CIO
DECEMBER 20, 2024
Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Curate the data.
CIO
NOVEMBER 19, 2024
The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data. The challenges of integrating data with AI workflows When I speak with our customers, the challenges they talk about involve integrating their data and their enterprise AI workflows.
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CIO
NOVEMBER 27, 2024
From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.
CIO
NOVEMBER 4, 2024
Most AI workloads are deployed in private cloud or on-premises environments, driven by data locality and compliance needs. AI a primary driver in IT modernization and data mobility AI’s demand for data requires businesses to have a secure and accessible data strategy. Cost, by comparison, ranks a distant 10th.
Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage
He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Dzone - DevOps
MARCH 28, 2025
This blog post discusses an end-to-end ML pipeline on AWS SageMaker that leverages serverless computing, event-trigger-based data processing, and external API integrations. The architecture downstream ensures scalability, cost efficiency, and real-time access to applications.
AWS Machine Learning - AI
OCTOBER 29, 2024
Batch inference in Amazon Bedrock efficiently processes large volumes of data using foundation models (FMs) when real-time results aren’t necessary. To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB.
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Apache Cassandra is an open-source distributed database that boasts an architecture that delivers high scalability, near 100% availability, and powerful read-and-write performance required for many data-heavy use cases.
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There’s no getting around it: selecting the right foundational data-layer components is crucial for long-term application success.
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Understanding your data security needs is tough enough, but what can be even more difficult is choosing the right software to fit your company. Flexibility and scalability. Fortunately, there is a solution. Key management system. User authentication and advanced security factors. Enterprise features. Download the checklist today!
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Learn 8 strategies to use data and technology to create scalable yet personalized ABM programs. Evolve beyond account-based marketing and create a hyper-personalized approach that considers stakeholders as individuals. Download the guide.
Advertiser: Data Robot
Machine Learning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machine learning lifecycle through automation and scalability. How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects?
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Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.
Speaker: Sam Owens, Product Management Lead, Namely Platform
To make things tougher, they needed something flexible, scalable and capable of serving different user types. Sam and Jessica will walk through how they: Discovered the data and analytics needs of their users, including understanding what data sources they needed access to. Balanced competing priorities and managed expectations.
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In today’s ambitious business environment, customers want access to an application’s data with the ability to interact with the data in a way that allows them to derive business value. After all, customers rely on your application to help them understand the data that it holds, especially in our increasingly data-savvy world.
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