Juan Lamadrid's repositories
awesome-compose
Awesome Docker Compose samples
customer-er
Translating text attributes (like name, address, phone number) into quantifiable numerical representations Training ML models to determine if these numerical labels form a match Scoring the confidence of each match
pos-dlt
Get started with our Solution Accelerator to rapidly ingesting all data sources and types at scale, build highly scalable streaming data pipelines with Delta Live Tables to obtain a real-time view of operation, and leverage real-time insights to tackle your most pressing in-store information needs
Practical-Deep-Learning-at-Scale-with-MLFlow
Practical Deep Learning at Scale with MLFlow, published by Packt
ProphecyPython
ProphecyPython repository
auto-data-linkage
Low effort linking and easy de-duplication. Databricks ARC provides a simple, automated, lakehouse integrated entity resolution solution for intra and inter data linking.
customer-lifetime-value
Ingest sample retail data, build visualizations to explore past purchase behavior and use machine learning to predict the likelihood of future purchases
databricks-sdk-py
Databricks SDK for Python (Beta)
dbsql-rest-api
Databricks SQL Execution Statement API
dbtunnel
Proxy solution to run elegant Web UIs or interact with LLMs natively inside databricks notebooks.
diy-llm-qa-bot
Driving a Large Language Model Revolution in Customer Service and Support
hls-llm-doc-qa
Build a question answering system based on a given collection of documents with open-source LLMs
large-language-models
Notebooks for Large Language Models (LLMs) Specialization
ml.school
Machine Learning School
Practical-Machine-Learning-on-Databricks
Practical Machine Learning on Databricks, published by packt
product-search
Semantic product search on Databricks
review-summarisation
Automated Analysis of Product Reviews Using Large Language Models
segmentation
Create advanced customer segments to drive better purchasing predictions based on behaviors. Using sales data, campaigns and promotions systems, this solution helps derive a number of features that capture the behavior of various households. Build useful customer clusters to target with different promos and offers.