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AI News Weekly - Issue #380: 63% of IT and security pros believe AI will improve corporate cybersecurity - Apr 11th 2024

AI Weekly

No legacy process is safe. And this is particularly true for accounts payable (AP) programs, where AI, coupled with advancements in deep learning, computer vision and natural language processing (NLP), is helping drive increased efficiency, accuracy and cost savings for businesses. techxplore.com Are deepfakes illegal?

Robotics 264
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Concept Drift vs Data Drift: How AI Can Beat the Change

Viso.ai

Two of the most important concepts underlying this area of study are concept drift vs data drift. In most cases, this necessitates updating the model to account for this “model drift” to preserve accuracy. An example of how data drift may occur is in the context of changing mobile usage patterns over time.

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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

Primary activities AIOps relies on big data-driven analytics , ML algorithms and other AI-driven techniques to continuously track and analyze ITOps data. The process includes activities such as anomaly detection, event correlation, predictive analytics, automated root cause analysis and natural language processing (NLP).

Big Data 266
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Five open-source AI tools to know

IBM Journey to AI blog

The readily available nature of open-source AI also raises security concerns; malicious actors could leverage the same tools to manipulate outcomes or create harmful content. Biased training data can lead to discriminatory outcomes, while data drift can render models ineffective and labeling errors can lead to unreliable models.

AI Tools 207
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AI Development Lifecycle Learnings of What Changed with LLMs

ODSC - Open Data Science

The Evolving AI Development Lifecycle Despite the revolutionary capabilities of LLMs, the core development lifecycle established by traditional natural language processing remains essential: Plan, Prepare Data, Engineer Model, Evaluate, Deploy, Operate, and Monitor.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

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Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

The repository also features architecture specifically designed for Computer Vision (CV) and Natural Language Processing (NLP) use cases. Model Observability: To be effective at monitoring and identifying model and data drift there needs to be a way to capture and analyze the data, especially from the production system.