In this course, you learn how to define automation policy for IBM System Automation for z/OS® (SA z/OS).
You learn how to create policy definitions for systems, applications, application groups, and monitor resources. This is delivered in an environment with multiple opportunities for hands-on lab exercises. You define automation policy for several environments: single system and multiple system within a basic sysplex. The System Automation for z/OS automation manager and automation agent run in a z/OS 2.2 environment. The automation platform, Tivoli® NetView for z/OS is at version 6 release 2.
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The Advanced Generative AI Development on AWS is designed for developers seeking to master the implementation of production-ready generative AI solutions on AWS. The course addresses the needs of organizations embarking on their generative AI journey and how to build comprehensive generative AI strategies that align with broader business objectives.
This advanced 3-day instructor-led training builds expertise across the entire generative AI stack – from foundation models to enterprise integration patterns. In addition, you will learn about advanced data processing techniques, vector database implementation and retrieval augmentation, sophisticated prompt engineering and governance, agentic AI systems and tool integration, AI safety and security measures, performance optimization and cost management strategies, comprehensive monitoring and observability solutions, testing and validation frameworks.
The course structure follows AWS’s proven model for generative AI adoption, progressing from experimentation to production-ready implementations.
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Data Engineering on AWS is a 3-day intermediate course, designed for professionals seeking a deep dive into data engineering practices and solutions on AWS.
Through a balanced combination of theory, practical labs, and activities, participants learn to design, build, optimize, and secure data engineering solutions using AWS services.
From foundational concepts to hands-on implementation of data lakes, data warehouses, and both batch and streaming data pipelines, this course equips data professionals with the skills needed to architect and manage modern data solutions at scale.
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Machine Learning (ML) Engineering on Amazon Web Services (AWS) is a 3-day intermediate course designed for ML professionals seeking to learn machine learning engineering on AWS. Participants learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities.
Participants will gain practical experience using AWS services such as Amazon SageMaker AI and analytics tools such as Amazon EMR to develop robust, scalable, and production-ready machine learning applications.
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This AWS-COA: Cloud Operations on AWS course teaches systems operators and anyone performing cloud operations functions how to manage and operate automatable and repeatable deployments of networks and systems on AWS. You will learn about cloud operations functions, such as installing, configuring, automating, monitoring, securing, maintaining, and troubleshooting these services, networks, and systems.
The course also covers specific AWS features, tools, and best practices related to these functions.
Trainocate is an AWS Authorized Training Partner as well as the AWS Global Training Partner of the Year 2024 is trusted by AWS to offer, deliver, and/or incorporate official AWS Training, including classroom and digital offerings. Whether your team prefers to learn from live instructors, on-demand courses, or both, ATPs offer a breadth of AWS Training options for learners of all levels.
Invest in your team’s cloud future: Master AWS with 600+ courses, labs, and interactive experiences, elevate your team’s cloud expertise with AWS Skill Builder Team Subscription.
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Learn how to extend DevOps practices to build, train and deploy machine learning models.
This MLOps Engineering on AWS course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations.
The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators.
Trainocate is an AWS Authorized Training Partner as well as the AWS Global Training Partner of the Year 2024 is trusted by AWS to offer, deliver, and/or incorporate official AWS Training, including classroom and digital offerings. Whether your team prefers to learn from live instructors, on-demand courses, or both, ATPs offer a breadth of AWS Training options for learners of all levels.
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Explore the top Data and AI certifications for 2025. Be the professional businesses are searching for—get certified today!
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Join an elite community of AWS Certified Developers today.
This AWS-DEV: Developing on AWS course teaches experienced developers how to programmatically interact with AWS services to build web solutions. It guides you through a high-level architectural discussion on resource selection and dives deep into using the AWS Software Development Kits (AWS SDKs) and Command Line Interface (AWS CLI) to build and deploy your cloud applications.
You will build a sample application during this course, learning how to set up permissions to the development environment, adding business logic to process data using AWS core services, configure user authentications, deploy to AWS cloud, and debug to resolve application issues.
This AWS Developer training includes code examples to help you implement the design patterns and solutions discussed in the course. The labs reinforce key course content and help you to implement solutions using the AWS SDK for Python, .NET, and Java, the AWS CLI, and the AWS Management Console.
