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Incremental Machine Learning for Linked Data Event Streams

Towards AI

Unlocking the Power of Real-time Predictions: An Introduction to Incremental Machine Learning for Linked Data Event Streams Photo by Isaac Smith on Unsplash This article discusses online machine learning, one of the most exciting subdomains of machine learning theory. LDES workbench in Apache NIFI (Image by the author.)

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Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit?—?Part 2 of 3

Mlearning.ai

Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit — Part 2 of 3 A comprehensive guide to develop machine learning applications from start to finish. Introduction Welcome Back, Let's continue with our Data Science journey to create the Stock Price Prediction web application.

Python 52
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Data Scientists : The Business Transcribers of the Cyber verse

Becoming Human

If you have any reviews, critics, or any need of advice for any analytics/Data Science/Machine Learning based project. Feel free to reach out to me on LinkedIn and you may use my Github/Kaggle repository of python and R code templates and already-made visualization’s for implementation or reference.

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13 Must Follow Best YouTube Channels for Data Science

Pickl AI

I wanted to know if YouTube could be my knowledge repository to learn more about Data Science. Top Data Science YouTubers 1. Data School by Kevin Markham Category: Tutorials and Python Programming Subscribers: 216K Link to the Channel: [link] It is a treasure trove of Data Science tutorials.

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Supercharging Your Data Pipeline with Apache Airflow (Part 2)

Heartbeat

In the image, you can see that the extract the weather data and extract metadata information about the location need to run in parallel. This is necessary because additional Python modules need to be installed. Similarly, if you need to complete a Python process, you will need the Python operator.

ETL 52
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Deploy pre-trained models on AWS Wavelength with 5G edge using Amazon SageMaker JumpStart

AWS Machine Learning Blog

script to retrieve the JumpStart model artifacts and deploy the pre-trained model to your local machine: python train_model.py encode("utf-8") import requests r2=requests.post(url="[link] data=request_body, headers={"Content-Type":"application/x-text","Accept":"application/json;verbose"}) print(r2.text) Run the train_model.py

BERT 101
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Automatically Testing for Demographic Bias in Clinical Treatment Plans Generated by Large Language Models

John Snow Labs

This study introduces LangTest , an innovative open-source Python library crafted to empower developers in the assessment and enhancement of Natural Language Processing (NLP) models. Supported Data: [link] data Testing in 3 lines of Code !pip In Conclusion: Setting up the Harness is like preparing a toolbox for a job.