AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface. Choose Create stack.

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Precisely

MARCH 14, 2023

First, private cloud infrastructure providers like Amazon (AWS), Microsoft (Azure), and Google (GCP) began by offering more cost-effective and elastic resources for fast access to infrastructure. Now, almost any company can build a solid, cost-effective data analytics or BI practice grounded in these new cloud platforms.

Data Quality 52

Data Quality Cloud Data Data Pipeline Data Observability 52

Dataconomy

SEPTEMBER 4, 2023

A well-documented case is the UK government’s failed attempt to create a unified healthcare records system, which wasted billions of taxpayer dollars. Downtime, like the AWS outage in 2017 that affected several high-profile websites, can disrupt business operations.

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IBM Journey to AI blog

JUNE 20, 2024

This growth underscores the escalating need for robust governance frameworks that ensure AI systems are transparent, fair and comply with increasing regulatory demands. Model governance Organizations can manage the entire lifecycle of their AI models with enhanced visibility and control.

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AWS Machine Learning Blog

APRIL 26, 2023

Ensuring data quality, governance, and security may slow down or stall ML projects. Through ML EBA, experienced AWS ML subject matter experts work side by side with your cross-functional team to provide prescriptive guidance, remove blockers, and build organizational capability for a continued ML adoption.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

This is a joint blog with AWS and Philips. Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. Data Management – Efficient data management is crucial for AI/ML platforms.

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Data Science Dojo

AUGUST 24, 2023

MLOps facilitates automated testing mechanisms for ML models, which detects problems related to model accuracy, model drift, and data quality. Data collection and preprocessing The first stage of the ML lifecycle involves the collection and preprocessing of data.

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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. AWS Glue AWS Glue is a fully managed ETL service provided by Amazon Web Services.

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The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

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Pickl AI

JANUARY 12, 2025

However, there are also challenges that businesses must address to maximise the various benefits of data-driven and AI-driven approaches. Data quality : Both approaches’ success depends on the data’s accuracy and completeness. How do We Integrate Data-driven and AI-driven Models?

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Pickl AI

NOVEMBER 4, 2024

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

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Pickl AI

AUGUST 30, 2024

Assessment Evaluate the existing data quality and structure. This step involves identifying any data cleansing or transformation needed to ensure compatibility with the target system. Assessing data quality upfront can prevent issues later in the migration process.

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Pickl AI

OCTOBER 17, 2024

Best Practices for ETL Efficiency Maximising efficiency in ETL (Extract, Transform, Load) processes is crucial for organisations seeking to harness the power of data. Implementing best practices can improve performance, reduce costs, and improve data quality.

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Pickl AI

OCTOBER 15, 2024

Talend Talend is a leading open-source ETL platform that offers comprehensive solutions for data integration, data quality , and cloud data management. It supports both batch and real-time data processing , making it highly versatile. Informatica Informatica is a widely recognised enterprise-grade ETL platform.

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Alation

SEPTEMBER 30, 2022

This June, Snowflake recognized Alation as its data governance partner of the year for the second year in a row, and Eckerson , IDC , BARC , Dresner , and Constellation all released reports just this summer naming Alation a data catalog leader. Everything and Everyone: The Catalog is the platform for Data Intelligence.

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Precisely

MAY 3, 2023

Modern Data Architectures Panel To discuss the importance of data quality, governance, and observability for digital transformation success, Precisely COO Eric Yau is joined by panelists Sanjeev Mohan, former Gartner Research VP, Atif Salam, CxO Advisor & Enterprise Technologist at AWS, and Tendü Yogurtçu, PhD, CTO at Precisely.

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Precisely

MAY 13, 2024

The right data integration solution helps you streamline operations, enhance data quality, reduce costs, and make better data-driven decisions. Are these sources a match for all my batch data ingest and change data capture (CDC) needs? What data governance controls do your solutions have in place?

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The MLOps Blog

MARCH 15, 2023

AWS provides several tools to create and manage ML model deployments. 2 If you are somewhat familiar with AWS ML base tools, the first thing that comes to mind is “Sagemaker”. AWS Sagemeaker is in fact a great tool for machine learning operations (MLOps) to automate and standardize processes across the ML lifecycle. S3 buckets.

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Alation

DECEMBER 6, 2022

At Informatica, he pioneered the concept of self-service data integration and data quality. He brings in decades of experience in data integration, as well as metadata management and data governance. He has also been instrumental in bootstrapping and steering major partnerships with IBM and AWS.

