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FOMO Alert: Discover 7 Unmissable Reasons to Attend GTC 2024

NVIDIA

Silicon Valley Experience: Embrace the energy of the world’s foremost tech hub. Inside the conference, GTC connects attendees with the latest technologies and minds. These aren’t just lectures but opportunities to question, engage and turn insights into actionable knowledge that can shape your career trajectory.

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Allen Institute for AI raises $30M fund for incubator to boost more startups amid AI gold rush

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RELATED: Inside the AI2 Incubator: Microsoft co-founder’s unfinished legacy fuels quest for new AI startups More than 20 companies have gone through the AI2 incubator. “It’s not like they’re creating a speech coach inside [Microsoft] Word or Office,” he said.

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How NVIDIA Fuels the AI Revolution With Investments in Game Changers and Market Makers

NVIDIA

These target companies include Ayar Labs, specializing in chip-to-chip optical connectivity, and Hugging Face, a hub for advanced AI models. Since the beginning of 2023, announcements have been made about 14 investments. The portfolio also includes next-generation enterprise solutions.

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AI Lands at Bengaluru Airport With IoT Company’s Intelligent Video Analytics Platform

NVIDIA

detects speed violations made by vehicles outside the terminal, helping to manage safe transportation around the travel hub. AI helps manage transportation inside and outside of BLR. Unauthorized people and vehicles in the airport can also be tracked and alerted to the platform’s users in real time for enhanced security.

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Writing More Production-Ready Data Science Project (Part 2): Creating a Web App with Streamlit and…

Mlearning.ai

file inside streamlit folder. inside requirements.txt pandas==1.3.5 Then, we copy the requirements.txt file to the same file name inside the image, and execute the normal pip install commands via RUN. The project structure is as followed, as stated in part 1. The place we configure the Streamlit app is the app.py works, app.py

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The Hugging Face Ecosystem

Mlearning.ai

The main components of the Hugging Face ecosystem are the Hub, Transformers & Diffusers libraries, the Inference API, and their web-application builder Gradio. Let’s start with the Hub. The Hub The first component we’ll discover is the Hub. Hugging Face Hub is a platform with models, datasets, and demo applications.

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From Dev to Production: Deploying HuggingFace BERT with KServe

Mlearning.ai

Building a docker container with the custom python kserve.Model and push the docker container to docker hub Deploy the custom InferenceService to our minikube Kubernetes cluster Test the KServe-hosted HuggingFace model Now let’s get to work building out our custom text classification InferenceService on KServe.

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