Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

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The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate. Allegro.io

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Smart Data Collective

SEPTEMBER 28, 2022

A data management solution can help you make better business decisions by giving you access to the right information at the right time. Data engineering services can analyze large amounts of data and identify trends that would otherwise be missed. Big data management increases the reliability of your data.

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Alation

JANUARY 26, 2023

Governance can — and should — be the responsibility of every data user, though how that’s achieved will depend on the role within the organization. This article will focus on how data engineers can improve their approach to data governance. How can data engineers address these challenges directly?

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Tableau

SEPTEMBER 23, 2021

For example, Tableau data engineers want a single source of truth to help avoid creating inconsistencies in data sets, while line-of-business users are concerned with how to access the latest data for trusted analysis when they need it most. Data quality: Gone are the days of “data is data, and we just need more.”

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Tableau

SEPTEMBER 23, 2021

For example, Tableau data engineers want a single source of truth to help avoid creating inconsistencies in data sets, while line-of-business users are concerned with how to access the latest data for trusted analysis when they need it most. Data quality: Gone are the days of “data is data, and we just need more.”

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Alation

OCTOBER 18, 2022

Making this data visible in the data catalog will let data teams share their work, support re-use, and empower everyone to better understand and trust data. Data Transformation in the Modern Data Stack. Data engineering plays a critical role in distributing data to a wide audience.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

It promotes a disciplined approach to data modeling, making it easier to ensure data quality and consistency across the ML pipelines. Moreover, it provides a straightforward way to track data lineage, so we can foresee which datasets will be affected by newly introduced changes.

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Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

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Alation

FEBRUARY 17, 2023

Reichental describes data governance as the overarching layer that empowers people to manage data well ; as such, it is focused on roles & responsibilities, policies, definitions, metrics, and the lifecycle of the data. In this way, data governance is the business or process side. Communication is essential.

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Alation

AUGUST 30, 2022

With Alation Anywhere launching in beta, we will meet people where they are, helping to deliver context and trustworthiness of data, from and across the modern data stack, starting with Tableau. With this integration, Alation descriptions and data quality flags of warnings and deprecations will propagate to Tableau.

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Alation

AUGUST 11, 2022

Data mesh says architectures should be decentralized because there are inherent problems with centralized architectures. For example, when we centralize, all the focus goes on the data engineers. But there are only so many data engineers available in the market today; there’s a big skills shortage.

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Alation

JANUARY 13, 2022

Data mesh forgoes technology edicts and instead argues for “decentralized data ownership” and the need to treat “data as a product”. Gartner on Data Fabric. Moreover, data catalogs play a central role in both data fabric and data mesh. We’ll dig into this definition in a bit. Design concept.

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Alation

FEBRUARY 14, 2023

At first, “governance progress” metrics could be utilised, such as: What % of tables have a data steward assigned? What % of tables have a definition? What % of data is or isn’t assigned to a domain? What % of columns have data quality rules implemented? What are the benefits of reducing duplicate data?

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Tableau

AUGUST 21, 2023

Catalog Enhanced data trust, visibility, and discoverability Tableau Catalog automatically catalogs all your data assets and sources into one central list and provides metadata in context for fast data discovery. Included with Data Management.

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Pickl AI

JUNE 7, 2024

Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Introduction In today’s business landscape, data integration is vital.

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DagsHub

JANUARY 6, 2025

  The next plausible question that you should be asking is, what one must define in the labeling guidelines so that the resultant annotations can be of good quality. This clear definition reduces the issues like subjectivity as each annotator will follow the same set of instructions and the produced annotations will be uniform. 

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Pickl AI

MAY 30, 2024

This crucial stage involves data cleaning, normalisation, transformation, and integration. By addressing issues like missing values, duplicates, and inconsistencies, preprocessing enhances data quality and reliability for subsequent analysis. Data Cleaning Data cleaning is crucial for data integrity.

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Alation

OCTOBER 12, 2021

One may define enterprise data analytics as the ability to find, understand, analyze, and trust data to drive strategy and decision-making. Enterprise data analytics integrates data, business, and analytics disciplines, including: Data management. Data engineering. Evaluate and monitor data quality.

