Learn more about the magic behind BingeWiz

The Tech Stack Breakdown

Data preprocessing pipeline
Model training Model Serving
Experiment tracking, data versioning, model registry
Feature StorE
Model storage
Vector Similarity Search
Embedding/llm API


What sets us apart

User-centric recommendations

We base our recommendations solely on user watch history, allowing us to train on unrelated datasets.


We use OpenAI’s API to learn embeddings from movie plot and other textual features, reducing overfitting, enhancing model results and enabling the model to recommend new or lesser-known movies.

IMPROVED Embedding Techniques

By concatenating ratings into user watch history before applying an attention layer, we supercharge our model metrics.

Best Practices in ML Operations

Key Steps for Effective Model Deployment and Performance Optimization

Clear requirements

We set budget-aligned limits on deployment from the start, avoiding unnecessary investments in design and models, quickly abandoning frameworks and models that would waste time.

Experiment tracking

Using ClearML, we document training runs with code changes, dataset versions, and hyperparams for easy result comparison and reproduction.

Model monitoring

Our system is continuously monitored with Prometheus and Grafana, ensuring production success. We define metrics to compare the live model to the offline evaluations, and measure speed, throughput and other metrics.

Read from the mind of our experts