Towards AI

AUGUST 25, 2023

Data Ingestion and Storage Resumes and job descriptions are collected from users and employers, respectively. AWS S3 is used to store and manage the data. NLP and Matching Engine Resumes and job descriptions are encoded into dense vector representations using a language model such as GPT or a custom fine-tuned model.

Data Ingestion 98

Data Ingestion NLP Large Language Models Software Engineer 98

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 Machine Learning Large Language Models ML Engineer 52

ODSC - Open Data Science

JULY 19, 2023

Personas associated with this phase may be primarily Infrastructure Team but may also include all of Data Engineers, Machine Learning Engineers, and Data Scientists. Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

AWS Machine Learning Blog

OCTOBER 23, 2024

Data flow Here is an example of this data flow for an Agent Creator pipeline that involves data ingestion, preprocessing, and vectorization using Chunker and Embedding Snaps. He focuses on Deep learning including NLP and Computer Vision domains.

Generative AI 77

Generative AI IDP LLM Automation 77

Unite.AI

JANUARY 13, 2025

My journey in the database field spans over 15 years, including six years as a software engineer at Oracle, where I was a founding member of the Oracle 12c Multitenant Database team. In 2017, the growing ability of AI to process unstructured data marked a turning point.

Data Scarcity 144

Data Scarcity ETL Data Ingestion Software Engineer 144

The MLOps Blog

MARCH 21, 2023

Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases. It would make sure that all development and deployment workflows use good software engineering practices. My Story DevOps Engineers Who they are?

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

DECEMBER 4, 2024

Amazon Kendra GenAI Index addresses common challenges in building retrievers for generative AI assistants, including data ingestion, model selection, and integration with various generative AI tools. Aakash Upadhyay is a Senior Software Engineer at AWS, specializing in building scalable NLP and Generative AI cloud services.

Metadata 95

Metadata Generative AI Data Ingestion Software Engineer 95