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 - Automate No-Code ML
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
Learning objectives
- Master the fundamentals of Amazon SageMaker in a no-code certified training
- Develop skills in ML automation for the enterprise
- Design professional workflows with integration of no-code tools
- Deploy predictive models accessible without advanced coding
- Implement continuous training solutions tailored to business needs
- Optimize decision-making processes through professional machine learning
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
Companies that fail to master Amazon SageMaker 2026 lose an average of 25% productivity on their data projects.
Competitors adopting these no-code tools gain an 18-month lead on product innovations.
Professionals without these skills see their career opportunities shrink against rising demand for operational ML talent.
Failing to act now exposes them to lasting strategic lag and rapid obsolescence of traditional methods.
Program
Module 1Theme: Discovering SageMaker and no-code basics (Bubble, Zapier)
Participants discover the Amazon SageMaker 2026 environment through a no-code approach. They explore the Canvas and Autopilot interfaces, connect simple data sources, and create their first predictive models. Practical exercises with Bubble for interfaces and Zapier for automations enable the generation of concrete deliverables such as a functional business dashboard.
Module 2Theme: Integrating SageMaker with Power Apps and Make
This day focuses on integrating SageMaker with Power Apps and Make. Learners configure automated data flows, train classification models and expose them via low-code applications. Real business cases illustrate the creation of predictive reports and intelligent notifications, with deliverables directly usable in production.
Module 3Theme: Deploying SageMaker and no-code optimization
Participants finalize their project by deploying SageMaker models via no-code tools. They learn to monitor performance, automate updates and share results with business teams. Practical workshops enable delivery of a complete solution integrating Bubble, Zapier and Power Apps for immediate professional use.
Evaluation method
- Knowledge validation quizzes at the end of each day
- Practical project in pairs with final presentation
- Self-assessment of acquired skills on provided grid
Learning method
- Live demonstrations on SageMaker environment
- Guided exercises with immediate trainer feedback
- Practical work on real business cases
- Digital materials and recordings made available
Methods, materials and delivery
The Training Amazon SageMaker 2026 - Automate No-Code ML 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 - Automate No-Code ML 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 - Automate No-Code ML 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 - Automate No-Code ML training cost?+
The price is $3,675 (USD) per participant. A detailed quote is sent within one business day.
How long is the Training Amazon SageMaker 2026 - Automate No-Code ML training?+
The training lasts 3 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?+
Basic knowledge of office tools and data manipulation, no prerequisites in programming or ML algorithms
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.
Your professional training, anywhere
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