MLOps & AIOps Certification Training in Electronic City Bangalore

Curriculum

86 Sections
394 Lessons
10 Weeks

Expand all sectionsCollapse all sections

DevOps Fundamentals
0
Introduction to DevOps
5
- 2.1
  DevOps philosophy and culture
- 2.2
  DevOps lifecycle and practices
- 2.3
  Business value and ROI of DevOps
- 2.4
  DevOps vs traditional development
- 2.5
  DevOps roles and team structures
Linux Fundamentals for DevOps
5
- 3.1
  Essential Linux commands and shell scripting
- 3.2
  File system and process management
- 3.3
  User permissions and security basics
- 3.4
  Networking fundamentals
- 3.5
  Automation with bash scripts
Version Control with Git
5
- 4.1
  Git fundamentals and workflow
- 4.2
  Branching strategies (GitFlow, trunk-based)
- 4.3
  Pull requests and code reviews
- 4.4
  Git hooks and automation
- 4.5
  GitHub/GitLab features for collaboration
CI/CD Fundamentals
5
- 5.1
  Continuous Integration principles
- 5.2
  Continuous Delivery/Deployment concepts
- 5.3
  Pipeline design patterns
- 5.4
  CI/CD tools overview (Jenkins, GitHub Actions)
- 5.5
  Hands-on: Building your first CI/CD pipeline
Infrastructure as Code (IaC)
5
- 6.1
  IaC principles and benefits
- 6.2
  Configuration management with Ansible
- 6.3
  Infrastructure provisioning with Terraform
- 6.4
  Cloud-specific IaC (CloudFormation, ARM templates)
- 6.5
  Hands-on: Automating infrastructure deployment
Containerization with Docker
5
- 7.1
  Container concepts and architecture
- 7.2
  Dockerfile best practices
- 7.3
  Container networking and storage
- 7.4
  Container registries and management
- 7.5
  Hands-on: Containerizing applications
Container Orchestration with Kubernetes
5
- 8.1
  Kubernetes architecture and components
- 8.2
  Deployments, Services, and Pods
- 8.3
  ConfigMaps and Secrets
- 8.4
  Kubernetes networking and storage
- 8.5
  Hands-on: Deploying applications on Kubernetes
Cloud Computing Fundamentals
5
- 9.1
  Major cloud providers comparison (AWS, Azure, GCP)
- 9.2
  Cloud service models (IaaS, PaaS, SaaS)
- 9.3
  Cloud networking and security
- 9.4
  Cost optimization strategies
- 9.5
  Multi-cloud and hybrid cloud approaches
Monitoring and Observability
5
- 10.1
  Monitoring principles and tools
- 10.2
  Prometheus and Grafana setup
- 10.3
  Log aggregation with ELK/EFK stack
- 10.4
  Distributed tracing with Jaeger/Zipkin
- 10.5
  Hands-on: Building a comprehensive monitoring stack
DevSecOps Integration
5
- 11.1
  Security integration in DevOps pipeline
- 11.2
  Vulnerability scanning
- 11.3
  Compliance as code
- 11.4
  Secret management
- 11.5
  Hands-on: Implementing security in CI/CD
MLOps
0
Introduction to MLOps
5
- 13.1
  ML lifecycle and MLOps concept
- 13.2
  MLOps vs DevOps: Key differences
- 13.3
  MLOps maturity model
- 13.4
  Challenges in ML system deployment
- 13.5
  Industry use cases and success stories
Data Engineering for ML
5
- 14.1
  Data collection and ingestion pipelines
- 14.2
  Data validation and quality checks
- 14.3
  Feature engineering at scale
- 14.4
  Feature stores (Feast, Hopsworks)
- 14.5
  Hands-on: Building data pipelines with Airflow
ML Experimentation & Tracking
5
- 15.1
  Experiment management fundamentals
- 15.2
  Tracking with MLflow and Weights & Biases
- 15.3
  Hyperparameter optimization techniques
- 15.4
  Reproducible ML research
- 15.5
  Hands-on: Setting up experiment tracking
ML Version Control
5
- 16.1
  Versioning ML code with Git
- 16.2
  Data versioning with DVC
- 16.3
  Model versioning strategies
- 16.4
  Experiment reproducibility
- 16.5
  Hands-on: Implementing ML versioning
ML Model Packaging & Deployment
