Training Model Serving AWS - Optimize Scalable Inference

Specialist course · Minimum level required

$6,300

  • 4 days
  • Remote
  • 10 people max.
Ref: GCK491
10 people max.
$6,300
In-company rate: from $10,710 for the whole teamExpertise rate: $1,575 per day, reflecting the difficulty levelAWS, Azure or OVH infrastructure: from $158 / day extra
On-site and certification exam on quote
4 days
Remote

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, Learni trainer · AI expert
Allan BUSI

Learni trainer · AI expert

73%productivity gap
×3cost of inaction
Flag of the United StatesBuilt for US teams
Payment by wire transferEnhanced confidentialityPerfect for companies with 50 to 500 employeesScheduling to fit any time zone

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Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft, Microsoft and Atos.

Model Serving AWS training objectives

  • Master the choice between real-time, asynchronous, serverless and batch endpoints to serve a model on AWS in a professional context.
  • Host multiple models behind a single endpoint and precisely measure the impact on performance.
  • Automatically scale on an invocation metric and handle production load spikes.
  • Deploy a new version in progressive mode with a reliable rollback strategy.
  • Measure the cost per thousand predictions for each serving option and optimize spend.
  • Integrate MLflow for version tracking and configure continuous drift monitoring.

Program

Model Serving AWS curriculum: 4 modules over 4 days

Module 1 : Real-time and Asynchronous Endpoints (SageMaker, MLflow)

Analysis of AWS serving architectures, creation of real-time and async endpoints, integration of MLflow for experiment tracking. Exercises on concrete participant use cases, latency measurement and first per-prediction costs, business feedback.

Module 2 : Serverless Inference and Scaling (SageMaker Serverless, Autoscaling)

Serverless deployment, configuration of autoscaling policies on invocation metrics, handling load spikes. Practical work with real data, comparison of latencies and costs, threshold tuning to optimize operational performance.

Module 3 : Batch Processing and Multi-Model Hosting (SageMaker Batch, Multi-Model Endpoints)

Setting up batch jobs, hosting multiple models on a single endpoint, measuring resource impact. Enterprise data use cases, analysis of cost per thousand predictions, optimization of serving strategies.

Module 4 : Progressive Deployments and Drift Monitoring (Canary, Model Monitor)

Progressive deployment strategies with rollback, implementation of drift monitoring, integration of business metrics. Deliverables: complete deployed pipeline, cost and latency dashboard, improvement plan for the company.

How Model Serving AWS skills are assessed

  • Hands-on exercise on a real AWS endpoint
  • Comparative analysis of costs and latencies
  • Review of the pipeline deployed by the instructor

How the Model Serving AWS course is taught

  • Practical exercises on participant data
  • Comparisons of serving modes under real conditions
  • Workshops on measuring and optimizing costs
  • Iterative feedback on deployments

Model Serving AWS training: access and practical arrangements

The Training Model Serving AWS - Optimize Scalable Inference 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 Model Serving AWS - Optimize Scalable Inference 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 Model Serving AWS - Optimize Scalable Inference 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.

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.

Our method

How Learni guarantees the quality of your Model Serving AWS training

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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Our social commitment

A school kit donated to a child for every training booked

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
Enroll and offer a kit
FAQ

Model Serving AWS: frequently asked questions

Which tools are used in Model Serving AWS training?+
Hands-on work runs on sageMaker, MLflow, sageMaker Serverless, autoscaling, sageMaker Batch and multi-Model Endpoints. Those tools come from the Model Serving AWS program itself, delivered remotely over 4 days, so participants practise on the stack they will use at work.
What can participants do after Model Serving AWS training?+
They can master the choice between real-time, asynchronous, serverless and batch endpoints to serve a model on AWS in a professional context, host multiple models behind a single endpoint and precisely measure the impact on performance and automatically scale on an invocation metric and handle production load spikes. Those are the stated objectives of the Model Serving AWS program at Advanced level, checked by the end-of-session assessment.
What does the Model Serving AWS program cover, day by day?+
Day 1: Real-time and Asynchronous Endpoints (SageMaker, MLflow). Day 2: Serverless Inference and Scaling (SageMaker Serverless, Autoscaling). Day 3: Batch Processing and Multi-Model Hosting (SageMaker Batch, Multi-Model Endpoints). Day 4: Progressive Deployments and Drift Monitoring (Canary, Model Monitor). The Model Serving AWS program runs over 4 days, delivered remotely.
How is Model Serving AWS training assessed?+
Assessment relies on hands-on exercise on a real AWS endpoint, comparative analysis of costs and latencies and review of the pipeline deployed by the instructor. Learni issues a completion certificate together with the individual assessment report at the end of the Model Serving AWS program, which an L&D team can file as evidence.
How much does Model Serving AWS training cost per participant?+
Model Serving AWS training costs $6,300 per participant. For an in-company team, the flat rate starts at $10,710 for the whole group, up to 8 people. AWS, Azure or OVH infrastructure is extra, from $158 per day. The detailed quote, sent within one business day, adjusts the amount to the format and how far the syllabus is tailored to your team.
How long does Model Serving AWS training take?+
4 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.
Who pays for Model Serving AWS training?+
Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
What do participants need to know before Model Serving AWS training?+
Confirmed experience in Python, ML model deployment and AWS fundamentals. Prior knowledge of SageMaker or equivalent is recommended.
Is a certificate issued after Model Serving AWS training?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.
Does Learni provide the equipment for Model Serving AWS training?+
No. A computer and a stable internet connection are required for each participant. Learni provides the learning platform, the trainer and all course materials.
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