AI News

NOVEMBER 21, 2023

Addressing this gap will require a multi-faceted approach including grappling with issues related to data quality and ensuring that AI systems are built on reliable, unbiased, and representative datasets. Companies have struggled with data quality and data hygiene.

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AI News

MAY 3, 2024

. “Our AI engineers built a prompt evaluation pipeline that seamlessly considers cost, processing time, semantic similarity, and the likelihood of hallucinations,” Ros explained. It’s obviously an ambitious goal, but it’s important to our employees and it’s important to our clients,” explained Ros.

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AI News

DECEMBER 19, 2023

Where [Kafka] falls down is in large-scale analytics,” explained Scott. While Kafka reliably transports high-volume data streams between applications and microservices, conducting complex analytical workloads directly on streaming data has historically been challenging.

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Unite.AI

NOVEMBER 20, 2024

Journalists do require some technical details, however, long-winded descriptions highlighting the complexity of your deep learning architecture or data quality will lead to you blending in with thousands of other tech-first firms. Tangible benefits are key.

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Unite.AI

MARCH 14, 2025

A McKinsey survey found that while one-quarter of respondents were concerned about accuracy, many struggled just as much with security, explainability, intellectual property (IP) management, and regulatory compliance. Select a use case with production in mind First and foremost, choose a use case with a clear path to production.

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Marktechpost

AUGUST 7, 2024

The Role of Explainable AI in In Vitro Diagnostics Under European Regulations: AI is increasingly critical in healthcare, especially in vitro diagnostics (IVD). These AI systems must perform accurately and provide explainable results to comply with regulatory requirements.

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AI News

SEPTEMBER 24, 2024

This wouldn’t be possible without forward-thinking customers like SSE Renewables who are willing to go on the journey with us,” explained Allen. See also: Hugging Face is launching an open robotics project Want to learn more about AI and big data from industry leaders?

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Flipboard

DECEMBER 27, 2023

Presented by SQream The challenges of AI compound as it hurtles forward: demands of data preparation, large data sets and data quality, the time sink of long-running queries, batch processes and more. In this VB Spotlight, William Benton, principal product architect at NVIDIA, and others explain how …

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Unite.AI

SEPTEMBER 23, 2024

From technical limitations to data quality and ethical concerns, it’s clear that the journey ahead is still full of obstacles. In the long term, the true potential of AI in drug discovery will likely depend on advancements in explainable AI, data infrastructure, and industry-wide collaboration.

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Towards AI

OCTOBER 31, 2024

It explains different embedding techniques, including Word2Vec, GloVe, BERT, and more, and details how to utilize embedding models from providers such as OpenAI, HuggingFace, and Gemini within LangChain. It covers key considerations like balancing data quality versus quantity, ensuring data diversity, and selecting the right tuning method.

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Unite.AI

FEBRUARY 17, 2025

Data Quality, Quantity, and Integration: As AI models require large amounts of high-quality data to perform effectively, enterprises must implement robust data collection and processing pipelines to ensure the AI is receiving current, accurate, relevant data.

Automation 147

Automation Responsible AI Generative AI AI Modeling 147

Unite.AI

JULY 19, 2024

It is designed to automatically detect and fix data issues that can negatively impact the performance of machine learning models, including language models prone to hallucinations. They can also identify data quality issues in text, image, and tabular datasets. Automatically detects mislabeled data. Enhances data quality.

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LLM AI AI Data Quality 279

IBM Journey to AI blog

OCTOBER 12, 2023

A single point of entry eliminates the need to duplicate sensitive data for various purposes or move critical data to a less secure (and possibly non-compliant) environment. Explainable AI — Explainable AI is achieved when an organization can confidently and clearly state what data an AI model used to perform its tasks.

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Pickl AI

FEBRUARY 17, 2025

Introduction: The Reality of Machine Learning Consider a healthcare organisation that implemented a Machine Learning model to predict patient outcomes based on historical data. However, once deployed in a real-world setting, its performance plummeted due to data quality issues and unforeseen biases.

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Towards AI

FEBRUARY 11, 2025

Beyond Scale: Data Quality for AI Infrastructure The trajectory of AI over the past decade has been driven largely by the scale of data available for training and the ability to process it with increasingly powerful compute & experimental models. Author(s): Richie Bachala Originally published on Towards AI.

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Flipboard

FEBRUARY 19, 2025

But first, we explain technical architecture that makes Alfred such a powerful tool for Andurils workforce. AWS GovCloud (US) foundation At the core of Alfreds architecture is AWS GovCloud (US), a specialized cloud environment designed to handle sensitive data and meet the strict compliance requirements of government agencies.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

For now, we consider eight key dimensions of responsible AI: Fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. This includes handling unexpected inputs, adversarial manipulations, and varying data quality without significant degradation in performance.