Trainocate is an AWS Authorized Training Partner as well as the AWS Global Training Partner of the Year 2024 is trusted by AWS to offer, deliver, and/or incorporate official AWS Training, including classroom and digital offerings. Whether your team prefers to learn from live instructors, on-demand courses, or both, ATPs offer a breadth of AWS Training options for learners of all levels.
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Build your future in the AWS Cloud with AWS Certified Solutions Architect – Associate certification.
The AWS-ARC: Architecting on AWS training course is intended for solutions architects, solution-design engineers, and developers seeking an understanding of AWS architecting. In this AWS Architect training course, you will learn to identify services and features to build resilient, secure and highly available IT solutions on the AWS Cloud.
Architectural solutions differ depending on industry, types of applications, and business size.
- AWS Authorized Instructors emphasize best practices using the AWS Well-Architected Framework
- Guides you through the process of designing optimal IT solutions, based on real-life scenarios.
- Modules focus on account security, networking, compute, storage, databases, monitoring, automation, containers, serverless architecture, edge services, and backup and recovery.
At the end of the course, you will practice building a solution and apply what you have learned with confidence.
Invest in your team’s cloud future: Master AWS with 600+ courses, labs, and interactive experiences, elevate your team’s cloud expertise with AWS Skill Builder Team Subscription.
Trainocate is an AWS Authorized Training Partner as well as the AWS Global Training Partner of the Year 2024 is trusted by AWS to offer, deliver, and/or incorporate official AWS Training, including classroom and digital offerings. Whether your team prefers to learn from live instructors, on-demand courses, or both, ATPs offer a breadth of AWS Training options for learners of all levels.
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This AIOps Foundation course aims to cover the origins of AIOps including the history behind the term, patterns that preceded it and the technology context in which it has evolved.
- Gain an understanding of the processes of combining big data analytics, machine learning algorithms, automation, and optimization into a single platform.
- Learn key principles and foundational concepts along with the core technologies of AIOps: big data and machine learning
- Validate understanding of how and why digital transformation, together with the evolution of machine learning, have brought about the rise of AIOps as an indispensable tool in today’s IT Operational landscape.
This foundation course will also provide the student with a solid understanding of the benefits of implementing AIOps in the organization, including common challenges and key steps in ensuring valuable and successful integration of artificial intelligence in the day to day operations of information technology solutions.
Unique and exciting exercises will be used to apply the concepts covered in the course and sample documents, templates, tools, and techniques will be provided to use after the class.
This course positions learners to successfully complete the AIOps Foundation certification exam.
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Microservices and Cloud-Native architectures have been goals of many organizations to help increase speed and agility, but as complexity grows, systems become increasingly challenging to observe. When issues occur, these issues are often di cult to triage and identify the root causes. This course introduces a range of practices for advancing resilience and how to architect end-to-end Observability for Cloud-Native applications. The advantages of building full-stack metrics, events, logs, and distributed tracing are introduced, along with the impact of DevSecOps on Observability and how AIOPs enhance Observability capabilities.
This course also covers how Network and Security Observability plays a key role in building reliability, the key aspects of security operations and automated responses are covered, The course aims to equip participants with the practices, methods, and tools to engage people across the organization involved in Observability by using real-life scenarios and case stories. Upon completion of the course, participants will have tangible takeaways to leverage situations such as implementing MELT models effectively, that fit their organizational context, building distributed tracing and resiliency by design.
The course is developed by leveraging key experts in the fields of telemetry, sources of knowledge and engaging with thought-leaders in the Observability space, and working with organizations who have crossed the chasm of modern Observability to extract real-life best practices. This course positions learners to successfully complete the Observability Foundation certification exam.
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This three-day course is designed to provide VMware vSphere® administrators with the knowledge and hands-on experience in managing VMware Cloud Foundation™ in a hybrid cloud environment and utilizing VMware HCX® for workload migration.
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This three-day training course provides the advanced knowledge, skills, and tools to achieve competency in troubleshooting problems in VMware Cloud Foundation™ infrastructure. In this course, you are exposed to tools and methodologies that help you support and troubleshoot your VMware Cloud Foundation environment.
In addition, various scenarios are presented to you in hands-on student labs and simulations in which you learn to identify, analyze, and solve problems systematically in the VMware Cloud Foundation infrastructure.
