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Training Feature Store (Feast) - Mastering Scalable ML Pipelines

Ref: DLI349
10 people max.
5500€ HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$170/day onsite · +$500 with certification exam
5 journées
distanciel

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Learning objectives

  • Master the Feast architecture to design scalable feature stores in the enterprise.
  • Develop professional skills in online/offline feature management with Feast.
  • Implement certified ML pipelines optimized for industrial production.
  • Configure advanced feature views and registries to accelerate deployments.
  • Optimize ML model performance via Feast in a professional context.
  • Integrate Feast with Kubernetes and Spark ecosystems for certified training.
  • Deploy robust feature store solutions tailored to business needs.

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.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Feature Store (Feast) Architecture: Initial Configuration and Scalability (Docker, Kubernetes)

Discover the advanced foundations of Feast to deploy a robust feature store, install Feast via Docker and Helm on Kubernetes, configure providers like Redis and BigQuery for online/offline storage, perform practical exercises on creating registries, test connections to real data warehouses, produce a deliverable architectural schema with diagrams and production-ready scripts.

Module 2Feature Store (Feast): Advanced Feature Views and Materialization (Spark, Transformations)

Dive into defining complex feature views with Feast, implement ETL transformations via PySpark integrated into the store, materialize batch and streaming features on GCS or S3, perform exercises on optimizing materialization jobs, analyze performance with integrated monitoring, generate validation reports and a functional ETL pipeline deployed in a simulated cluster.

Module 3Feature Store (Feast): High-Performance Online Serving and Retrieval (Redis, Kafka)

Master online feature serving with Feast on Redis and Kafka, configure real-time retrievals for ML inferences, develop client scripts for asynchronous querying, test latency under load with tools like Locust, integrate with TensorFlow Serving models, produce a Prometheus monitoring dashboard and a scalable serving system ready to use.

Module 4Feature Store (Feast): Advanced Integrations and Security (MLflow, Kubeflow)

Integrate Feast with MLflow and Kubeflow for complete MLOps workflows, manage versioning of features and models, implement security with RBAC and data encryption, carry out practical end-to-end pipeline cases on AWS EKS, debug common production errors, deliver an integrated project with API documentation and automated tests.

Module 5Feature Store (Feast): Optimization and Real Enterprise Cases (Best Practices, Troubleshooting)

Optimize Feast feature stores for enterprise scale via advanced partitioning and caching, analyze real cases from Netflix and Uber, resolve real troubleshooting scenarios, deploy a complete PoC on hybrid cloud, evaluate ROI with business metrics, finalize with a certifying deliverable: optimized architecture, source code, and rollout plan.

Evaluation method

  • Interactive technical quizzes and advanced MCQs on Feast.
  • Real case studies with oral and written presentations.
  • Final certifying feature store deployment project.

Learning method

  • 100% hands-on pedagogical methods with dedicated cloud labs.
  • Hands-on exercises on real production Feast environments.
  • Live support by certified Qualiopi MLOps experts.
  • Unlimited access to replays and resources for 12 months.

Methods, materials and delivery

The Training Feature Store (Feast) - Mastering Scalable ML Pipelines 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 Feature Store (Feast) - Mastering Scalable ML Pipelines 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 Feature Store (Feast) - Mastering Scalable ML Pipelines 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

4.9 · +100 verified reviews
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
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.

Step 1

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.
Step 2

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.
Step 3

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.
Step 4

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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