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From concept to reality: Navigating the Journey of RAG from proof of concept to production

AWS Machine Learning Blog

Generative AI has emerged as a transformative force, captivating industries with its potential to create, innovate, and solve complex problems. Machine learning (ML) engineers must make trade-offs and prioritize the most important factors for their specific use case and business requirements.

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

Import the dataset into SageMaker Canvas In SageMaker Canvas, you can see quick actions to get started building and using ML and generative artificial intelligence (AI) models, with a no code platform. With a data flow, you can prepare data using generative AI, over 300 built-in transforms, or custom Spark commands.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Google Cloud Vertex AI Google Cloud Vertex AI provides a unified environment for both automated model development with AutoML and custom model training using popular frameworks. With the help of Neptune, AI teams at Waabi were able to optimize their experiment tracking workflow.

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Virtual fashion styling with generative AI using Amazon SageMaker 

AWS Machine Learning Blog

Generative artificial intelligence (AI) refers to AI algorithms designed to generate new content, such as images, text, audio, or video, based on a set of learned patterns and data. AI-driven design tools can create unique apparel designs based on input parameters or styles specified by potential customers through text prompts.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account. To solve this problem, we make the ML solution auto-deployable with a few configuration changes. ML engineers no longer need to manage this training metadata separately.

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DataRobot Notebooks: Enhanced Code-First Experience for Rapid AI Experimentation

DataRobot Blog

Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. The new DataRobot Notebooks offering plays a crucial role in providing a collaborative environment for AI builders to use a code-first approach to accelerate one of the most time-consuming parts of the machine learning lifecycle.

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Benchmarking Computer Vision Models using PyTorch & Comet

Heartbeat

Make sure that you import Comet library before PyTorch to benefit from auto logging features Choosing Models for Classification When it comes to choosing a computer vision model for a classification task, there are several factors to consider, such as accuracy, speed, and model size. What comes out is amazing AI-generated art!