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Deploying LLM on RunPod

InnovationM

Runpod emerges as a beacon of innovation in cloud computing, specifically tailored to empower AI, ML, and general computational tasks. Engineered to harness the power of GPU and CPU resources within Pods, it offers a seamless blend of efficiency and flexibility through serverless computing options. How to approach it?

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning - AI

Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. We then discuss how building on a secure foundation is essential for generative AI.

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Managing Machine Learning Workloads Using Kubeflow on AWS with D2iQ Kaptain

d2iq

Complexity: There are lots of cloud-native and AI/ML tools on the market. Integration: Only a small percentage of production ML systems are model code; the rest is glue code needed to make the overall process repeatable, reliable, and resilient. Read the blog to learn more about D2iQ Kaptain on Amazon Web Services (AWS).

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

By George Trujillo, Principal Data Strategist, DataStax Increased operational efficiencies at airports. Investments in artificial intelligence are helping businesses to reduce costs, better serve customers, and gain competitive advantage in rapidly evolving markets. Instant reactions to fraudulent activities at banks.

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Of Muffins and Machine Learning Models

Cloudera

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. In this article, we explore model governance, a function of ML Operations (MLOps). Machine Learning Model Lineage. Each workspace is associated with a collection of cloud resources.

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

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Snowflake Best Practices for Data Engineering

Perficient

Introduction: We often end up creating a problem while working on data. So, here are few best practices for data engineering using snowflake: 1.Transform This means that data can be truncated and reprocessed if errors are found in the transformation pipeline , providing data scientists with a great source of raw data.