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Unite.AI

JANUARY 9, 2024

Building a strong data foundation. Building a robust data foundation is critical, as the underlying data model with proper metadata, data quality, and governance is key to enabling AI to achieve peak efficiencies. Proper governance. Humans must validate AI’s output to ensure it is safe.

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IBM Journey to AI blog

JULY 9, 2024

Strong data governance is foundational to robust artificial intelligence (AI) governance. Companies developing or deploying responsible AI must start with strong data governance to prepare for current or upcoming regulations and to create AI that is explainable, transparent and fair.

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Metadata Data Quality Responsible AI Artificial Intelligence 146

AssemblyAI

OCTOBER 31, 2024

This level of accuracy means sales teams can: Track competitive opportunities accurately Size deals based on precise user counts Prioritize opportunities based on accurate timelines Customer support that gets it right the first time During a support call a customer explains: "I've been trying to activate my iPhone 15 Pro.

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Automation Data Quality Explainability AI 147

IBM Journey to AI blog

APRIL 10, 2023

That is, it should support both sound data governance —such as allowing access only by authorized processes and stakeholders—and provide oversight into the use and trustworthiness of AI through transparency and explainability.

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Towards AI

NOVEMBER 6, 2024

This visualization helps in identifying data quality issues and planning imputation or cleanup strategies for meaningful analysis. Data Preprocessing To start, we preprocess the data by: Handling Missing Values: We handle missing values as necessary, either by imputing or ignoring them as appropriate.

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IBM Journey to AI blog

MAY 8, 2023

Crucially, the insurance sector is a financially regulated industry where the transparency, explainability and auditability of algorithms is of key importance to the regulator. Usage risk—inaccuracy The performance of an AI system heavily depends on the data from which it learns.

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Unite.AI

JANUARY 20, 2025

Headquartered in Oregon, the company is at the forefront of transforming how healthcare data is shared, monetized, and applied, enabling secure collaboration between data custodians and data consumers. Can you explain how datma.FED utilizes AI to revolutionize healthcare data sharing and analysis?

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AWS Machine Learning Blog

FEBRUARY 27, 2024

Monitoring – Continuous surveillance completes checks for drifts related to data quality, model quality, and feature attribution. Workflow A corresponds to preprocessing, data quality and feature attribution drift checks, inference, and postprocessing. Workflow B corresponds to model quality drift checks.

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IBM Journey to AI blog

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Metadata Data Quality Data Discovery Data Scientist 130

Unite.AI

SEPTEMBER 30, 2024

As AI takes center stage, AI quality assurance can empower teams to deliver higher-quality software faster. This article explains how AI in quality assurance streamlines software testing while improving product performance. What is AI-powered Quality Assurance?

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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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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.

Data Quality 52

Data Quality Automation Data Scientist ML 52

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.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Unite.AI

OCTOBER 22, 2024

This shift marks a pivotal moment in the industry, with AI set to revolutionize various aspects of QE, from test automation to data quality management. Data Quality: The Foundation for AI-Driven Testing As organizations become more reliant on data-driven decision-making, the quality of their data takes on heightened importance.

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PyImageSearch

JANUARY 6, 2025

In quality control, an outlier could indicate a defect in a manufacturing process. By understanding and identifying outliers, we can improve data quality, make better decisions, and gain deeper insights into the underlying patterns of the data. finance, healthcare, and quality control).

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Python Explainability Computer Vision Deep Learning 104

Snorkel AI

MARCH 4, 2025

Data quality dependency: Success depends heavily on having high-quality preference data. When choosing an alignment method, organizations must weigh trade-offs like complexity, computational cost, and data quality requirements. Learn how to get more value from your PDF documents! Sign up here!

LLM 52

LLM Large Language Models Data Quality Prompt Engineering 52

AWS Machine Learning Blog

OCTOBER 9, 2024

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. You can review the recommendations and augment rules from over 25 included data quality rules.

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The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Your data team can manage large-scale, structured, and unstructured data with high performance and durability. Data monitoring tools help monitor the quality of the data.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Pickl AI

OCTOBER 17, 2024

As organisations increasingly rely on data-driven insights, effective ETL processes ensure data integrity and quality, enabling informed decision-making. ETL facilitates Data Analytics by transforming raw data into meaningful insights, empowering businesses to uncover trends, track performance, and make strategic decisions.

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Heartbeat

JUNE 12, 2023

If the test or validation data distribution has too much deviance from the training data distribution, then we must go for retraining since it is a sign of population drift. Model Interpretability and Explainability Model interpretability and explainability describe how a machine learning model arrives at its predictions or decisions.

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Machine Learning Data Drift Explainability Data Quality 98