Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Fireworks.ai - Scaling AI Inference in Production training are: Mastery of Python, REST/GraphQL APIs, LLM models, and Docker container basics. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Fireworks.ai - Scaling AI Inference in Production training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Fireworks.ai - Scaling AI Inference in… | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training Fireworks.ai - Scaling AI Inference in Production training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training Fireworks.ai - Scaling AI Inference in Production training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Fireworks.ai - Scaling AI Inference in Production training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master Fireworks.ai to deploy scalable professional AI models

Optimize LLM and generative model inference in the enterprise

Develop certifying AI applications with Fireworks.ai SDK

Implement fine-tuning and custom models on Fireworks.ai

Design secure and monitored production pipelines

Deploy performant multimodal AI workflows for advanced skills

Training program

Module 1Fireworks.ai: Advanced Configuration and Inference APIs (Python/JS SDK, Authentication)

Quick installation of the Fireworks.ai environment, hands-on with official Python and JavaScript SDKs for secure authentication and initial API requests, practical exercises on inference with open-source LLMs like Llama 3, analysis of generated responses with JSON parsing, creation of a first professional deployable script for enterprise use, real-time debugging of common errors for smooth and efficient implementation from day one.

Module 2Fireworks.ai: LLM Inference Optimization (Prompt Engineering, Batching, Caching)

In-depth exploration of advanced prompt engineering techniques tailored to Fireworks.ai, implementation of batching for handling massive simultaneous request volumes, configuration of intelligent caching to reduce latencies by 70%, real-world cases on enterprise chatbots with hybrid RAG, hands-on exercises to measure and optimize throughput live, production of custom performance metrics ready for production use.

Module 3Fireworks.ai: Multimodal Generation (Images, Embeddings, Fine-Tuning)

Deployment of image generative models like Stable Diffusion on Fireworks.ai, integration of vector embeddings for semantic search, first steps in serverless fine-tuning of custom models via API, practical projects on generating visual assets for AI marketing, chaining text-image modalities for creative workflows, validation of outputs with automated quality tools, deliverables ready for immediate application integration.

Module 4Fireworks.ai: Security, Scaling, and Monitoring (RBAC, Autoscaling, Observability)

Advanced configuration of RBAC and rate limiting on Fireworks.ai for secure enterprise environments, setup of automatic autoscaling for traffic spikes, integration of monitoring with Prometheus and Grafana for real-time traceability, extreme load simulations with Locust tools, API hardening against injections and data leaks, development of proactive alerts, consolidation into an operational dashboard for DevOps teams.

Module 5Fireworks.ai: Red Thread Projects and Production Deployment (CI/CD, Cloud Hybridization)

Assembly of the red thread project into a complete AI application on Fireworks.ai, implementation of CI/CD pipelines with GitHub Actions for continuous deployments, hybridization with other clouds like AWS Lambda, end-to-end resilience and performance testing under stress, collective code review by the expert trainer, final packaging with certifying documentation, preparation for scalable production rollout for immediate ROI in the enterprise.