Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

What is the duration of the Training AWS IoT Core - Deploying Secure IoT Fleets training?

The Training AWS IoT Core - Deploying Secure IoT Fleets training takes place over 28 heures. The program is structured in progressive modules for optimal skill development.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training AWS IoT Core - Deploying Secure IoT Fleets training are: AWS experience (IAM, Lambda, VPC), proficiency in MQTT/CoAP, Python/Node.js programming. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training AWS IoT Core - Deploying Secure IoT Fleets training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training AWS IoT Core - Deploying Secure IoT Fleets | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training AWS IoT Core - Deploying Secure IoT Fleets training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training AWS IoT Core - Deploying Secure IoT Fleets training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training AWS IoT Core - Deploying Secure IoT Fleets training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training access terms and deadlines

Training objectives

Master AWS IoT Core architecture for professional IoT deployments

Configure secure and scalable device fleets in enterprise settings

Implement advanced rules with certifying serverless integrations

Optimize IoT security via Device Defender and OTA updates

Deploy hybrid AWS IoT Greengrass solutions in production

Analyze IoT performance with CloudWatch and Fleet Indexing

Develop certifying skills to boost your cloud career

Training program

Module 1AWS IoT Core: Advanced Device Configuration and Thing Registry (MQTT, X.509 certificates, policies)

Start by creating an optimized Thing Registry for thousands of devices, generate certificates via Just-In-Time Registration with AWS CLI, test secure MQTT connections via MQTT.fx and the console interface, implement granular IoT policies to restrict access, diagnose common errors in real-time, and produce a connected device prototype ready for scalable professional environments.

Module 2AWS IoT Core: Rules Engine and Serverless Integrations (Lambda, DynamoDB, Kinesis)

Dive into the rules engine to route IoT messages to Lambda, DynamoDB or Kinesis, configure advanced SQL transformations on MQTT payloads, integrate conditional actions with republish and cloud-to-device messaging, develop custom Lambda functions to process data in real-time, test end-to-end flows on concrete enterprise cases like industrial telemetry, and generate initial dashboards to visualize operational insights.

Module 3AWS IoT Core Security: Device Defender and OTA Updates (Anomaly Detection, Mutual TLS)

Strengthen security by activating Device Defender to detect behavioral anomalies via ML metrics, configure compliance audits and automated mitigation actions, implement secure OTA updates with cryptographic signatures, test mutual TLS and certificate rotation on simulated fleets, analyze common attacks like MQTT injections, and produce a certifying defense plan to protect your IoT solutions in production against cyber threats.

Module 4Scaling AWS IoT Core: Greengrass, Fleet Management and Monitoring (CloudWatch, Shadow Service)

Master scaling with AWS IoT Greengrass for hybrid edge deployments, configure Greengrass cores with local ML components, manage fleets via Fleet Indexing and Jobs, integrate Thing Shadows for offline-online synchronization, monitor metrics via CloudWatch alarms and X-Ray tracing, optimize costs on high-load scenarios like smart cities, and deploy a complete ongoing project with performance report for immediate production deployment.