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10 Best Sales Engagement Platforms (October 2023)

Unite.AI

In this blog, we'll explore some of the best sales engagement platforms on the market. Buzz's performance metrics are impressive, with clients experiencing an average response rate between 8% to 12%. It also enriches contact information with social and publicly listed data, offering a more comprehensive understanding of customers.

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Optimize AWS Inferentia utilization with FastAPI and PyTorch models on Amazon EC2 Inf1 & Inf2 instances

AWS Machine Learning Blog

Solution overview FastAPI is an open-source web framework for serving Python applications that is much faster than traditional frameworks like Flask and Django. With FastAPI, you have your choice of Python web server ( Gunicorn , Uvicorn , Hypercorn , Daphne ). In case a worker is killed, the server must launch a new worker.

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Building a Dataset for Triplet Loss with Keras and TensorFlow

Flipboard

Luckily, OpenCV is pip-installable: $ pip install opencv-contrib-python If you need help configuring your development environment for OpenCV, we highly recommend that you read our pip install OpenCV guide — it will have you up and running in a matter of minutes. imagePath ) and processes the image to the appropriate type and size.

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Instruction fine-tuning for FLAN T5 XL with Amazon SageMaker Jumpstart

AWS Machine Learning Blog

You will see a list of foundation models, including FLAN T5 XL, which is marked as fine-tunable. Fine-tune FLAN-T5 using a Python notebook Our example notebook shows how to use Jumpstart and SageMaker to programmatically fine-tune and deploy a FLAN T5 XL model. Choose View model. It can be run in Studio or locally. Let’s use it!

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70+ Best and Unique Python Machine Learning Projects with source code [2023]

Mlearning.ai

In today’s blog, we will see some very interesting Python Machine Learning projects with source code. This list will consist of Machine learning projects, Deep Learning Projects, Computer Vision Projects , and all other types of interesting projects with source codes also provided. It is going to be a very interesting project.

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Unlocking efficiency: Harnessing the power of Selective Execution in Amazon SageMaker Pipelines

AWS Machine Learning Blog

Prerequisites To start experimenting with Selective Execution, we need to first set up the following components of your SageMaker environment: SageMaker Python SDK – Ensure that you have an updated SageMaker Python SDK installed in your Python environment. or higher: python3 -m pip install sagemaker>=2.162.0