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.
1 seat held for the next session
30:00Held for another
Request a call back and an advisor reaches you within 24 business hours, Monday to Saturday.
Training Amazon SageMaker 2026 - Deploying Scalable AI in the Enterprise
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
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
Our social commitment
A school kit donated to a child for every training
To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.
- Backpack, notebooks and essential supplies
- Distributed through our partner charities
- Included, at no extra cost to you
The Learni story
Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.
Don't let this gap widen
Why this program matters
Without mastery of SageMaker 2026, 87% of enterprise ML projects fail in the deployment phase according to Gartner, resulting in average losses of 1,5M€ per delayed project.
Teams lose 40% of their time on manual debugging and cloud costs inflated by 300% without autoscale optimization.
Your career stagnates amid explosive demand for MLOps experts, while competitors scale AI 5x faster.
Invest now to convert these risks into lasting, certifiable competitive advantages.
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, 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
Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.
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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FAQ
Frequently asked questions
How much does the Training Amazon SageMaker 2026 - Deploying Scalable AI in the Enterprise training cost?+
The price is $5,775 (USD) per participant. A detailed quote is sent within one business day.
How long is the Training Amazon SageMaker 2026 - Deploying Scalable AI in the Enterprise training?+
The training lasts 5 journées, available live online (US time zones) or on-site at your offices.
How is this training paid for?+
Most US teams pay directly through their company (L&D or training budget). We invoice in US dollars and accept bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
Are there any prerequisites?+
Advanced proficiency in Python, machine learning, AWS (EC2, S3, Lambda), CI/CD pipelines, and deep learning models deployed in production
Is a certificate delivered at the end?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report.
Does Learni provide the equipment?+
No. A computer and stable internet connection are required for the participant. Learni provides the educational platform, the trainer and all course materials.
On-site & remote
This training across cities
Available on-site and remotely. Pick your city to see the local training center.
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