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.
Learning objectives
- Master essential AWS services for machine learning such as SageMaker, Rekognition, and Comprehend.
- Effectively prepare for the MLA-C01 certification exam with proven strategies.
- Configure and deploy scalable machine learning pipelines on the AWS platform.
- Validate your professional ML engineering skills for real-world projects.
- Implement optimized ML models with feature engineering and hyperparameter tuning on AWS.
- Obtain the AWS Machine Learning Engineer Associate certification to accelerate your career progression.
- Optimize costs and performance of ML solutions in AWS cloud environments.
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.
Program
Module 1AWS Machine Learning Fundamentals: SageMaker Studio, data preparation, and basic models (SageMaker, Glue, S3, practical labs)
Discover the pillars of machine learning on AWS through SageMaker Studio for an integrated environment. Learn to prepare data with Glue and S3, and train your first models using Jupyter notebooks. Practical labs on the AWS console: ingestion of real datasets, data exploration, and first automated training sessions. Deepen business use cases for immediate implementation (7 hours).
Module 2Advanced AWS ML Engineering: Feature engineering, tuning, and deployment (SageMaker Pipelines, Autopilot, endpoints, advanced labs)
Dive into advanced techniques with SageMaker Pipelines to orchestrate end-to-end workflows. Master feature engineering, hyperparameter tuning via Autopilot, and deployment of scalable endpoints. Practical exercises on the console: model optimization for production, integration with Lambda, and monitoring via CloudWatch. Analyze real business cases for directly applicable enterprise skills (7 hours).
Module 3Intensive MLA-C01 Exam Preparation: Practice QCM, full review, and strategies (exam simulations, error review)
Decisive day dedicated to certification: exhaustive review of MLA-C01 domains (data engineering, modeling, deployment). Take timed practice multiple-choice questions identical to the real exam, analyze errors with personalized feedback. Exam strategies: time management, common pitfalls, scoring focus. Final labs on complex AWS scenarios to consolidate knowledge. Ready to pass your cert with full confidence (7 hours).
Evaluation method
- Continuous evaluation through interactive quizzes and graded practical labs.
- MLA-C01 exam simulations with practice multiple-choice questions (200+ questions).
- Individual feedback on real projects and case studies.
- Qualiopi training certificate and optional AWS exam voucher.
Learning method
- Active pedagogy: 70% hands-on practice on the AWS console in real-time.
- Hands-on labs with real datasets and native AWS tools.
- Case studies from Fortune 500 companies using AWS ML.
- Blended learning method: concise theory + collaborative exercises in small groups.
Methods, materials and delivery
The Training AWS Machine Learning Engineer Associate MLA-C01 - Get Your Cert in 3 Days April 2026 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 AWS Machine Learning Engineer Associate MLA-C01 - Get Your Cert in 3 Days April 2026 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 AWS Machine Learning Engineer Associate MLA-C01 - Get Your Cert in 3 Days April 2026 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
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
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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