5
- 17.1
  Model serialization formats (ONNX, SavedModel)
- 17.2
  Model serving with RESTful APIs (FastAPI)
- 17.3
  Batch inference systems
- 17.4
  Edge deployment strategies
- 17.5
  Hands-on: Deploying models as services
ML CI/CD Pipelines
5
- 18.1
  ML-specific CI/CD challenges
- 18.2
  Testing ML models and components
- 18.3
  Automating ML workflows
- 18.4
  Continuous training and deployment
- 18.5
  Hands-on: Building an ML CI/CD pipeline
Model Monitoring & Management
5
- 19.1
  Performance monitoring metrics
- 19.2
  Data drift and concept drift detection
- 19.3
  Model retraining strategies
- 19.4
  A/B testing for ML models
- 19.5
  Hands-on: Setting up model monitoring
ML Infrastructure Orchestration
5
- 20.1
  ML workflows with Kubeflow
- 20.2
  Managed ML platforms (SageMaker, Vertex AI)
- 20.3
  Resource optimization for ML
- 20.4
  Scaling ML training and inference
- 20.5
  Hands-on: Orchestrating ML workflows
ML on Edge and Mobile
5
- 21.1
  Edge computing for ML
- 21.2
  Model optimization for edge devices
- 21.3
  TensorFlow Lite and PyTorch Mobile
- 21.4
  Federated learning concepts
- 21.5
  Hands-on: Deploying models to edge devices
MLOps for Computer Vision and NLP
5
- 22.1
  Specific challenges for CV/NLP models
- 22.2
  Data pipeline considerations
- 22.3
  Model optimization techniques
- 22.4
  Deployment architectures
- 22.5
  Hands-on: End-to-end CV/NLP deployment
LLMOps
0
Introduction to LLMOps
5
- 24.1
  Large Language Model fundamentals
- 24.2
  LLMOps vs traditional MLOps
- 24.3
  LLM lifecycle management
- 24.4
  LLM deployment challenges
- 24.5
  Business applications of LLMs
Foundation Model Management
5
- 25.1
  Open-source vs proprietary LLMs
- 25.2
  Model selection criteria
- 25.3
  Hosting and serving large models
- 25.4
  Model weight management
- 25.5
  Hands-on: Setting up a foundation model
Prompt Engineering & Management
5
- 26.1
  Prompt engineering fundamentals and patterns
- 26.2
  Prompt versioning and templates
- 26.3
  Testing and evaluating prompts
- 26.4
  Prompt management systems
- 26.5
  Hands-on: Building a prompt management workflow
LLM Fine-tuning & Customization
5
- 27.1
  Fine-tuning methodologies
- 27.2
  Parameter-efficient techniques (LoRA, QLoRA)
- 27.3
  Domain adaptation strategies
- 27.4
  Evaluation of fine-tuned models
- 27.5
  Hands-on: Fine-tuning LLMs for specific tasks
Retrieval Augmented Generation (RAG)
5
- 28.1
  RAG architecture and components
- 28.2
  Document retrieval systems
- 28.3
  Vector databases and embeddings
- 28.4
  Hybrid search techniques
- 28.5
  Hands-on: Building a RAG system
LLM Deployment Architectures
5
- 29.1
  Inference optimization techniques
- 29.2
  Quantization and distillation
- 29.3
  Caching strategies
- 29.4
  Scaling and load balancing
- 29.5
  Hands-on: Deploying optimized LLMs
LLM Evaluation & Testing
5
- 30.1
  Evaluation metrics for LLMs
- 30.2
  Red-teaming and adversarial testing
- 30.3
  Automated evaluation frameworks
- 30.4
  Continuous evaluation pipelines
- 30.5
  Hands-on: Building an LLM evaluation system
LLM Observability & Monitoring
5
- 31.1
  Output quality monitoring
- 31.2
  Response time and cost tracking
- 31.3
  User feedback integration
- 31.4
  Anomaly detection for LLMs
- 31.5
  Hands-on: Implementing LLM monitoring
Responsible LLM Implementation
5
- 32.1
  Alignment techniques
- 32.2
  Content filtering systems
- 32.3
  Explainability and transparency
- 32.4
  Ethical considerations and governance
- 32.5
  Hands-on: Implementing LLM guardrails
Multimodal LLMs
5
- 33.1
  Vision-language models
- 33.2
  Audio-text integration
