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Training Amazon SageMaker - Mastering Scalable Low-Code ML

Ref: VJE620
10 people max.
From $3,465 HT / per person
Pay in 3 installments · On-site on request · +$540 with certification exam
3 days
Remote

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Learning objectives

  • Master SageMaker notebooks to develop professional, certifiable ML skills
  • Configure automated training jobs in enterprise-adapted training
  • Deploy scalable predictive endpoints with SageMaker for real projects
  • Optimize hyperparameters using low-code tools in certifying training
  • Integrate SageMaker into data pipelines to boost enterprise skills
  • Design robust ML models via Studio and Canvas in a professional context

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 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 · AI expert

73%productivity gap
×3cost of inaction

Program

Module 1Amazon SageMaker: Data Preparation and Exploration (notebooks, processing jobs, low-code tools)

Discover advanced basics of SageMaker Studio for importing and cleaning large datasets via automated processing jobs, perform practical exercises on collaborative notebooks with libraries like Pandas and Scikit-learn, set up secure AWS environments for enterprise projects, test real-time data transformations, and produce exploratory reports ready for ML training, all in 7 interactive hours.

Module 2Amazon SageMaker: Model Training and Tuning (hyperparameter tuning, built-in algorithms)

Dive into distributed model training via SageMaker jobs, optimize hyperparameters with automated Bayesian algorithms, apply exercises on concrete cases like image classification or churn prediction, integrate XGBoost and TensorFlow in low-code, monitor performance live with CloudWatch, generate versioned model artifacts, and prepare scalable deployments, covering 7 hours of intensive certifying practice.

Module 3Amazon SageMaker: Deployment and Monitoring (endpoints, A/B testing, Canvas low-code)

Master serverless predictive endpoint deployment with auto-scaling, test models with A/B testing via SageMaker Experiments, explore Canvas for rapid no-code ML on business data, implement drift and bias monitoring with Model Monitor, complete a final project integrating full pipelines, export actionable insights for the enterprise, and validate skills via simulated production deployment, in 7 hours focused on professional results.

Evaluation method

  • Daily interactive quizzes on SageMaker tools
  • Final ML deployment project with personalized feedback
  • Qualiopi certifying attestation validating intermediate skills

Learning method

  • 70% hands-on approach on real enterprise cases
  • Shared Jupyter notebooks for hands-on exercises
  • Live remote pedagogical support
  • Post-training resources for skill consolidation

Methods, materials and delivery

The Training Amazon SageMaker - Mastering Scalable Low-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 - Mastering Scalable Low-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 - Mastering Scalable Low-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.

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.

FAQ

Frequently asked questions

How much does the Training Amazon SageMaker - Mastering Scalable Low-Code ML training cost?+
The individual price is $3,465 (USD). A detailed quote is sent within one business day.
How long is the Training Amazon SageMaker - Mastering Scalable Low-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?+
Basics in Python and machine learning, familiarity with AWS (S3, EC2), knowledge of Jupyter notebooks
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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