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.

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phData

DECEMBER 29, 2022

This automation includes things like SQL translation during a data platform migration (SQLMorph), making changes to your Snowflake information architecture (Tram), and checking for parity and data quality between platforms (Data Source Automation). The post phData Toolkit December 2022 Update appeared first on phData.

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Pickl AI

NOVEMBER 4, 2024

As organisations increasingly rely on data to drive decision-making, understanding the fundamentals of Data Engineering becomes essential. The global Big Data and Data Engineering Services market, valued at USD 51,761.6 million in 2022, is projected to grow at a CAGR of 18.15% , reaching USD 140,808.0

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IBM Journey to AI blog

OCTOBER 12, 2023

Securing AI models and their access to data While AI models need flexibility to access data across a hybrid infrastructure, they also need safeguarding from tampering (unintentional or otherwise) and, especially, protected access to data. Learn more about IBM watsonx 1.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

Indeed, this perspective characterized much of the coverage around generative AI as the release of ChatGPT and other tools mainstreamed the technology in 2022, with some analysts predicting that we were on the brink of a revolution that would reshape the future of work. In this context, data quality often outweighs quantity.

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Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

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phData

MARCH 1, 2023

This tool ​​collects, compares, analyzes, and acts on data source metadata and metrics. This allows you to perform tasks such as ensuring data quality against data sources (once or over time), compare data metrics and metadata across environments, and create/manage data pipelines for all your tables and views.

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The MLOps Blog

MARCH 21, 2023

” — Isaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 Monitoring Monitoring is an essential DevOps practice, and MLOps should be no different. It is very easy for a data scientist to use Python or R and create machine learning models without input from anyone else in the business operation. Model registry.

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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. All right, so let’s set the stage first with some examples: a focus on data quality leads to better ML-powered products. This is not very obvious if you just focus on the math.

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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. All right, so let’s set the stage first with some examples: a focus on data quality leads to better ML-powered products. This is not very obvious if you just focus on the math.

Machine Learning 52

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phData

JUNE 7, 2023

AI Today ChatGPT was released on November 30, 2022, and has since been coined the fastest-growing app in internet history. You don’t need massive data sets because “data quality scales better than data size.”

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The MLOps Blog

MAY 9, 2023

The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Data ingestion (extraction and versioning). Data validation (writing tests to check for data quality). Data preprocessing. Model performance analysis and evaluation.

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The MLOps Blog

AUGUST 11, 2023

Internally within Netflix’s engineering team, Meson was built to manage, orchestrate, schedule, and execute workflows within ML/Data pipelines. Meson managed the lifecycle of ML pipelines, providing functionality such as recommendations and content analysis, and leveraged the Single Leader Architecture. 2022, January 18).

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