- 33.3
  Multimodal embeddings
- 33.4
  Multimodal fine-tuning strategies
- 33.5
  Hands-on: Working with multimodal LLMs
AI Fundamentals
0
Introduction to AI & Machine Learning
5
- 35.1
  AI concepts and history
- 35.2
  Types of machine learning
- 35.3
  Deep learning fundamentals
- 35.4
  AI ethics and responsible development
- 35.5
  Current state of AI industry
Mathematics for AI
5
- 36.1
  Linear algebra fundamentals
- 36.2
  Probability and statistics
- 36.3
  Calculus for optimization
- 36.4
  Information theory basics
- 36.5
  Hands-on: Math implementation in Python
Machine Learning Fundamentals
5
- 37.1
  Supervised, unsupervised, and reinforcement learning
- 37.2
  Feature engineering basics
- 37.3
  Model selection and evaluation
- 37.4
  Common ML algorithms
- 37.5
  Hands-on: Building basic ML models
Deep Learning Essentials
5
- 38.1
  Neural network architecture
- 38.2
  Backpropagation and optimization
- 38.3
  Convolutional neural networks
- 38.4
  Recurrent neural networks
- 38.5
  Hands-on: Building deep learning models
Transformer Architecture
5
- 39.1
  Attention mechanisms
- 39.2
  Self-attention and multi-head attention
- 39.3
  Encoder-decoder architecture
- 39.4
  Positional encodings
- 39.5
  Hands-on: Implementing transformer models
Natural Language Processing
5
- 40.1
  Text preprocessing techniques
- 40.2
  Word embeddings and language models
- 40.3
  Sequence modeling for text
- 40.4
  Transformers for NLP
- 40.5
  Hands-on: Building NLP applications
Computer Vision Basics
5
- 41.1
  Image processing fundamentals
- 41.2
  Object detection and recognition
- 41.3
  Image segmentation
- 41.4
  Vision transformers
- 41.5
  Hands-on: Building CV applications
Reinforcement Learning
5
- 42.1
  RL fundamentals and terminology
- 42.2
  Value-based methods
- 42.3
  Policy gradient methods
- 42.4
  Deep reinforcement learning
- 42.5
  Hands-on: Building RL agents
AI Tools & Frameworks
5
- 43.1
  TensorFlow and Keras
- 43.2
  PyTorch ecosystem
- 43.3
  Hugging Face transformers
- 43.4
  JAX for research
- 43.5
  Hands-on: Working with AI frameworks
AI Ethics & Governance
5
- 44.1
  Bias and fairness in AI
- 44.2
  Privacy considerations
- 44.3
  Explainable AI techniques
- 44.4
  Regulatory frameworks
- 44.5
  Hands-on: Implementing ethical AI practices
AIOps
0
Introduction to AIOps
5
- 46.1
  AIOps concept and evolution
- 46.2
  AIOps vs traditional IT operations
- 46.3
  Business value of AIOps
- 46.4
  AIOps implementation challenges
- 46.5
  AIOps maturity model
IT Operations Data Collection
5
- 47.1
  Telemetry data collection frameworks
- 47.2
  Log aggregation systems
- 47.3
  Metrics collection platforms
- 47.4
  Data integration strategies
- 47.5
  Hands-on: Building data collection pipelines
AIOps Data Processing
5
- 48.1
  Data normalization techniques
- 48.2
  Time-series processing
- 48.3
  Event correlation methods
- 48.4
  Anomaly detection preprocessing
- 48.5
  Hands-on: Processing operations data
Anomaly Detection Systems
5
- 49.1
  Statistical anomaly detection
- 49.2
  ML-based anomaly detection
- 49.3
  Time-series anomaly detection
- 49.4
  Multivariate anomaly detection
- 49.5
  Hands-on: Building anomaly detection models
Predictive Analytics for IT
5
- 50.1
  Failure prediction models
- 50.2
  Capacity planning algorithms
- 50.3
  SLA prediction techniques
- 50.4
  Resource optimization models
- 50.5
  Hands-on: Building predictive models for IT
Root Cause Analysis & Remediation
5
- 51.1
  Automated RCA techniques
- 51.2
  Causal inference in IT systems
- 51.3
  Event correlation for troubleshooting
- 51.4
