AIOps Course

CHATOPS TRACK

• Chatbot Framework (RASA, Microsoft BOT Framework)
• API Development (Microservice, Development, Deployment, Unit Test Frameworks)
• Entity, Intent, Pattern, Regular Expression based Knowledge Extractions
• ML Model, Deep Learning
• LangChain, LLM , LlamaIndex, NER
• Python, Nodejs Library (NLP Libraries, Data Visualization, ETL Library)
• Vector Database, NOSQL Database, SQL Database
• Deployment, Jenkins, CI/CD, Docker, Container, Kubernetes
• Reporting Tools, PowerBI, ReactJS, Apache Superset.
• JavaScript, HTML, CSS, REST API, Authentications.
• Hyperscalers platform (AWS, IBM, AZURE)
• Presentation, Business Demo.
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GenAI for Leaders

• Build a Generative AI Strategy with Project Roadmap, strategic outcome and ROI projections.
• Foundation for LLM, FM, GENAI, Transformer, AI
• Prompt Engineering, RAG, Data Privacy and AI Ethics
• Industry specific and Real-world use cases showcasing the transformative impact of AI and Generative AI.
• GenAI supported use cases (Top 10) in NLP, Logs, Text
• Enhancing the customer experience with GenAI use cases.
• Business operations and employee productivity improvements using GENAI in day 1, day 2 activities.
• Hands-on Experience & Model Evaluation and Performance Metrics.
• Integration and Deployment & Future Trends and Innovation.
• Create Business Impact with AI backed decisions evaluation framework.
• Tools and platforms used for Generative AI projects and initiatives.
• Navigating potential challenges, biases, and risks associated with Generative AI.
• Comparison with hyperscalers providers GenAI top features
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GenAI for Developers (Hands-On)

• ML Model, Deep Learning
• Python, Nodejs Library (NLP Libraries, Data Visualization, ETL Library)
• Vector Database, NOSQL Database, SQL Database
• LangChain, LLM , LlamaIndex, NER
• Foundation for LLM, FM, GENAI, Transformer, AI
• Prompt Engineering, RAG, Data Privacy and AI Ethics
• Train, deploy, and productionalize ML models at scale with Vertex AI, Microsoft Machine Learning Studio, Watsonx.ai
• Tensorflow, pytorch , Tensorboard, Keras, Hugging Face Models,
• MLOps, Deployment, CI/CD pipelines for GenAI Models.
• Hyperscalers platform (AWS, IBM, AZURE)
• Presentation, Business Demo.

Note:

1. GenAI for Developers
   1. SME should have familiar with Development Tools (Jupiter, VSCode, Python, Test Framework etc.)
   1. All training should be lab-based case study.
2. Chatbot Developer
   1. SME should have familiar with Development Tools and Basic Tool concepts. (Chatbot, Chatops, Virtual Agents, NLP, ML Basic)
   1. All training should be lab-based case study.