Rigorous trainer selection
Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.
- Triple validation: technical, pedagogical, sectoral.
- Minimum rating 4.8/5 over the last 12 sessions.
Learning objectives
- Master re-ranking model architectures for professional searches
- Develop skills in fine-tuning enterprise-certified models
- Implement scalable and performant re-ranking pipelines
- Optimize real-time inference for critical applications
- Evaluate the business impact of re-ranking models in production
- Design advanced feature engineering for maximum precision
The Learni story
Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.
Don't let this gap widen
Why this program matters
Without this upskilling, your team accumulates a technological gap that translates directly into productivity loss.
Organizations that don't train their talents on key topics see their competitiveness drop.
Every quarter without training is a gap widening with competitors who invest.
The cost of inaction quickly exceeds that of well-targeted training.
Program
Module 1Re-ranking Models: Advanced Architectures and Feature Engineering (transformers, embeddings, datasets)
Dive into the advanced foundations of re-ranking models with comparative analysis of cross-encoder and bi-encoder transformers, rich feature extraction via Hugging Face and Sentence Transformers, preparation of custom enterprise datasets, practical exercises on real queries to boost relevance, creation of your first evaluable prototype with NDCG and MRR metrics.
Module 2Re-ranking Models: Fine-tuning and Optimization (PyTorch, LoRA, quantization)
Learn to fine-tune models like monoBERT or ColBERT on your internal data with PyTorch Lightning, integration of LoRA techniques for computational efficiency, optimization via quantization and distillation to reduce latency, practical workshops on real e-commerce cases, production of deployable custom models, immediate measurement of gains in precision and speed.
Module 3Re-ranking Models: Hybrid Pipelines and Evaluation (RAG, advanced metrics)
Build hybrid pipelines combining retrieval and re-ranking in a complete RAG flow with LangChain and FAISS, implement offline metrics like Recall@K and online A/B testing, simulations on massive enterprise query volumes, advanced debugging of biases and drifts, development of real-time monitoring dashboards, validation through concrete and actionable red thread project.
Module 4Re-ranking Models: Production Deployment and Use Cases (Docker, Kubernetes, scaling)
Master the deployment of re-ranking models in production via Docker and Kubernetes for horizontal scalability, FastAPI integration for fast inference APIs, cloud cost management on AWS/GCP, real use cases in search engines and personalized recommendation, stress-tests on high traffic, finalization of your complete pipeline with CI/CD and ready-to-use professional documentation.
Evaluation method
- Multiple-choice quiz to validate acquired knowledge at the end of the training
- Continuous evaluation through practical exercises
- Defense of the red thread project in front of the trainer
Learning method
- Sessions led by an expert practitioner
- Practical exercises on real enterprise cases
- Progressive red thread project throughout the training
- Complete course materials provided to each participant
Methods, materials and delivery
The Training Re-ranking Models - Optimizing the Precision of AI Searches program is delivered onsite or remote (blended-learning, e-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.
The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.
Equipment required
For the smooth delivery of the Training Re-ranking Models - Optimizing the Precision of AI Searches program, the following equipment is required:
- Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
- Training environments installed on workstations or accessible online
- Course materials, hands-on exercises and complementary resources
- Post-training access to materials and educational resources
For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.
* contact us for remote delivery feasibility** ratio varies depending on the program
Skills assessment methods
Assessment of skills acquired during the Training Re-ranking Models - Optimizing the Precision of AI Searches program is performed through:
- During training: case studies, hands-on labs and professional scenarios
- End of training: self-assessment questionnaire and skills evaluation by the trainer
- After training: completion certificate detailing acquired skills
Program accessibility
Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.
Enrollment terms and lead times
Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.
Verified reviews
What our learners
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
Our method
Training quality, guaranteed at every step
Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.
You meet the trainer beforehand
30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.
- Live briefing on goals and team context.
- Veto right — we swap the trainer for free if needed.
Content tailored to your context
No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.
- Hands-on cases drawn from your stack and projects.
- Program co-written then validated by your team.
Continuous quality follow-up
Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.
- NPS, knowledge quizzes and skills self-assessment.
- Satisfaction guarantee: fully satisfied or free rework.
A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.
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