Semantic product search on Databricks
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Updated
Mar 18, 2024 - Python
Semantic product search on Databricks
Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.
Perform fine-grained forecasting at the store-item level in an efficient manner, leveraging the distributed computational power of the Databricks Lakehouse Platform.
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
Automated Analysis of Product Reviews Using Large Language Models
Connect the impact of marketing and your ad spend to sales. Efficiently pinpoint the impact of various revenue-generating marketing activities to understand what works best. Focus on the best-performing channels to optimize media mix and drive revenue.
Use machine learning and the Databricks Lakehouse Platform for product matching that can be used by marketplaces and suppliers for various purposes. Resolve differences between product definitions and descriptions and determine which items are likely pairs and which are distinct across disparate data sets.
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
Ingest sample retail data, build visualizations to explore past purchase behavior and use machine learning to predict the likelihood of future purchases
Products We Think You Might Like: Generating Personalized Recommendations Using Matrix Factorization
Preempt churn with the Databricks Solution Accelerator for predicting subscriber attrition. Learn how to analyze behavioral data to identify subscribers with an increased risk of cancellation. Then use machine learning to quantify the likelihood to churn as well as indicate which factors explain that risk.
Get started with our Solution Accelerator for Scalable Route Generation to optimize delivery routes and increase profitability
Build a similarity-based image recommendation system for e-commerce that takes into account the visual similarity of items as an input for making product recommendations.
Perform demand forecasting at the part level rather than the aggregate level to minimize disruptions in your supply chain and increase sales. Manage material shortages and predict overplanning
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.
Survival analysis is a collection of statistical methods used to examine and predict the time until an event of interest occurs. In this Solution Accelerator, learn how to use different survival analysis techniques for predicting churn and calculating lifetime value.
This Solution Accelerator shows how OOS can be solved with real-time data and analytics by using the Databricks Lakehouse Platform to solve on-shelf availability in real time to increase retail sales. The accelerator can also be used for supply chain solutions.
Enabling Computer Vision Applications With the Data Lakehouse
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