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Training Amazon SageMaker - Deploying Scalable ML Pipelines

Ref: DNJ606
10 people max.
5250 € 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 SageMaker Studio for professional, certifying ML workflows
  • Develop scalable data processing pipelines in enterprise environments
  • Optimize distributed training of ML models with SageMaker
  • Deploy and manage high-performance ML endpoints in production
  • Implement monitoring and A/B testing for advanced competencies
  • Design SageMaker Serverless solutions for maximum efficiency

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.

Fouzi Benzidane
Fouzi Benzidane

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1SageMaker Studio: advanced configuration and collaborative workflows (Jupyter, Git, SDK)

Install and customize SageMaker Studio for professional data teams, configure secure domains with IAM and VPC, integrate Git for versioning notebooks and scripts, develop collaborative workflows on large datasets, practical exercises refactoring existing notebooks, produce reusable enterprise-optimized templates, achieve immediate measurable productivity gains from the first session.

Module 2SageMaker Processing: data pipelines and Feature Store (Spark, Pandas, transformations)

Build scalable Processing jobs with Spark and Pandas on SageMaker, process terabytes of raw data in parallel, integrate Feature Store for storing and serving features in real-time, apply advanced transformations like embedding and scaling, real-world cases on enterprise IoT logs, generate automated pipelines ready for training, proven 60% reduction in data preparation time through hands-on exercises.

Module 3SageMaker Training: hyperparameter tuning and distributed training (TensorFlow, PyTorch, Ray)

Launch distributed multi-GPU training on SageMaker with TensorFlow and PyTorch, automate hyperparameter tuning via Automatic Model Tuning, scale on Spot clusters to minimize AWS costs, optimize models on custom datasets like churn prediction, interactive tuning exercises with custom metrics, produce exportable trained models, validated 4x acceleration in time-to-model live.

Module 4SageMaker Deployment: endpoints and serverless inference (multi-models, autoscaling)

Deploy SageMaker endpoints with autoscaling and multi-models for real-time inference, configure batches for massive predictions, migrate to Serverless Inference 2026 for zero management, integrate Lambda and API Gateway for enterprise apps, load testing on real e-commerce scenarios, create canaries for safe deployments, achieve 99.9% reliability through intensive practical simulations.

Module 5SageMaker Monitoring and optimization: Model Monitor, Clarify, advanced Autopilot (drift, bias)

Implement Model Monitor to detect data drift and model decay in production, analyze biases with Clarify on complex models, optimize with SageMaker Autopilot for automated baselines, configure CloudWatch alerts and SageMaker Experiments, full audit case on enterprise capstone project, generate ML governance reports, master 2026 best practices for total compliance and 70% downtime reduction.

Evaluation method

  • Technical MCQ on SageMaker and its advanced components
  • Evaluation through deployment of a complete ML pipeline
  • Presentation of the capstone project with measured optimization

Learning method

  • Sessions led by active AWS certified experts
  • Practical exercises on real enterprise cases using AWS Free Tier
  • Capstone SageMaker project from processing to monitoring
  • Detailed course materials and access to recent labs

Methods, materials and delivery

The Training Amazon SageMaker - Deploying Scalable ML Pipelines 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 - Deploying Scalable 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 - Deploying Scalable 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

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
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« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
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« 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
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« 😊👍 »

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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