Unite.AI

NOVEMBER 11, 2024

Jumio has made substantial investments in both time and financial resources to navigate the complex and ever-changing landscape of AI regulations. A cornerstone of this strategy is our commitment to data integrity and diversity, evident in our significant investment in privacy and compliance measures and dataset curation.

Machine Learning 162

Machine Learning AI AI Natural Language Processing 162

Unite.AI

JULY 19, 2024

This extensive knowledge base allows for robust AI validation that makes Pythia ideal for situations where accuracy is important. Here are some key features of Pythia: With its real-time hallucination detection capabilities, Pythia enables AI models to make reliable decisions. Automatically detects mislabeled data.

LLM 279

LLM AI AI Data Quality 279

IBM Journey to AI blog

MAY 15, 2024

Business leaders risk compromising their competitive edge if they do not proactively implement generative AI (gen AI). However, businesses scaling AI face entry barriers. Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models.

ETL 213

ETL Data Integration Data Quality Generative AI 213

Pickl AI

JULY 24, 2024

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning?

Data Quality 52

Data Quality Machine Learning Automation Data Integration 52

Unite.AI

DECEMBER 6, 2024

At the fundamental level, your data quality is your AI differentiator. The accuracy of, and particularly the generated responses of, a RAG application will always be subject to the quality of data that is being used to train and augment the output.

AI 130

AI AI Data Platform LLM 130

Towards AI

FEBRUARY 11, 2025

Author(s): Richie Bachala Originally published on Towards AI. 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.

Data Quality 54

Data Quality Neural Network ETL Computer Vision 54

Unite.AI

MAY 23, 2024

Taking stock of which data the company has available and identifying any blind spots can help build out data-gathering initiatives. From there, a brand will need to set data governance rules and implement frameworks for data quality assurance, privacy compliance, and security.

Machine Learning 269

Machine Learning AI AI AI Engineer 269

Unite.AI

JUNE 12, 2024

While cinematic portrayals of AI often evoke fears of uncontrollable, malevolent machines, the reality in IT is more nuanced. Professionals are evaluating AI's impact on security , data integrity, and decision-making processes to determine if AI will be a friend or foe in achieving their organizational goals.

Data Quality 147

Data Quality Automation AI AI 147

IBM Journey to AI blog

OCTOBER 12, 2023

The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly. Here’s what’s involved in making that happen.

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Data Scientist Metadata Explainability Responsible AI 234

IBM Journey to AI blog

AUGUST 23, 2023

The recent success of artificial intelligence based large language models has pushed the market to think more ambitiously about how AI could transform many enterprise processes. However, consumers and regulators have also become increasingly concerned with the safety of both their data and the AI models themselves.

Large Language Models 167

Large Language Models Data Discovery LLM AI 167

Ehud Reiter

JANUARY 22, 2025

Unfortunately, digital interventions (including AI) almost always lose people over time; keeping people engaged and using a system for ten years is a huge challenge. Data : High-quality large medical data sets are very hard to get. Deployment : It is very hard to deploy AI on a large scale in healthcare.

AI 118

AI AI Automation Data Quality 118

IBM Journey to AI blog

JANUARY 17, 2023

Regardless of size, industry or geographical location, the sprawl of data across disparate environments, increase in velocity of data and the explosion of data volumes has resulted in complex data infrastructures for most enterprises. The result is more useful data for decision-making, less hassle and better compliance.

Data Science 146

Data Science Data Integration Automation Metadata 146

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.

Metadata 130

Metadata Data Quality Data Discovery Data Scientist 130

Pickl AI

MARCH 7, 2025

Key Takeaways The 4 Vs of Big Data define how businesses handle massive amounts of information. Volume, Velocity, Variety, and Veracity drive insights, AI models, and decision-making. Real-time data processing helps businesses react faster to market trends and risks. Data quality affects decision-making.

Big Data 52

Big Data Machine Learning Data Quality Data Science 52

The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Marktechpost

AUGUST 10, 2024

High-Risk AI: These include critical applications like medical AI tools or recruitment software. They must meet strict standards for accuracy, security, and data quality, with ongoing human oversight. These entities will work together to ensure regulatory uniformity and solve new difficulties in AI governance.

Artificial Intelligence 132

Artificial Intelligence Artificial Intelligence AI AI 132

Marktechpost

JUNE 16, 2024

The feature eliminates the need for data teams to manually manage maintenance operations, such as scheduling jobs, diagnosing failures, and managing infrastructure. Anker: The data engineering team at Anker reported a 2x improvement in query performance and 50% savings in storage costs after enabling Predictive Optimization.

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Data Ingestion Python Automation Data Scientist 116

Marktechpost

DECEMBER 3, 2024

Whether users need data from structured Excel spreadsheets or more unstructured formats like PowerPoint presentations, MegaParse provides efficient parsing while maintaining data integrity. The significance of MegaParse lies not just in its versatility but also in its focus on information integrity and efficiency.

LLM 102

LLM AI Tools Large Language Models Data Ingestion 102

Defined.ai blog

SEPTEMBER 5, 2023

Similarly, by feeding AI models with retail sales data, you’re essentially ‘training’ it to be more intuitive, efficient, and predictive. Together, these resources ensure a tailored, customer-centric experience for AI-driven systems. Tableau : Visualization plays a crucial role in understanding data.

AI Modeling 52

AI Modeling Machine Learning AI AI 52

AWS Machine Learning Blog

JUNE 3, 2024

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Custom Spark commands can also expand the over 300 built-in data transformations. Other analyses are also available to help you visualize and understand your data.

