Machine Learning

Machine Learning | News, how-tos, features, reviews, and videos

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Understanding Azure HPC

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Review: Nvidia AI Enterprise shines on VMware

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New Tech Forum

How no-code, reusable AI will bridge the AI divide

The ability to reuse pre-built AI solutions and components, and customize them without coding, will finally allow AI solutions to be created without requiring scarce AI talent or costly IT resources.

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How digital twins improve physical systems

This emerging technology merges the physical and digital worlds in ways that have the potential to transform many industries.

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New Relic expands enterprise full-stack observability to include MLOps

New Relic has updated its full-stack analysis and observability platform, One, to allow data scientists and machine learning engineers to import data from different systems, monitor ML application performance and retrain models.

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New Tech Forum

Best practices for developing governable AI

Focus on these engineering best practices to build high-quality models that can be governed effectively.

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Review: Document parsing in AWS, Azure, and Google Cloud

Amazon Textract, Azure Form Recognizer, and Google Document AI can parse your unstructured documents and produce structured information for all kinds of digital transformation use cases.

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AWS updates databases, AI and serverless offerings at re:Invent

At re:Invent this week, AWS unveiled updates to database, machine learning and serverless offerings designed to reduce complexity and cost.

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AWS brings no-code to Amazon SageMaker machine learning

Amazon SageMaker Canvas provides a visual interface for preparing data and training models, no programming or machine learning expertise required.

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UNESCO launches global standard for AI ethics

Standard aims to provide AI with a strong ethical basis that will not only protect but also promote human rights and human dignity.

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How the public clouds are innovating on AI

Companies without the resources to develop in-house machine learning models are turning to the big cloud providers.

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Why AI investments fail to deliver

The success or failure of AI initiatives has more to do with people than with technology. If you want to put AI into practice in a way that improves business outcomes, you must avoid these 6 mistakes.

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How to automate QA testing of SaaS and low-code applications

IT can still test without access to underlying source code. Use agile acceptance testing, address the business logic, and adopt test platforms with machine learning.

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New Tech Forum

The high cost of data science toil

Data science toil saps agility and prevents organizations from scaling data science efforts efficiently and sustainably. Here’s how to avoid it.

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Google Cloud tools aim to ease machine-learning, cross-cloud analytics

Google Cloud Platform (GCP) is offering new dedicated data and machine learning (ML) tools designed to clear up data inefficiencies and ease application development for enterprises.

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3 ways to use data, analytics, and machine learning in test automation

Better testing means better software. Using NLP, test data generation, and optimized testing can quickly improve applications.

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The best open source software of 2021

InfoWorld’s 2021 Bossie Awards recognize the year’s best open source software for software development, devops, data analytics, and machine learning.

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Explainable AI explained

Someday machine learning models may be more ‘glass box‘ than black box. Until then, explainability tools and techniques can help us understand how a black box model makes its decisions.

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Review: Google Cloud Vertex AI irons out ML platform wrinkles

Vertex AI greatly improves the integration of Google Cloud’s AI/ML platform and AutoML services, combining a new unified API with very good modeling capabilities.

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What is vector search? Better search through AI

Vector similarity search uses machine learning to translate the similarity of text, images, or audio into a vector space, making search faster, more accurate, and more scalable.

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