Catalogue 188 Courses found
List of sub-fields
Course list
Reference | Course | Duration | Prices | |
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CT CASP-005 | Advanced Security Operations CASP+ | 5 days 40 hours |
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MS AI-050 | AI-050: Develop Generative AI Solutions with Azure OpenAI Service | 1 day 8 hours |
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MS AI-102 | AI-102T00--A: Designing and Implementing a Microsoft Azure AI Solution | 4 days 32 hours |
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MS AI-3003 | AI-3003: Build a natural language processing solution with Azure AI Services | 1 day 8 hours |
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MS AI-900 | AI-900: Microsoft Azure AI Fundamentals | 1 day 8 hours |
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EV8 TECH-001 | Artificial Intelligence (AI) | 4 days 32 hours |
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Ev8 ACE-001 | Associate Cloud Engineer | 10 days 300 hours |
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AWS CLF-C01 | AWS Certified Cloud Practitioner (CLF-C01) | 3 days 24 hours |
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AWS DVA-C02 | AWS Certified Developer – Associate (DVA-C02) | 3 days 24 hours |
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AWS SAA-C03 | AWS Certified Solutions Architect - Associate (SAA-C03) | 4 days 32 hours |
5 days (40 hours)
At the end of the course, participants will be able to:
- Analyze security requirements in hybrid networks to work toward an enterprise-wide, zero-trust security architecture with advanced secure cloud and virtualization solutions.
- Address advanced threat management, vulnerability management, risk mitigation, incident response tactics and digital forensic analysis.
- Prove an organization's overall cybersecurity resiliency metric and compliance to regulations.
- Configure for endpoint security controls, enterprise mobility, cloud/hybrid environments and enterprise-wide PKI.
CT CASP-005
At the end of this A1-050 training, participants should be able to:
- Design and implement generative artificial intelligence models using the Azure platform.
- Build, evaluate, and optimize AI models.
- Manage and Monitor AI workflows.
- Integrate AI solutions into business processes.
- Develop proficiency in leveraging Azure's pre-built AI capabilities.
MS AI-050
4 days (32 hours)
At the end of this AI-102 training, participants should be able to:
- Design, implement, and monitor AI solutions in Microsoft Azure.
- Use cognitive services, machine learning, and knowledge mining to architect and implement Microsoft AI solutions.
- Implement computer vision, natural language processing, and conversational AI.
- Manage and optimize AI solutions.
MS AI-102
At the end of the AI-3003 training, participants should be able to:
- Deploy a language resource and use prebuilt models.
- Create a custom text classification solution.
- Create a custom named entity recognition (NER) solution.
MS AI-3003
1 day (8 hours)
At the end of this AI-900 training, participants should be able to:
- Describe cloud concepts.
- Describe core Azure services.
- Describe security, privacy, compliance, and trust.
- Describe Azure pricing, Service Level Agreements, and Lifecycles.
- Leverage the lab environment during the instructional portion of the class.
MS AI-900
4 days (32 hours)
At the end of this training, you will be able to:
- State different kinds of solutions AI can make possible and considerations for responsible AI practices.
- Describe the core concepts of machine learning.
- Identify different types of machine learning.
- Describe considerations for training and evaluating machine learning models.
- Describe core concepts of deep learning.
- Use automated machine learning in Azure Machine Learning service.
EV8 TECH-001
10 days (300 hours)
By the end of the training, participants should be able to:
- Explain the core concepts and components of cloud infrastructure, including virtual machines, networks, storage, and identity services.
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Optimize compute resources for performance, cost, and scalability.
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Implement and troubleshoot network connectivity between cloud resources.
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Understand the basic concepts of cloud databases, including Cloud SQL and Cloud Firestore.
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Apply security best practices for data protection, encryption, and compliance in the cloud environment.
Ev8 ACE-001
3 days (24 hours)
At the end of the training, participants should be able to:
- Explain the value of AWS Cloud.
- Understand and explain the AWS shared responsibility model.
- Understand AWS Cloud security best practices.
- Understand AWS Cloud costs, economics, and billing practices.
- Describe and position the core AWS services, including compute, network, databases, and storage.
- Identify AWS services for common use cases.
AWS CLF-C01
3 days (24 hours)
At the end of this training, participants should be able to:
- Identify the key features of the core AWS technologies used to build serverless applications, like S3, DynamoDB, Elastic Beanstalk, Lambda, and API Gateway.
- Build, deploy, and troubleshoot serverless applications in AWS.
- Use AWS CLI, AWS service APIs, and SDKs to interact with AWS.
- Create a CI/CD pipeline to deploy applications on AWS.
- Implement AWS security best practices using IAM, KMS, and MFA.
- Configure AWS services for optimal performance.
AWS DVA-C02
4 days (32 hours)
At the end of this training, participants should be able to:
- Design and implement distributed systems on AWS.
- Design cost and performance optimized solutions, demonstrating a strong understanding of the AWS Well-Architected Framework.
- Make informed decisions about when and how to apply key AWS Services for compute, storage, database, networking, monitoring, and security.
- Design architectural solutions to address common business challenges.
- Create and operate a data lake in a secure and scalable way, ingest and organize data into the data lake, and optimize performance and costs.
- Prepare for the certification exam, identify your strengths and gaps for each domain area, and build strategies for identifying incorrect responses.
- Deploy, manage, and operate workloads on AWS as well as implement security controls and compliance requirements.
- Use the AWS Management Console and the AWS Command Line Interface (CLI).
- Identify which AWS services meet a given technical requirement and define technical requirements for an AWS-based application.