You will get Recommendation System Audit & Improvement Plan (Production ML)
Project details
Many recommendation systems fail not because of the model, but because of misaligned objectives, biased data, or weak evaluation setups.
This project is a focused audit of your existing recommendation or ranking system. The goal is to identify what’s actually limiting performance and provide a clear, prioritized improvement plan before you invest more time or budget.
I review your system from a production and business-impact perspective such as covering data, objectives, modeling approach, evaluation metrics, and experimentation setup. This is not a generic checklist; it’s a practical diagnosis based on real-world experience building and fixing large-scale recommender systems.
You’ll receive clear answers to questions like:
-Why do offline metrics look good but online impact is weak?
-Is the issue data, objectives, ranking logic, or evaluation bias?
-What should be fixed first to unlock real improvement?
-This audit is ideal if you already have a recommender system in place but results are disappointing, unstable, or hard to trust.
This project is a focused audit of your existing recommendation or ranking system. The goal is to identify what’s actually limiting performance and provide a clear, prioritized improvement plan before you invest more time or budget.
I review your system from a production and business-impact perspective such as covering data, objectives, modeling approach, evaluation metrics, and experimentation setup. This is not a generic checklist; it’s a practical diagnosis based on real-world experience building and fixing large-scale recommender systems.
You’ll receive clear answers to questions like:
-Why do offline metrics look good but online impact is weak?
-Is the issue data, objectives, ranking logic, or evaluation bias?
-What should be fixed first to unlock real improvement?
-This audit is ideal if you already have a recommender system in place but results are disappointing, unstable, or hard to trust.
What's included $350
These options are included with the project scope.
$350
- Delivery Time 7 days
- Number of Revisions 2
Frequently asked questions
About Berker
Senior Data Scientist | Recommendation, Ranking & Personalization
Istanbul, Turkey - 9:41 pm local time
I’m a senior data scientist with 13+ years of experience building and scaling production-grade recommendation, ranking, and personalization systems. My work focuses on real business outcomes such as engagement, conversion and retention, not just offline metrics.
I specialize in:
-Diagnosing underperforming recommendation & ranking systems
-Designing retrieval + ranking architectures
-Aligning metrics, A/B testing, and business goals
-Production ML pipelines, model serving, and monitoring
If you already have a model but results are disappointing or you want to design the system right from the start, I can help you take the right next step with clarity and confidence.
Steps for completing your project
After purchasing the project, send requirements so Berker can start the project.
Delivery time starts when Berker receives requirements from you.
Berker works on your project following the steps below.
Revisions may occur after the delivery date.
Context & Goal Alignment
Review business objectives, success metrics, and system constraints to ensure the recommendation system is optimized for the right outcomes.
System & Data Review
Analyze the current architecture, data flow, feature usage, and modeling approach to identify structural or data-related issues.