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 RLHF algorithms to effectively align large language models in a professional context.
- Develop complete RLHF pipelines with C# and ML.NET to boost enterprise skills.
- Implement advanced reward models from qualitative human feedback.
- Design Blazor applications integrating RLHF for interactive certified AI interfaces.
- Optimize RLHF performance in production ASP.NET with a focus on scalability and security.
- Deploy robust RLHF solutions, evaluating impact on business metrics in professional training.
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 1RLHF Fundamentals: Advanced Protocols and Reward Modeling (C#, ML.NET)
Dive into advanced RLHF protocols through practical workshops on ML.NET and C#, model reward functions from real human datasets, analyze biases with integrated debugging tools, build your first functional reward model, test it on simulated LLMs, and validate alignment metrics in real-time for immediate enterprise applications.
Module 2RLHF Pipelines: PPO and Iterative Fine-Tuning (ASP.NET Core, Dataset Curation)
Build end-to-end RLHF pipelines with Proximal Policy Optimization in C# on ASP.NET Core, curate and annotate human feedback datasets via collaborative interfaces, integrate automated training loops, optimize hyperparameters for rapid convergence, simulate production scenarios with high loads, and generate evaluation reports to certify your professional skills.
Module 3RLHF Integration: Blazor Apps and Secure APIs (Real-Time Feedback Loops)
Develop interactive Blazor applications embedding RLHF for live human feedback, expose robust ASP.NET APIs for RL iterations, implement safety and ethical alignment mechanisms, test with real users via A/B simulations, measure AI response improvements, and deploy a complete prototype ready for certified enterprise environments.
Module 4RLHF Deployment and Optimization: Scaling, Monitoring (Kubernetes, Observability)
Master RLHF deployment at scale with Kubernetes and ML.NET in production, configure advanced monitoring for drifts and performance, optimize training costs via advanced techniques, integrate CI/CD for continuous updates, analyze real alignment failure cases, and produce a business rollout plan validating your skills in certified professional training.
Evaluation method
- Practical projects on complete RLHF pipelines evaluated by experts.
- Advanced quizzes and real-world case studies in C# and ML.NET.
- Presentations of Blazor prototypes with quantified alignment metrics.
Learning method
- Intensive hands-on workshops for 80% practice, 20% theory.
- Case studies from leading companies in aligned AI.
- Real-time personalized feedback from certified trainers.
- Access to open-source codebase and post-training resources.
Methods, materials and delivery
The Training RLHF - Aligning AI with Human Feedback in .NET 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 RLHF - Aligning AI with Human Feedback in .NET 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 RLHF - Aligning AI with Human Feedback in .NET 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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