Data footprint
Includes preparation of about 2 million unstructured records in a documented delivery setting.
Deep dive
Platform delivery, data preparation, and annotation automation.
The delivery layer behind model work: Databricks workflows, employee-facing RAG, data preparation, annotation and synthetic-data throughput, experiment iteration, and maintainable code.
Strong model work depends on the layer behind it: data, tooling, governed release controls, team coordination, and repeatable iteration.
Platform layer
Employee-facing Databricks RAG app using Databricks Vector Search and Model Serving LLM endpoints; approved for company-wide rollout to ~3,500 employees after a two-week helpdesk-team trial.
How the work runs
Release gates, audit logging, token/cost tracking, annotation automation, synthetic coverage, refactoring, and team coordination move together.
Platform map
From raw records to repeatable iteration.
The path moves through intake, preparation, annotation or augmentation, model iteration, and operational feedback.
01
Data intake
Work starts with large structured and unstructured sources, not clean benchmark inputs.
02
Preparation and ETL
Cleaning, transformation, Databricks-backed handling, and release controls create a usable training and evaluation surface.
03
Annotation and coverage
Automation and synthetic data expansion help reduce manual-label bottlenecks in class-heavy NLP work.
04
Model iteration
Transformers, LLMs, RAG, and supporting experiments move faster when the data layer advances with them.
05
Delivery loop
Refactoring, mentoring, token/cost tracking, and small-team coordination keep the work maintainable.
Delivery layers
The layers around the model work.
Databricks workflows
Platform choices shape iteration speed.
Recent delivery pairs LLM and NLP work with Azure Databricks so large-scale data handling and experimentation can move inside the same operating surface.
Data preparation
Useful modeling starts with careful data work.
Preparing about 2 million unstructured records and cutting model-preparation time by ~30% show depth beyond clean benchmark data.
Annotation automation
Coverage has to scale or the project stalls.
Automated collection, annotation support, and synthetic-data expansion reduced labeling pressure in a 29-class NLP problem and kept experimentation moving.
Mentoring
Delivery credibility includes code and people.
Refactoring Python code, mentoring junior data scientists, and managing 4 direct-report data science interns at Blue Guardian helped turn model work into a maintained delivery path.
Results
Workflow and throughput results.
Data preparation
About 2M records
Prepared for generative chemistry model work at Servier Canada.
Employee RAG
Approved for ~3,500 employees
Databricks App with Vector Search and Model Serving LLM endpoints, cleared for company-wide rollout after a two-week trial.
Model preparation
~30% faster
Model-preparation time cut by config-driven preprocessing and embedding pipelines.
Annotation cost
~$25K avoided
Estimated manual labeling cost avoided through LLM data generation, validation, and annotation.
Coverage expansion
LLM-generated synthetic text
Used to expand class coverage and accelerate iteration in the 29-class mental-health classification work.
What makes it real
Working patterns behind the results.
01
The data path is maintained, not treated as a one-time preprocessing script.
02
Automation is used to reduce bottlenecks, not to hide weak workflow ownership.
03
Leadership shows up as iteration speed, cleaner code, and team throughput.
The practical evidence is in the data preparation, platform decisions, automation strategy, and delivery loop that keep experimentation grounded.