Remove 2017 Remove Artificial Intelligence Remove ML
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Building Generative AI and ML solutions faster with AI apps from AWS partners using Amazon SageMaker

AWS Machine Learning Blog

Our customers want a simple and secure way to find the best applications, integrate the selected applications into their machine learning (ML) and generative AI development environment, manage and scale their AI projects. Comet has been trusted by enterprise customers and academic teams since 2017.

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Machine Learning & Data Analysts: Seizing the Opportunity in 2018

Dataconomy

Undoubtedly, 2017 has been yet another hype year for machine learning (ML) and artificial intelligence (AI). As ML and AI become increasingly ubiquitous in many industries, so does the proof that advanced analytics significantly improve day-to-day operations and drive more revenue for businesses.

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Navigating tomorrow: Role of AI and ML in information technology

Dataconomy

Artificial intelligence and machine learning are no longer the elements of science fiction; they’re the realities of today. With the ability to analyze a vast amount of data in real-time, identify patterns, and detect anomalies, AI/ML-powered tools are enhancing the operational efficiency of businesses in the IT sector.

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2021 in Review: What Just Happened in the World of Artificial Intelligence?

Applied Data Science

Transformers taking the AI world by storm The family of artificial neural networks (ANNs) saw a new member being born in 2017, the Transformer. ML models are however statistical in nature, which theoretically means that their average performance may be very different from the one during a specific training run.

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Llama 4 family of models from Meta are now available in SageMaker JumpStart

AWS Machine Learning Blog

This approach allows for greater flexibility and integration with existing AI and machine learning (AI/ML) workflows and pipelines. By providing multiple access points, SageMaker JumpStart helps you seamlessly incorporate pre-trained models into your AI/ML development efforts, regardless of your preferred interface or workflow.

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Getting Started with AI

Towards AI

As a reminder, I highly recommend that you refer to more than one resource (other than documentation) when learning ML, preferably a textbook geared toward your learning level (beginner/intermediate / advanced). In ML, there are a variety of algorithms that can help solve problems. I also have a medium article on AI Learning Resources.

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Announcing new Jupyter contributions by AWS to democratize generative AI and scale ML workloads

AWS Machine Learning Blog

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

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