CLOUD

Cloud training

KBS Academy offers comprehensive Cloud training designed to help you master cloud computing environments and associated services. Our courses cover major platforms like AWS, Azure, and Google Cloud, as well as key topics such as infrastructure management, security, and cost optimization. Whether you want to learn how to migrate systems to the cloud or optimize resource utilization, our training will provide you with the essential skills to succeed in this rapidly growing field.

Take control of the digital future—enroll now in our Cloud training and become an indispensable expert in cloud solutions!

Training Objectives

Specific Objectives

Objectifs Spécifiques : A la fin de cette formation, (1) Vous saurez à quoi ressemble un véritable projet ML, (2) Vous apprendrez les algorithmes les plus connus du ML, (3) Vous apprendrez à implémenter plusieurs projets d’apprentissage automatique, (4) Vous serez en mesure d’inclure ces études de cas dans votre CV, (5) Vous serez en mesure de mieux vous vendre en tant que spécialiste ML, (6) Vous vous sentirez en confiance lors d’un entretien en ML.

Module descriptions

Module 01: Introduction to cloud computing

Identify cloud concepts:

Definition of cloud: Explain the concept of cloud computing as an online resource for data storage and processing.
How cloud works: Illustrate how the cloud enables access to computing resources via the internet and the importance of virtualization for this infrastructure.
Cost-effectiveness of cloud: Analyze cost savings, flexibility, and productivity gains; introduce economies of scale.
Virtualization and cloud computing: Examine virtualization as the foundation of the cloud, enabling resource isolation, portability, and energy efficiency.
SOA (Service-Oriented Architecture) and cloud computing: Show how service-oriented architectures allow modularity and facilitate the integration of cloud services.

Advantages and disadvantages of cloud computing:

Advantages:
- Outsourcing resources: Access to infrastructures without acquisition or maintenance costs.
- Dynamic resource allocation: Flexibility to adjust resources in real-time as needed.
- Logical isolation: separation of data for security and compliance.
Disadvantages:
- Security: Risks associated with data in shared environments.
- Legislation: Constraints on data location and management (e.g., GDPR).

Module 02: Understanding cloud structure

Types of cloud services (Service Models):

IaaS (Infrastructure as a Service): On-demand infrastructure for hosting, storing, and managing data.
PaaS (Platform as a Service): Platform for developing and deploying applications without direct infrastructure management.
SaaS (Software as a Service): Software accessible online for end-users, often by subscription.
Cloud deployment models:
Public cloud: Environment shared by multiple organizations, managed by providers like Amazon, Microsoft.
Private cloud: Cloud environment dedicated to a single organization, allowing more control and security.
Hybrid cloud: Combination of public and private cloud, ensuring increased flexibility for various workloads.

Module 03: Measuring the benefits of cloud for business

Impact by company size:
- Large enterprises: Cost optimization, scalability for large data volumes, reduced time to market.
- SMEs: Access to professional infrastructures without heavy investment, enhanced competitiveness.
- Startups: Agility for innovative projects and potential for rapid growth without high initial costs.
Cloud market overview: Understanding the current structure and trends of the cloud market.
Key providers: Introduction to providers such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure.
CAPEX/OPEX and Time to Market (TTM):
- CAPEX (Capital Expenditure): Initial investment for traditional infrastructure.
- OPEX (Operational Expenditure): Operational cost of cloud services as recurring expenses.
- TTM (Time to Market): Reduction in the time required to launch products or services by leveraging readily available resources.

Module 04: Discovering cloud applications

Scaling and flexibility of cloud resources

Scalability: Explains how the cloud enables managing demand spikes and allocating resources in real-time (e.g., auto-scaling).
Load balancing: Describes mechanisms for distributing workloads to optimize performance.

Performance and high availability

High availability mechanisms: Discusses redundancy and disaster recovery techniques to ensure optimal service availability.
Failure management: Explains how cloud providers handle service interruptions effectively.

Financial aspects of the cloud

Cost optimization: Studies pricing models (pay-as-you-go, subscription plans) to select the best value-for-money option.
ROI (Return on Investment): Demonstrates how the cloud increases ROI for businesses by saving on infrastructure, maintenance, and technical staff.

Module 05: Service continuity on the cloud after a disaster

Disaster recovery mechanisms: Study solutions to quickly restore services after a disaster, including backups and automated recovery with DRaaS (Disaster Recovery as a Service).
Multi-Cloud strategies for resilience: Discover how to distribute services across multiple cloud providers to avoid reliance on a single vendor and enhance application availability.

Module 06: Migration and optimization of information systems (IS) to the cloud

Steps and migration methods:
Explore approaches to cloud migration, such as:
- Lift and Shift: Directly migrate applications to the cloud without making changes.
- Partial Refactoring: Modify and adapt parts of applications to benefit from cloud capabilities.
- Cloud-Native Optimization: Fully optimize applications to leverage the features of cloud-native architecture.
Adopting DevOps Practices: Develop a DevOps culture to:
- Automate and optimize deployments.
- Foster agile collaboration between teams for a high-performance and resilient IS in the cloud.

Prix HT : 2000 € (3 jours)

Domaine :

Intelligence Artificielle, Développement, Data

Niveau :

Débutant et Intermédiaire

Public cible

Profil des Participants : Les passionnés de science des données et intelligence artificielle et toute personne ayant des connaissances en intelligence artificielle et en Python.
Prérequis : Notions de base des mathématiques et statistiques, et fondamentaux du Language de programmation Python.

Durée de la Formation

Nombre de Modules : Trois modules.
Durée Totale Estimée : 3 ou 4 jours.

Méthodologie Pédagogique

La formation combinera entre deux méthodes pédagogiques :

La méthode affirmative pour expliquer les fondamentaux du Machine Learning.
La méthode applicative / démonstrative pour réaliser des exercices, des études de cas et des cas pratiques avec des projets.

Evaluation et Certification

Modalités d’Évaluation : Quizz, tests, examen final.
Critères de Validation : Score minimum de 80 %.
Type de Certification : Certificat de participation, attestation de compétences

Matériel et Ressources

Outils et Logiciels Nécessaires : Python, Anaconda, Jupyter notebook.

Environnement Lab

Dans cette formation, les apprenants utiliseront Jupyter Notebook pour réaliser leurs travaux.