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Training Amazon SageMaker 2026 - Deploying Scalable AI in the Enterprise

Ref: YUI333
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 new features of SageMaker 2026 for professional certifying ML workflows
  • Develop end-to-end automated pipelines boosting enterprise skills
  • Design scalable AI models with auto-scaling and edge deployment
  • Optimize costs and performance via SageMaker Debugger and Clarify
  • Implement advanced MLOps solutions for expert certification
  • Integrate SageMaker into hybrid enterprise ecosystems
  • Deploy autonomous AI agents with Guardrails and advanced Canvas

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 1SageMaker 2026 Pipelines: Advanced Architecture and Distributed Processing (SDK Tools, Processing Jobs)

Dive into SageMaker 2026 pipelines with practical exercises on massive datasets, configure distributed Processing Jobs via Python SDK and Boto3, integrate Ground Truth for automated labeling, test real-time feature engineering transformations, produce deliverables like reusable blueprints for your enterprise, and analyze performance metrics for immediate optimization.

Module 2Advanced SageMaker 2026 Training: Hyperparameter Tuning and Distributed Training (Built-in Algorithms, JumpStart)

Explore distributed training in SageMaker 2026 on GPU/TPU clusters, use Hyperparameter Tuning Jobs with Bayesian optimization, deploy built-in algorithms like XGBoost and DeepAR, integrate JumpStart for fine-tuning LLMs, perform exercises on real enterprise datasets, generate automated modeling reports, and validate models via advanced cross-validation for certifying skills.

Module 3SageMaker 2026 Deployment: Serverless Endpoints and Multi-Model Deployments (Inference, A/B Testing)

Master serverless endpoints in SageMaker 2026 for automatic scaling, configure multi-model deployments with traffic splitting, implement A/B testing and canary deployments, integrate SageMaker Runtime for low-latency inference, practice on concrete enterprise cases like real-time prediction, produce CloudWatch monitoring dashboards, and secure APIs via IAM and VPC endpoints.

Module 4SageMaker 2026 MLOps: Monitoring, Debugging, and Bias Detection (Model Monitor, Debugger, Clarify)

Deepen MLOps with SageMaker Model Monitor 2026 for continuous drift detection, use Debugger for real-time profiling, apply Clarify for explainability and bias mitigation, automate retrainings via EventBridge, test on production-like pipelines, generate GDPR-compliant reports, and integrate with GitHub Actions for complete CI/CD, strengthening professional skills.

Module 5Optimization and Innovation in SageMaker 2026: AI Agents, Canvas, and Edge (Autopilot++, Guardrails)

SageMaker 2026 innovations: deploy autonomous AI agents with Guardrails, leverage Canvas for expert no-code fine-tuning, optimize costs via Spot Instances and Savings Plans, integrate edge deployment on Greengrass, practice advanced enterprise cases like RAG pipelines, produce a final certifying project with deliverable portfolio, and plan a scalable MLOps roadmap for your organization.

Evaluation method

  • Daily technical quizzes and practical challenges on SageMaker 2026
  • Final project: end-to-end MLOps pipeline deployed in simulated production
  • Qualiopi certifying attestation with portfolio of concrete deliverables

Learning method

  • 70% hands-on: workshops on secure AWS playground
  • 30% theory: case studies from Fortune 500 companies
  • Individual remote mentoring for personalized feedback
  • Post-training resources: videos, templates, and alumni community

Methods, materials and delivery

The Training Amazon SageMaker 2026 - Deploying Scalable AI in the Enterprise 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 Amazon SageMaker 2026 - Deploying Scalable AI in the Enterprise 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 Amazon SageMaker 2026 - Deploying Scalable AI in the Enterprise 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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