Unite.AI

FEBRUARY 7, 2024

Machine learning (ML) is a powerful technology that can solve complex problems and deliver customer value. However, ML models are challenging to develop and deploy. MLOps are practices that automate and simplify ML workflows and deployments. MLOps make ML models faster, safer, and more reliable in production.

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Machine Learning Large Language Models LLM BERT 288

Machine Learning Research at Apple

JANUARY 28, 2024

Machine learning (ML) models are fundamentally shaped by data, and building inclusive ML systems requires significant considerations around how to design representative datasets.

Machine Learning 105

Machine Learning ML Data Quality 105

AWS Machine Learning Blog

NOVEMBER 26, 2024

With the increasing use of large models, requiring a large number of accelerated compute instances, observability plays a critical role in ML operations, empowering you to improve performance, diagnose and fix failures, and optimize resource utilization. This data makes sure models are being trained smoothly and reliably.

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LLM ML Large Language Models Deep Learning 98

AI News

SEPTEMBER 27, 2024

First is clear alignment of the data strategy with the business goals, making sure the technology teams are working on what matters the most to the business. Second, is data quality and accessibility, the quality of the data is critical. Poor data quality will lead to inaccurate insights.

AI Strategy 304

AI Strategy Automation Data Quality Big Data 304

Unite.AI

OCTOBER 30, 2023

When unstructured data surfaces during AI development, the DevOps process plays a crucial role in data cleansing, ultimately enhancing the overall model quality. Improving AI quality: AI system effectiveness hinges on data quality. Poor data can distort AI responses.

DevOps 305

DevOps Software Development Automation Artificial Intelligence 305

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out her talk, “ Power trusted AI/ML Outcomes with Data Integrity ,” there! Due to the tsunami of data available to organizations today, artificial intelligence (AI) and machine learning (ML) are increasingly important to businesses seeking competitive advantage through digital transformation.

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Data Integration ML ESG Big Data 98

Google Research AI blog

MARCH 30, 2023

Posted by Peter Mattson, Senior Staff Engineer, ML Performance, and Praveen Paritosh, Senior Research Scientist, Google Research, Brain Team Machine learning (ML) offers tremendous potential, from diagnosing cancer to engineering safe self-driving cars to amplifying human productivity. Each step can introduce issues and biases.

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Marktechpost

APRIL 11, 2024

Researchers from Meta developed a machine learning (ML)-based approach to address the challenges of optimizing bandwidth estimation (BWE) and congestion control for real-time communication (RTC) across Meta’s family of apps. Meta’s ML-based approach involves two main components: offline ML model learning and parameter tuning.

Machine Learning 105

Machine Learning ML Categorization Data Quality 105

Marktechpost

JULY 23, 2024

AI and ML in Untargeted Metabolomics and Exposomics: Metabolomics employs a high-throughput approach to measure a variety of metabolites and small molecules in biological samples, providing crucial insights into human health and disease. The HRMS generates data in three dimensions: mass-to-charge ratio, retention time, and abundance.

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Machine Learning ML Algorithm Data Extraction 103

AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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ML Machine Learning Data Science Data Drift 106

AWS Machine Learning Blog

DECEMBER 4, 2024

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

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ML Machine Learning Generative AI AI 86

Marktechpost

SEPTEMBER 30, 2024

Unlike large-scale models like ESM2 and ProGen2, AMPLIFY focuses on improving data quality rather than model size, achieving superior performance with 43 times fewer parameters. The team evaluated three strategies—data quality, quantity, and training steps—finding that improving data quality alone can create state-of-the-art models.

Data Quality 60

Data Quality Neural Network Natural Language Processing Large Language Models 60

AWS Machine Learning Blog

OCTOBER 28, 2024

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. SageMaker Model Monitor monitors the quality of SageMaker ML models in production.

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ML Metadata Data Scientist Machine Learning 98

Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. Machine learning(ML) is evolving at a very fast pace. Machine learning(ML) is evolving at a very fast pace.

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Machine Learning ML Neural Network Algorithm 109

Marktechpost

MAY 7, 2024

The burgeoning expansion of the data landscape, propelled by the Internet of Things (IoT), presents a pressing challenge: ensuring data quality amidst the deluge of information. However, the quality of that data is paramount, especially given the escalating reliance on Machine Learning (ML) across various industries.

Large Language Models 132

Large Language Models Automation Data Quality ML 132

AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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ML Machine Learning Auto-complete Auto-classification 87

Pickl AI

FEBRUARY 17, 2025

However, once deployed in a real-world setting, its performance plummeted due to data quality issues and unforeseen biases. This scenario highlights a common reality in the Machine Learning landscape: despite the hype surrounding ML capabilities, many projects fail to deliver expected results due to various challenges.

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Machine Learning Algorithm Explainability Data Quality 52

Unite.AI

DECEMBER 23, 2023

Some of the APM strategies employed for this include: Predictive Maintenance: By using modern AI/ML capabilities to analyze big data , this strategy can monitor an asset’s health and forecast maintenance. Root Cause Analysis (RCA): This strategy emphasizes understanding the root causes of asset failures in a structured manner.

