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The Three Big Announcements by Databricks AI Team in June 2024

Marktechpost

Table Search and Filtering: Integrated search and filtering functionalities allow users to find specific columns or values and filter data to spot trends and identify essential values. Enhanced Python Features: New Python coding capabilities include an interactive debugger, error highlighting, and enhanced code navigation features.

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Boost employee productivity with automated meeting summaries using Amazon Transcribe, Amazon SageMaker, and LLMs from Hugging Face

AWS Machine Learning Blog

The service allows for simple audio data ingestion, easy-to-read transcript creation, and accuracy improvement through custom vocabularies. Prerequisites To follow along with this post, you should have the following prerequisites: Python version greater than 3.9 AWS CDK version 2.0

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Improving air quality with generative AI

AWS Machine Learning Blog

The solution harnesses the capabilities of generative AI, specifically Large Language Models (LLMs), to address the challenges posed by diverse sensor data and automatically generate Python functions based on various data formats. The solution only invokes the LLM for new device data file type (code has not yet been generated).

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Automate the deployment of an Amazon Forecast time-series forecasting model

AWS Machine Learning Blog

You can implement this workflow in Forecast either from the AWS Management Console , the AWS Command Line Interface (AWS CLI), via API calls using Python notebooks , or via automation solutions. The console and AWS CLI methods are best suited for quick experimentation to check the feasibility of time series forecasting using your data.

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A Comprehensive Overview of Data Engineering Pipeline Tools

Marktechpost

Objective of Data Engineering: The main goal is to transform raw data into structured data suitable for downstream tasks such as machine learning. This involves a series of semi-automated or automated operations implemented through data engineering pipeline frameworks.

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Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training.

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Modular functions design for Advanced Driver Assistance Systems (ADAS) on AWS

AWS Machine Learning Blog

Automation levels The SAE International (formerly called as Society of Automotive Engineers) J3016 standard defines six levels of driving automation, and is the most cited source for driving automation. This ranges from Level 0 (no automation) to Level 5 (full driving automation), as shown in the following table.