC - Open Data Science

JANUARY 18, 2024

So as you take inventory of your existing skill set, you’ll want to start to identify the areas where you need to focus on to become a data engineer. These areas may include SQL, database design, data warehousing, distributed systems, cloud platforms (AWS, Azure, GCP), and data pipelines. First, articles.

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Data Engineer Data Engineering Data Engineering Data Engineering 52

Dataversity

MARCH 29, 2021

The post The Move to Public Cloud and an Intelligent Data Strategy appeared first on DATAVERSITY. Click to learn more about author Joe Gaska. It has taken a global pandemic for organizations to finally realize that the old way of doing businesses – and the legacy technologies and processes that came with it – are no longer going to cut it.

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DataOps Data Lakes Azure AWS 94

ODSC - Open Data Science

APRIL 24, 2023

To cluster the data we have to calculate distances between IPs — The number of all possible IP pairs is very large, and we had to solve the scale problem. Data Processing and Clustering Our data is stored in a Data Lake and we used PrestoDB as a query engine. Ori also has an AWS Data Analytics certification.

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Clustering SQL Algorithm Data Science 52

Alation

MARCH 21, 2023

Having been in business for over 50 years, ARC had accumulated a massive amount of data that was stored in siloed, on-premises servers across its 7 business domains. Using Alation, ARC automated the data curation and cataloging process. “So We have been able to combine datasets that we didn’t combine before.”

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Analytics Analytics Data Silos Big Data 52

The MLOps Blog

OCTOBER 20, 2023

Nevertheless, many data scientists will agree that they can be really valuable – if used well. And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. There are some outspoken critics , as well as passionate fans.

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SQL Database Data Scientist Python 52

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake.

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Data Pipeline ETL SQL Data Quality 59

The MLOps Blog

MAY 9, 2023

In this article, you will: 1 Explore what the architecture of an ML pipeline looks like, including the components. 3 Quickly build and deploy an end-to-end ML pipeline with Kubeflow Pipelines on AWS. Semi Koen’s article gives detailed insight into machine learning pipeline architectures.

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The MLOps Blog

MARCH 21, 2023

Learn from the practical experience of four ML teams on collaboration in this article. Data scientists and machine learning engineers need an infrastructure layer that lets them scale their work without having to be networking experts. (in This article defines architecture as the way the highest-level components are wired together.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources. In this post, we will explore building a reusable RAG data pipeline on LangChain —an open source framework for building applications based on LLMs—and integrating it with AWS Glue and Amazon OpenSearch Serverless.

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Alation

JUNE 7, 2023

As the authors of a Harvard Business Review article, “Roaring Out of Recession” note, three years after the Great Recession of 2007–2009, the most recent period of global economic instability, 9% of companies didn’t simply recover — they flourished, outperforming competitors by at least 10% in sales and profit growth.

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Data Silos Data Lakes Data Governance Business Intelligence 52

AWS Machine Learning Blog

JANUARY 21, 2025

This post builds on a previous post, Integrate QnABot on AWS with ServiceNow , and explores how to build an intelligent assistant using Amazon Lex , Amazon Bedrock Knowledge Bases , and a custom ServiceNow integration to create an automated incident management support experience. Data in Amazon S3 is encrypted by default.

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