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 Advanced ML Pipelines
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
Learning objectives
- Master advanced pipelines in Amazon SageMaker 2026 to accelerate professional ML projects
- Develop skills in auto-tuning and low-code feature engineering in a business context
- Design optimized serverless deployments with SageMaker for certified performance gains
- Implement automated MLOps workflows adapted for Qualiopi certification training
- Optimize ML models with the new SageMaker 2026 APIs for enterprise-ready results
- Integrate Zapier and Power Apps into SageMaker ecosystems for hybrid no-code automation
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 Amazon SageMaker 2026, 85% of enterprise ML projects fail at the deployment stage according to Gartner 2025, resulting in annual losses of 12M€ per SME from non-scalable models.
Data scientists stagnate in their careers, losing 25% of salary opportunities against MLOps-certified competition.
Companies risk undetected biases, GDPR fines up to 4% of revenue, and fatal competitive lag in low-code AI.
Invest now to turn these risks into lasting competitive advantages.
Program
Module 1SageMaker 2026 Pipelines: Advanced Configuration and Feature Stores (Low-Code Tools, Massive Datasets)
Discover SageMaker 2026 Processing and Training pipelines via the low-code Studio interface, configure scalable feature stores for terabyte datasets, perform practical exercises on real business cases like churn prediction, generate automated reports, integrate Bubble for no-code dashboards, and produce your first deployable pipeline by end of day with personalized trainer feedback.
Module 2AutoML and Tuning in SageMaker 2026: Optimized Hyperparameters (Advanced Algorithms, JumpStart)
Dive into the improved AutoML of SageMaker 2026 with JumpStart 2.0, test automated hyperparameters on XGBoost and Transformer models, apply hands-on exercises to tune neural networks in low-code, analyze metrics like ROC-AUC on Kaggle datasets, integrate Zapier for automated triggers, and validate deliverables like an optimized model ready for enterprise production.
Module 3SageMaker 2026 Endpoints and Inference: Serverless and Multi-Model (Edge, Low-Code Canvas)
Master serverless endpoints in SageMaker 2026 for real-time inference, deploy multi-models with no-code Canvas on concrete IoT cases, practice automatic scaling exercises under load, optimize AWS costs with simulated budgets, connect Power Apps for mobile interfaces, generate actionable CloudWatch logs, and end with a live group-tested endpoint with partial certification.
Module 4Advanced MLOps in SageMaker 2026: Monitoring and A/B Testing (Model Monitor, Experiments)
Implement complete MLOps workflows with SageMaker Experiments and Clarify for bias detection, monitor model drift in production via Model Monitor, conduct A/B tests on real e-commerce scenarios, automate retrainings with EventBridge, integrate no-code tools like Make for alerts, produce a personalized Grafana dashboard, and assess your skills via a certifying MLOps audit.
Module 5Hybrid Integrations in SageMaker 2026: No-Code/Low-Code Enterprise (API Gateway, Lambda)
Finalize with advanced SageMaker 2026 integrations to Power Apps and Zapier for hybrid apps, deploy via API Gateway and Lambda for infinite scalability, practice a capstone project on predictive supply chain, optimize costs by -40% via Spot Instances, generate a certifying portfolio with source code and live demo, receive individual coaching for your professional roadmap, and validate the training with a practical Qualiopi exam.
Evaluation method
- Daily interactive quizzes on Studio 2026 with automated scoring
- Day 5 capstone project: complete ML pipeline deployed and monitored
- Qualiopi certificate issued after 80% success on practical exercises
Learning method
- 100% hands-on with real datasets and SageMaker Studio low-code
- Case studies from Fortune 500 companies using SageMaker 2026
- Individual remote mentoring for advanced debugging
- Production simulations with real loads and live monitoring
Methods, materials and delivery
The Training Amazon SageMaker 2026 - Deploying Advanced ML Pipelines 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 Advanced 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 Amazon SageMaker 2026 - Deploying Advanced 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
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 Advanced ML Pipelines 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 Advanced ML Pipelines 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?+
Mastery of Python, supervised/unsupervised machine learning, basic AWS, experience with SageMaker Studio or equivalent
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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