“datasets”

Build RAG / unstructured-document evaluation datasets and demo documents (e.g.

Generate realistic synthetic data using Spark + Faker (strongly recommended).

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology.

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure.

Translates SQL queries, DDL schemas, and stored procedures from legacy database dialects (Teradata, Oracle, Redshift, Snowflake, SparkSQL, HiveQL) to BigQuery…

Generates Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos on Google Cloud from resolved PromQL or ListTimeSeries queries.

Selects, deploys, and customizes AI models on Amazon SageMaker.

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT…

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end.