Generative AI 123

Generative AI Categorization Auto-complete Auto-classification 123

Unite.AI

JUNE 17, 2024

This involves defining clear policies and procedures for how data is collected, stored, accessed, and used within the organization. It should include guidelines for data quality, data integration, and data security, as well as defining roles and responsibilities for data management.

Automation 147

Automation Artificial Intelligence Artificial Intelligence Generative AI 147

Pickl AI

JUNE 23, 2023

The Importance of Data-Centric Architecture Data-centric architecture is an approach that places data at the core of AI systems. At the same time, it emphasizes the collection, storage, and processing of high-quality data to drive accurate and reliable AI models. How Does Data-Centric AI Work?

Continuous Learning 52

Continuous Learning Data Quality AI AI 52

Pickl AI

OCTOBER 2, 2024

Summary: Artificial Intelligence (AI) is revolutionising Genomic Analysis by enhancing accuracy, efficiency, and data integration. Despite challenges like data quality and ethical concerns, AI’s potential in genomics continues to grow, shaping the future of healthcare.

Neural Network 52

Neural Network Deep Learning Machine Learning Artificial Intelligence 52

Defined.ai blog

AUGUST 10, 2023

It can quickly process large amounts of data, precisely identifying patterns and insights humans might overlook. Businesses can transform raw numbers into actionable insights by applying AI. For instance, an AI model can predict future sales based on past data, helping businesses plan better.

Business Intelligence 52

Business Intelligence Data Analysis Neural Network Data Quality 52

AWS Machine Learning Blog

NOVEMBER 15, 2023

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.

Data Ingestion 117

Data Ingestion Metadata NLP Data Scientist 117

Defined.ai blog

SEPTEMBER 7, 2023

Healthcare datasets serve as the foundational blocks on which various AI solutions, such as diagnostic tools, treatment prediction algorithms, patient monitoring systems, and personalized medicine models, are built. Consider them the encyclopedias AI algorithms use to gain wisdom and offer actionable insights.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Flipboard

FEBRUARY 12, 2025

This will only worsen, and companies must learn to adapt their models to unique, content-rich data sources. Model improvements in the future wont come from brute force and more data; they will come from better data quality, more context, and the refinement of underlying techniques.

AI 65

AI AI Robotics Automation 65

Heartbeat

OCTOBER 12, 2023

Challenges in Multi-Modal Learning Multi-modal learning, the convergence of multiple data modalities (e.g., Heterogeneous Data Integration : Combining data from different modalities that differ in format, scale, and dimensionality requires careful integration.

Large Language Models 59

Large Language Models Neural Network Convolutional Neural Networks Artificial Intelligence 59

Pickl AI

JUNE 6, 2024

Data scrubbing is often used interchangeably but there’s a subtle difference. Cleaning is broader, improving data quality. This is a more intensive technique within data cleaning, focusing on identifying and correcting errors. Data scrubbing is a powerful tool within this cleaning service.

Machine Learning 40

Machine Learning Algorithm Business Intelligence Data Quality 40

Marktechpost

JULY 17, 2023

It is impossible to completely substitute accurate data because precise, accurate data are still needed to generate practical synthetic examples of the information. How Important Is Synthetic Data? AI models are typically more accurate when they have more varied training data.

Machine Learning 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

Unite.AI

JANUARY 1, 2025

Their solutions streamline trial processes, ensuring compliance and retention through integrated support and training for patients and sponsors alike. Clario has integrated over 30 AI models across various stages of clinical trials. Our AI models are delivering accurate overreads while the patient is still at the site.

Algorithm 162

Algorithm AI Modeling AI Tools Data Quality 162

Pickl AI

JULY 23, 2023

Examples of datasets include a spreadsheet containing information about customer demographics, a database of medical records, or a collection of images for training an AI model. The Role of Data in the Digital Age Data plays a pivotal role in shaping the technological landscape in today’s interconnected world.

Data Analysis 52

Data Analysis Explainability Algorithm Machine Learning 52

Unite.AI

NOVEMBER 13, 2024

Large Language Models (LLMs) , advanced AI models capable of understanding and generating human language, are changing this domain. Users can instruct the model to perform tasks like “ normalize dates to MM/DD/YYYY ” or “ fill missing values with the median.”

Large Language Models 264

Large Language Models Data Analysis AI AI 264

Unite.AI

JULY 5, 2024

Robust data management is another critical element. Establishing strong information governance frameworks ensures data quality, security and regulatory compliance. Lastly, predictive analytics powered by Gen AI have groundbreaking potential. Transparent, explainable AI models are necessary for informed decision-making.

Data Integration 147

Data Integration Data Quality Algorithm AI Modeling 147

AI News

OCTOBER 17, 2024

Despite the surge in AI investments, business leaders are acutely aware of the additional risk exposure that AI brings to their organisations. Data integrity and security emerged as the biggest deterrents to implementing new AI solutions.

Big Data 291

Big Data AI AI Data Quality 291

Unite.AI

JANUARY 22, 2025

Scalability: GenAI LLMs can be data- and compute-intensive, so the underlying data infrastructure needs to be able to scale to meet the demands of these models. Another key trend is the increased application of accelerated technologies for AI inferencing, particularly with companies like Nvidia.

Auto-complete 147

Auto-complete Automation Machine Learning Data Integration 147

TransOrg Analytics

NOVEMBER 25, 2024

This blog delves into how generative AI can be used for commodity price forecasting, the methodologies it employs, the benefits it offers, and the challenges it presents. a geopolitical crisis or a supply chain disruption), these models can generate potential price trajectories, allowing stakeholders to plan for various contingencies.

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Generative AI Neural Network Data Quality AI 52