ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Advanced ML Algorithms & EnsembleMethods Hyperparameter Tuning & Model Optimization AutoML & Real-Time MLSystems Explainable AI & EthicalAI Time Series Forecasting & NLP Techniques Who Should Attend: ML Engineers, Data Scientists, and Technical Practitioners working on production-level ML solutions.

Data Scientist 52

Data Scientist Large Language Models Machine Learning ML Engineer 52

Heartbeat

NOVEMBER 6, 2023

You might need to extract the weather and metadata information about the location, after which you will combine both for transformation. 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 type of execution is shown below.

ETL 52

ETL Python Metadata Deep Learning 52

The MLOps Blog

MARCH 12, 2024

Model management Teams typically manage their models, including versioning and metadata. Develop the text preprocessing pipeline Data ingestion: Use Unstructured.io to ingest data from health forums, medical journals, and wellness blogs. using techniques like RLHF.)

Prompt Engineering 52

Prompt Engineering Prompt Engineer Large Language Models LLM 52

The MLOps Blog

MAY 9, 2023

The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Data ingestion (extraction and versioning). Data validation (writing tests to check for data quality). Data preprocessing. Is it a black-box model, or can the decisions be explained?

ML 98

ML Machine Learning Metadata Data Science 98

The MLOps Blog

MARCH 21, 2023

To make that possible, your data scientists would need to store enough details about the environment the model was created in and the related metadata so that the model could be recreated with the same or similar outcomes. Your ML platform must have versioning in-built because code and data mostly make up the ML system.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

NOVEMBER 14, 2024

This metadata includes details such as make, model, year, area of the damage, severity of the damage, parts replacement cost, and labor required to repair. The information contained in these datasets—the images and the corresponding metadata—is converted to numerical vectors using a process called multimodal embedding.

Metadata 101

Metadata Data Ingestion Generative AI Computer Vision 101

AWS Machine Learning Blog

AUGUST 20, 2024

In this post, we discuss an architecture to query structured data using Amazon Q Business, and build out an application to query cost and usage data in Amazon Athena with Amazon Q Business. You can extend this architecture to use additional data sources, query validation, and prompting techniques to cover a wider range of use cases.

Natural Language Processing 119

Natural Language Processing Metadata NLP Data Ingestion 119

AWS Machine Learning Blog

OCTOBER 11, 2024

Generative AI solutions often use Retrieval Augmented Generation (RAG) architectures, which augment external knowledge sources for improving content quality, context understanding, creativity, domain-adaptability, personalization, transparency, and explainability. This can potentially improve the accuracy and quality of search results.

Metadata 105

Metadata Generative AI LLM Data Ingestion 105