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Artificial Intelligence in - IT & Tech Services

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Objectives

The course highlights AI-driven transformation of IT and tech operations, focusing on workflow automation, coding assistance, model deployment, and enterprise integration. It emphasizes operational efficiency, intelligent service delivery, and secure, governed AI adoption. The program also shows how AI can enhance problem-solving and decision-making across IT service management, DevOps pipelines, and enterprise technology solutions.



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Enhancing Efficiency with AI

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AI Powered Workflows in Excel

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IBM Watson

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Pytorch & Deep Learning

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Amazon SageMaker

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Course Outcome

Leverage Generative AI APIs to build innovative solutions for IT service delivery and support.
Integrate AI-powered coding assistants to accelerate development and reduce manual effort.
Design and deploy machine learning models using scalable, production-ready pipelines.
Automate IT workflows including ticket resolution, documentation, and infrastructure monitoring. and corporate policies.
Implement governance, security, and cost controls for AI usage in IT environments.
Monitor AI system performance and usage effectively to maintain operational integrity.
Deliver real-time AI-enabled IT solutions that enhance productivity and service quality.

Why Mazenet?


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    Expert Faculty

    Our Faculty comprises of 300+ SMEs with many years of experience. All our trainers possess a minimum of 8+ years of experience.

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    Proven Track Record

    We have served over 200+ global corporate clients, consistently maintaining a 99% success rate in meeting training objectives for 300+ technologies with quick turnaround time.

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    Blended Learning

    We provide course content over any platform that our clients prefer. You can choose an exclusive platform or a combination of ILT, VILT, and DLP.

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    Learning Paths

    The learning paths are very defined with clear benchmarks. Quantitative assessments at regular intervals measure the success of the learning program.

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    Case Study

    We have amassed over 10,000 case studies to support training delivery. Candidates will be trained to work on any real-time business vertical immediately after the training.

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    24*7 Global Availability

    We are equipped to conduct training on any day, date or time. We have delivered training pan India, Singapore, North America, Hong Kong, Egypt and Australia.

Delivery Highlights

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    Customized Training Modules

    Training programs are highly flexible with module customizations to suit the requirements of the business units.

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    Certification

    The training can be supplemented with appropriate certifications that are recognized across the industry.

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    Multi-language Support

    Course content can be delivered in English, Spanish, Japanese, Korean or any other language upon request.

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    Personalized Training Reports

    Candidates are assessed individually at regular intervals and are provided unique learning suggestions to suit their learning calibre.

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    Industry-Oriented Training

    Industry-oriented training, completing which, candidates can be immediately deployed for billable projects.

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    Diverse Training Platforms

    Choose from Instructor-Led Training, Virtual Instructor-Led Training, Digital Learning Platform and Blended Training platforms

Contact Us
Ready to get started?
Let's chat.
info@mazenetsolution.com +91 9500929297
          +91 7397723052
Get In Touch With Us


Get Started Today

Course Preview

  • Trace the evolution of AI in technology service delivery.
  • Differentiate Generative AI from traditional machine learning in enterprise IT.
  • Explore common use cases like ticket resolution, code generation, and infrastructure optimization.
  • Get familiar with tools such as OpenAI, GitHub Copilot, Hugging Face, Vertex AI, and Azure ML.

  • Learn basics of large language models (LLMs) and foundation models.
  • Integrate APIs from OpenAI, Azure OpenAI, Claude, and Cohere.
  • Master prompt engineering fundamentals including context, tokens, and system roles.
  • Apply GenAI for summarization, document parsing, and knowledge assistance.
  • Hands-on: create a service bot with OpenAI API and API chaining.

  • Use tools like GitHub Copilot, Tabnine, and Replit Ghostwriter for code completion, optimization, and documentation.
  • Generate test cases and build internal automation snippets.
  • Understand limitations, security risks, and responsible AI usage policies.
  • Hands-on project: build a document-based LLM assistant.
  • Hands-on: refactor code and write tests using Copilot.

  • Follow the machine learning lifecycle: build, train, deploy, and monitor models.
  • Deploy AI models using Flask, FastAPI, or Streamlit.
  • Adopt MLOps with versioning, CI/CD, and cloud deployment (AWS SageMaker, Azure ML, Vertex AI).
  • Use monitoring tools like MLflow, Prometheus, and Grafana.
  • Hands-on: deploy classification model as a REST API.

  • Incorporate GenAI into service desks and knowledge bases.
  • Automate documentation, log analysis, and IT workflows.
  • Embed AI APIs into internal apps or portals.
  • Review case studies such as GenAI in L1/L2 support, DevOps bots, and ITSM automation.

  • Manage token usage and quotas for GenAI APIs.
  • Ensure API security with role-based access controls.
  • Follow data handling policies and audit API logs.
  • Monitor AI usage and optimize costs within IT environments.