Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

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Pickl AI

OCTOBER 19, 2023

Data quality plays a significant role in helping organizations strategize their policies that can keep them ahead of the crowd. Hence, companies need to adopt the right strategies that can help them filter the relevant data from the unwanted ones and get accurate and precise output.

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Pickl AI

OCTOBER 10, 2023

In this blog, we are going to unfold the two key aspects of data management that is Data Observability and Data Quality. Data is the lifeblood of the digital age. Today, every organization tries to explore the significant aspects of data and its applications.

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Precisely

SEPTEMBER 19, 2024

Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust data governance for AI ensures data privacy, compliance, and ethical AI use. Proactive data quality measures are critical, especially in AI applications.

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ODSC - Open Data Science

FEBRUARY 24, 2023

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. With these data exploration tools, you can determine if your data is accurate, consistent, and reliable.

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Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data Ensuring data quality and integrity Data quality and integrity are essential for accurate data analysis. Data engineers are responsible for ensuring that the data collected is accurate, consistent, and reliable.

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Dataconomy

JULY 28, 2023

In the digital age, the abundance of textual information available on the internet, particularly on platforms like Twitter, blogs, and e-commerce websites, has led to an exponential growth in unstructured data. Text data is often unstructured, making it challenging to directly apply machine learning algorithms for sentiment analysis.

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Precisely

JUNE 26, 2023

They shore up privacy and security, embrace distributed workforce management, and innovate around artificial intelligence and machine learning-based automation. The key to success within all of these initiatives is high-integrity data. Only 46% of respondents rate their data quality as “high” or “very high.”

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Pickl AI

OCTOBER 11, 2023

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data. What is Data Observability?

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Precisely

SEPTEMBER 11, 2023

It provides a unique ability to automate or accelerate user tasks, resulting in benefits like: improved efficiency greater productivity reduced dependence on manual labor Let’s look at AI-enabled data quality solutions as an example. Problem: “We’re unsure about the quality of our existing data and how to improve it!”

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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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Snorkel AI

OCTOBER 4, 2023

Artificial intelligence and machine learning (AI/ML) offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management. Traditional credit scoring models rely on static variables and historical data like income, employment, and debt-to-income ratio. Book a demo today.

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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. We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one.

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Snorkel AI

OCTOBER 4, 2023

Artificial intelligence and machine learning (AI/ML) offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management. Traditional credit scoring models rely on static variables and historical data like income, employment, and debt-to-income ratio. Book a demo today.

AI 59

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Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

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Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

OCTOBER 4, 2023

Artificial intelligence and machine learning (AI/ML) offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management. Traditional credit scoring models rely on static variables and historical data like income, employment, and debt-to-income ratio.

AI 52

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The MLOps Blog

MARCH 15, 2023

Scalability : A data pipeline is designed to handle large volumes of data, making it possible to process and analyze data in real-time, even as the data grows. Data quality : A data pipeline can help improve the quality of data by automating the process of cleaning and transforming the data.

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Alation

JULY 6, 2021

Modern data governance relies on automation, which reduces costs. Automated tools make data governance processes very cost-effective. Machine learning plays a key role, as it can increase the speed and accuracy of metadata capture and categorization. This empowers leaders to see and refine human processes around data.

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Alation

AUGUST 26, 2021

As data collection and volume surges, enterprises are inundated in both data and its metadata. For this reason, data intelligence software has increasingly leveraged artificial intelligence and machine learning (AI and ML) to automate curation activities, which deliver trustworthy data to those who need it.

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Dataconomy

MARCH 26, 2025

ETL architecture components The architecture of ETL pipelines is composed of several key components that ensure seamless operation throughout the data processing stages: Data profiling: Assesses the quality of raw data, determining its suitability for the ETL process and setting the stage for effective transformation.

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