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Pickl AI

DECEMBER 4, 2023

Data governance and security Like a fortress protecting its treasures, data governance, and security form the stronghold of practical Data Intelligence. Think of data governance as the rules and regulations governing the kingdom of information. It ensures data quality , integrity, and compliance.

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Alation

MAY 31, 2023

American Family Insurance: Governance by Design – Not as an Afterthought Who: Anil Kumar Kunden , Information Standards, Governance and Quality Specialist at AmFam Group When: Wednesday, June 7, at 2:45 PM Why attend: Learn how to automate and accelerate data pipeline creation and maintenance with data governance, AKA metadata normalization.

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DagsHub

OCTOBER 23, 2024

Here are some challenges you might face while managing unstructured data: Storage consumption: Unstructured data can consume a large volume of storage. For instance, if you are working with several high-definition videos, storing them would take a lot of storage space, which could be costly.

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phData

MAY 6, 2024

Data Quality Next, dive into the details of your data. Now, a single customer might use multiple emails or phone numbers, but matching in this way provides a precise definition that could significantly reduce or even eliminate the risk of accidentally associating the actions of multiple customers with one identity.

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Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream. That is definitely a problem.

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Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream. That is definitely a problem.

Supervised Learning 52

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Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream. That is definitely a problem.

Supervised Learning 52

Supervised Learning AI AI ML 52

DagsHub

JANUARY 27, 2025

Unsupervised learning has shown a big potential in large language models but high-quality labelled data remains the gold standard for AI systems to be accurate and aligned with human language and understanding.   LabelBox LabelBox is an AI-powered data engine platform that supports text annotation along with other data types.

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phData

JUNE 7, 2023

The most critical and impactful step you can take towards enterprise AI today is ensuring you have a solid data foundation built on the modern data stack with mature operational pipelines, including all your most critical operational data. This often involves software engineering, data engineering, and system design skills.

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phData

JULY 18, 2023

Embracing Automation: As already mentioned, the abstraction that the Modern Data Stack provides means that most of the infrastructure maintenance that would typically be required to maintain an enterprise data platform is automated away. Testing: Data engineering should be treated as a form of software engineering.

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The MLOps Blog

MARCH 15, 2023

For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial. This includes data quality, privacy, and compliance. Two Data Scientists: Responsible for setting up the ML models training and experimentation pipelines.

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phData

AUGUST 17, 2023

In the rapidly evolving landscape of data engineering, Snowflake Data Cloud has emerged as a leading cloud-based data warehousing solution, providing powerful capabilities for storing, processing, and analyzing vast amounts of data.

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Precisely

SEPTEMBER 26, 2024

Key Takeaways: Trusted AI requires data integrity. For AI-ready data, focus on comprehensive data integration, data quality and governance, and data enrichment. Building data literacy across your organization empowers teams to make better use of AI tools. The impact?

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AWS Machine Learning Blog

JANUARY 26, 2024

Organizational resiliency draws on and extends the definition of resiliency in the AWS Well-Architected Framework to include and prepare for the ability of an organization to recover from disruptions.

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phData

SEPTEMBER 27, 2024

Data Quality Management : Persistent staging provides a clear demarcation between raw and processed customer data. This makes it easier to implement and manage data quality processes, ensuring your marketing efforts are based on clean, reliable data. All this raw data goes into your persistent stage.

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The MLOps Blog

AUGUST 11, 2023

1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g., Furthermore, Netflix’s Maestro platform uses DAGs to orchestrate and manage workflows within machine learning/data pipelines. Scikit-learn, Feature Tools) 4 Model Training (e.g.,

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phData

NOVEMBER 1, 2024

It serves as a vital protective measure, ensuring proper data access while managing risks like data breaches and unauthorized use. Strong data governance also lays the foundation for better model performance, cost efficiency, and improved data quality, which directly contributes to regulatory compliance and more secure AI systems.

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