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Dataversity

JUNE 2, 2021

The deliverability of cloud governance models has improved as public cloud usage continues to grow and mature. These models allow large enterprises to tier and scale their AWS Accounts, Azure Subscriptions, and Google Projects across hundreds and thousands of cloud users and services. Click to learn more about author Jay Chapel.

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phData

SEPTEMBER 19, 2023

Cost reduction by minimizing data redundancy, improving data storage efficiency, and reducing the risk of errors and data-related issues. Data Governance and Security By defining data models, organizations can establish policies, access controls, and security measures to protect sensitive data.

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Precisely

JUNE 6, 2023

At Precisely’s Trust ’23 conference, Chief Operating Officer Eric Yau hosted an expert panel discussion on modern data architectures. The group kicked off the session by exchanging ideas about what it means to have a modern data architecture. Data observability also helps users identify the root cause of problem in the data.

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Alation

MARCH 21, 2023

What are common data challenges for the travel industry? Some companies struggle to optimize their data’s value and leverage analytics effectively. When companies lack a data governance strategy , they may struggle to identify all consumer data or flag personal data as subject to compliance audits.

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Precisely

MARCH 9, 2023

The same can be said of other leading platforms such as Databricks, Cloudera, and data lakes offered by the major cloud providers such as AWS, Google, and Microsoft Azure. Whichever platform you choose, Precisely Connect can help you integrate data from any source, including the critical mainframe systems like IBM i, z/OS, and others.

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Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. IDF works natively on cloud platforms like AWS. DataOps has emerged as an exciting solution.

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DagsHub

FEBRUARY 29, 2024

Data preparation involves multiple processes, such as setting up the overall data ecosystem, including a data lake and feature store, data acquisition and procurement as required, data annotation, data cleaning, data feature processing and data governance.

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Pickl AI

JULY 25, 2023

Data Integration and ETL (Extract, Transform, Load) Data Engineers develop and manage data pipelines that extract data from various sources, transform it into a suitable format, and load it into the destination systems. Data Quality and Governance Ensuring data quality is a critical aspect of a Data Engineer’s role.

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Pickl AI

APRIL 26, 2024

I break down the problem into smaller manageable tasks, define clear objectives, gather relevant data, apply appropriate analytical techniques, and iteratively refine the solution based on feedback and insights. Describe a situation where you had to think creatively to solve a data-related challenge.

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Alation

DECEMBER 14, 2021

But by partnering with a professional consultant in data quality management systems, forward-thinking enterprises gain a significant competitive edge over their competitors. Amazon Web Services (AWS). Let’s start with some simple definitions. What is cloud-native? Examples of cloud-hosting providers include: Alibaba Cloud.

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Heartbeat

MAY 29, 2023

Some of the steps that can be taken include: Data Governance: Implementing rigorous data governance policies that ensure fairness, transparency, and accountability in the data used to train LLMs.

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DagsHub

OCTOBER 23, 2024

Use automated tagging tools and natural language processing (NLP) models to extract metadata from text-based data. Tooling : Apache Tika , ElasticSearch , Databricks , and AWS Glue for metadata extraction and management. It also aids in identifying the source of any data quality issues.

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AWS Machine Learning Blog

APRIL 3, 2025

At the heart of this transformation is the OMRON Data & Analytics Platform (ODAP), an innovative initiative designed to revolutionize how the company harnesses its data assets. Data governance challenges Maintaining consistent data governance across different systems is crucial but complex.

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Data Science Blog

JULY 23, 2023

It advocates decentralizing data ownership to domain-oriented teams. Each team becomes responsible for its Data Products , and a self-serve data infrastructure is established. This enables scalability, agility, and improved data quality while promoting data democratization.

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Alation

NOVEMBER 11, 2021

As IT leaders oversee migration, it’s critical they do not overlook data governance. Data governance is essential because it ensures people can access useful, high-quality data. Let’s take a look at some of the key principles for governing your data in the cloud: What is Cloud Data Governance?

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Flipboard

MARCH 21, 2025

Through this unified query capability, you can create comprehensive insights into customer transaction patterns and purchase behavior for active products without the traditional barriers of data silos or the need to copy data between systems. Data analysts discover the data and subscribe to the data.

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Precisely

FEBRUARY 9, 2024

They’re where the world’s transactional data originates – and because that essential data can’t remain siloed, organizations are undertaking modernization initiatives to provide access to mainframe data in the cloud. That approach assumes that good data quality will be self-sustaining.

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phData

JULY 18, 2023

Better Transparency: There’s more clarity about where data is coming from, where it’s going, why it’s being transformed, and how it’s being used. Improved Data Governance: This level of transparency can also enhance data governance and control mechanisms in the new data system.

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