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FastAPI Meets OpenAI CLIP: Build and Deploy with Docker

Flipboard

Jump Right To The Downloads Section Building on FastAPI Foundations In the previous lesson , we laid the groundwork for understanding and working with FastAPI. Interactive Documentation: We showcased the power of FastAPIs auto-generated Swagger UI and ReDoc for exploring and testing APIs. Looking for the source code to this post?

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Training a Custom Image Classification Network for OAK-D

PyImageSearch

Table of Contents Training a Custom Image Classification Network for OAK-D Configuring Your Development Environment Having Problems Configuring Your Development Environment? Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g.,

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How to Use Hugging Face Pipelines?

Towards AI

Hugging Face is a platform that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more. The NLP tasks we’ll cover are text classification, named entity recognition, question answering, and text generation. Next, when creating the classifier object, the model was downloaded.

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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

Flipboard

Overview of solution In this post, we go through the various steps to apply ML-based fuzzy matching to harmonize customer data across two different datasets for auto and property insurance. Run an AWS Glue ETL job to merge the raw property and auto insurance data into one dataset and catalog the merged dataset.

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Build an image-to-text generative AI application using multimodality models on Amazon SageMaker

AWS Machine Learning Blog

CLIP model CLIP is a multi-modal vision and language model, which can be used for image-text similarity and for zero-shot image classification. This is where the power of auto-tagging and attribute generation comes into its own. Moreover, auto-generated tags or attributes can substantially improve product recommendation algorithms.

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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning Blog

Use case overview The use case outlined in this post is of heart disease data in different organizations, on which an ML model will run classification algorithms to predict heart disease in the patient. You can also download these models from the website. module.eks -auto-approve terraform destroy -target=module.m_fedml_edge_client_2.module.eks

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Deploying HuggingFace Models with AWS SageMaker

Pragnakalp

Now click on the “Download.csv” button to download the credentials (Access Key ID and Secret access key). Auto-Scaling for Dynamic Workloads One of the key benefits of using SageMaker for model deployment is its ability to auto-scale. Deploying Hugging Face Models Create a virtual environment and install the required libraries.