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Data Quality Big Data Artificial Intelligence Artificial Intelligence 176

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

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Machine Learning DevOps Data Scientist Data Quality 129

AWS Machine Learning Blog

JANUARY 28, 2025

Furthermore, evaluation processes are important not only for LLMs, but are becoming essential for assessing prompt template quality, input data quality, and ultimately, the entire application stack. This allows you to keep track of your ML experiments. We specifically focus on SageMaker with MLflow.

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LLM Large Language Models ML Algorithm 94

Flipboard

JUNE 15, 2023

Read the full series here: Building the foundation for customer data quality. The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies is pushing the boundaries of what can be achieved in marketing, customer experience … This article is part of a VB special issue.

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Generative AI Data Quality Machine Learning Artificial Intelligence 123

IBM Journey to AI blog

SEPTEMBER 5, 2024

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

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Metadata Data Quality Auto-classification DevOps 188

Unite.AI

FEBRUARY 10, 2024

Data Quantity Multilingual models require a larger vocabulary to represent tokens in many languages than monolingual models, but many languages lack large-scale datasets. Data Quality Concerns Ensuring the accuracy and cultural appropriateness of multilingual LLM outputs across languages is a significant concern.

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LLM Large Language Models Data Quality ML 313

IBM Journey to AI blog

JULY 30, 2024

Data Scientists and AI experts: Historically we have seen Data Scientists build and choose traditional ML models for their use cases. Data Scientists will typically help with training, validating, and maintaining foundation models that are optimized for data tasks.

Generative AI 266

Generative AI Data Scientist Software Engineer AI 266

Towards AI

JUNE 25, 2024

This is the first one, where we look at some functions for data quality checks, which are the initial steps I take in EDA. 🧰 The dummy data While Spark is famous for its ability to work with big data, for demo purposes, I have created a small dataset with an obvious duplicate issue. Let’s get started.

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ML Machine Learning Big Data Data Quality 80

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

OCTOBER 17, 2023

By adopting technologies like artificial intelligence (AI) and machine learning (ML), companies can give a boost to their customer segmentation efforts. In those cases, a traditional approach run by humans can work better, especially if you mainly have qualitative data. Here’s a guide to help you accomplish that.

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Machine Learning AI AI Algorithm 272

Unite.AI

FEBRUARY 21, 2025

Akeneos Product Cloud solution has PIM, syndication, and supplier data manager capabilities, which allows retailers to have all their product data in one spot. While personalization is nothing new to brands, AI and ML technology allows brands to enter new levels of customer personalization to meet the high consumer expectations.

Natural Language Processing 144

Natural Language Processing NLP Categorization Algorithm 144

AWS Machine Learning Blog

AUGUST 4, 2023

If you are a returning user to SageMaker Studio, in order to ensure Salesforce Data Cloud is enabled, upgrade to the latest Jupyter and SageMaker Data Wrangler kernels. This completes the setup to enable data access from Salesforce Data Cloud to SageMaker Studio to build AI and machine learning (ML) models.

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ML Categorization AI AI 88

Marktechpost

MARCH 11, 2024

Their solution focuses on filtering image-text data, a novel approach that introduces a nuanced scoring system for data quality evaluation, offering a more refined assessment than its predecessors. The research introduces a comprehensive scoring system that evaluates the quality of image-text pairs across four distinct metrics.

Machine Learning 129

Machine Learning Data Quality Artificial Intelligence Artificial Intelligence 129

Flipboard

FEBRUARY 19, 2025

The retrieval component uses Amazon Kendra as the intelligent search service, offering natural language processing (NLP) capabilities, machine learning (ML) powered relevance ranking, and support for multiple data sources and formats. Focus should be placed on data quality through robust validation and consistent formatting.

Responsible AI 81

Responsible AI Natural Language Processing Explainability Large Language Models 81

IBM Journey to AI blog

DECEMBER 20, 2023

This calls for the organization to also make important decisions regarding data, talent and technology: A well-crafted strategy will provide a clear plan for managing, analyzing and leveraging data for AI initiatives. Identify potential partners and vendors Find companies in the AI and ML space that have worked within your industry.

AI Strategy 224

AI Strategy Artificial Intelligence Artificial Intelligence Machine Learning 224

Pickl AI

DECEMBER 3, 2024

Summary: Data preprocessing in Python is essential for transforming raw data into a clean, structured format suitable for analysis. It involves steps like handling missing values, normalizing data, and managing categorical features, ultimately enhancing model performance and ensuring data quality.

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Python ML Categorization Machine Learning 52

AWS Machine Learning Blog

OCTOBER 9, 2024

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. To learn more, see the documentation.

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Data Quality ML Machine Learning ETL 82

AWS Machine Learning Blog

NOVEMBER 7, 2024

SageMaker JumpStart is a machine learning (ML) hub that provides a wide range of publicly available and proprietary FMs from providers such as AI21 Labs, Cohere, Hugging Face, Meta, and Stability AI, which you can deploy to SageMaker endpoints in your own AWS account. It’s serverless so you don’t have to manage the infrastructure.

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Generative AI Machine Learning AI AI 105

AWS Machine Learning Blog

MARCH 18, 2025

Regulatory compliance By integrating the extracted insights and recommendations into clinical trial management systems and EHRs, this approach facilitates compliance with regulatory requirements for data capture, adverse event reporting, and trial monitoring. He helps customers implement big data, machine learning, and analytics solutions.

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LLM NLP Data Integration AI 102