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6 Remote AI Jobs to Look for in 2024

ODSC - Open Data Science

As the demand for AI professionals grows, so too does the opportunity for those who see remote AI jobs as a career goal. This is emphasized by the fact that many AI-focused companies are now actively hiring for remote positions, giving job seekers the flexibility to work from anywhere. billion in 2021 to $331.2 billion by 2026.

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Real Time Applications of Python: Boosting Efficiency and Innovation

Pickl AI

Scientific computing, automation, game development, and IoT projects also benefit from Python’s ease of use. Versatility Python is a versatile language that can be used for a wide range of applications, including web development, data analysis, artificial intelligence, scientific computing, automation, and more.

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How to Build a CI/CD MLOps Pipeline [Case Study]

The MLOps Blog

Additionally, CI/CD also provides the organization with a clear and transparent audit trail of the changes that have been made to the model, which can be useful for troubleshooting and compliance purposes The points elaborated below were some of the key considerations that went into our MLOps system design.

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What Can AI Teach Us About Data Centers? Part 2: Business Considerations

ODSC - Open Data Science

ChatGPT can help leaders understand how data centers can contribute to business success and how to evaluate investments in data center technologies. The major focus of Part 1 was describing what ChatGPT is, how it came to be, how it works, and how it can be used to address technical question about data centers.

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Learnings From Building the ML Platform at Mailchimp

The MLOps Blog

Today, I’m your host, Aurimas, and together with me, there’s a cohost, Piotr Niedźwiedź, who is a co-founder and the CEO of neptune.ai. How to transition from data analytics to MLOps engineering Piotr: Miki, you’ve been a data scientist, right? With us today on the episode is our guest, Mikiko Bazeley.

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AI-Fueled Productivity: Generative AI Opens New Era of Efficiency Across Industries

NVIDIA

Generative AI — the ability of algorithms to create new text, images, sounds, animations, 3D models and even computer code — is moving at warp speed, transforming the way people work and play. The engine driving generative AI is accelerated computing. The stakes are high.

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Training large language models on Amazon SageMaker: Best practices

AWS Machine Learning Blog

SageMaker Training is a managed batch ML compute service that reduces the time and cost to train and tune models at scale without the need to manage infrastructure. Parallelism – Your choice of distributed training library is crucial for appropriate use of the GPUs. Some of the best practices in this post refer specifically to ml.p4d.24xlarge