  Automated remediation frameworks
- 51.5
  Hands-on: Building RCA systems
AIOps Platforms & Integration
5
- 52.1
  Commercial AIOps platforms
- 52.2
  Open-source AIOps tools
- 52.3
  ITSM integration strategies
- 52.4
  Incident management automation
- 52.5
  Hands-on: Implementing an AIOps platform
Self-Healing Systems
5
- 53.1
  Automated remediation patterns
- 53.2
  Self-healing infrastructure
- 53.3
  Chaos engineering practices
- 53.4
  Resilience testing frameworks
- 53.5
  Hands-on: Building self-healing capabilities
Cloud-Native AIOps
5
- 54.1
  Kubernetes observability
- 54.2
  Microservices monitoring
- 54.3
  Serverless function monitoring
- 54.4
  Container health management
- 54.5
  Hands-on: Cloud-native AIOps implementation
AIOps & DevSecOps Integration
5
- 55.1
  Security monitoring with AIOps
- 55.2
  Threat detection models
- 55.3
  Compliance automation
- 55.4
  Security incident response
- 55.5
  Hands-on: Implementing SecOps with AIOps
Generative AI
0
Introduction to Generative AI
5
- 57.1
  Generative vs discriminative models
- 57.2
  Types of generative models
- 57.3
  Applications of generative AI
- 57.4
  Business use cases
- 57.5
  Ethical considerations
Foundation Models
5
- 58.1
  Pre-training methodologies
- 58.2
  Transfer learning concepts
- 58.3
  Scaling laws and emergent abilities
- 58.4
  Foundation model ecosystems
- 58.5
  Hands-on: Working with foundation models
Text Generation Models
5
- 59.1
  Language model architecture
- 59.2
  GPT and other autoregressive models
- 59.3
  Text generation techniques
- 59.4
  Control mechanisms for text generation
- 59.5
  Hands-on: Building text generation applications
Image Generation
5
- 60.1
  GAN architecture and training
- 60.2
  Diffusion models (DALL-E, Stable Diffusion)
- 60.3
  Text-to-image systems
- 60.4
  Style transfer and image manipulation
- 60.5
  Hands-on: Building image generation applications
Audio & Speech Generation
5
- 61.1
  Speech synthesis technologies
- 61.2
  Music generation models
- 61.3
  Audio style transfer
- 61.4
  Voice cloning considerations
- 61.5
  Hands-on: Building audio generation applications
Video Generation
5
- 62.1
  Text-to-video systems
- 62.2
  Video diffusion models
- 62.3
  Motion synthesis techniques
- 62.4
  Temporal consistency methods
- 62.5
  Hands-on: Building video generation applications
Multimodal Generation
5
- 63.1
  Cross-modal generation techniques
- 63.2
  Text-to-3D systems
- 63.3
  Multimodal understanding
- 63.4
  Combined generative pipelines
- 63.5
  Hands-on: Building multimodal applications
Generative AI Deployment
5
- 64.1
  Serving generative models efficiently
- 64.2
  Latency optimization
- 64.3
  Cost management for generation
- 64.4
  User feedback integration
- 64.5
  Hands-on: Deploying generative AI services
Generative AI for Business
5
- 65.1
  Content creation workflows
- 65.2
  Personalization systems
- 65.3
  Creative assistance tools
- 65.4
  Enterprise integration patterns
- 65.5
  Hands-on: Building business applications
Responsible Generative AI
5
- 66.1
  Bias detection and mitigation
- 66.2
  Content filtering systems
- 66.3
  Attribution and provenance
- 66.4
  Copyright and ownership issues
- 66.5
  Hands-on: Implementing responsible AI guardrails
AI Agents
0
Introduction to AI Agents
5
- 68.1
  Agent architecture and components
- 68.2
  Types of AI agents
- 68.3
  Agent capabilities and limitations
- 68.4
  Business applications of agents
- 68.5
  Ethical considerations for autonomous systems
Agent Development Frameworks
5
- 69.1
  LangChain for agent development
- 69.2
  AutoGPT architecture
- 69.3
  BabyAGI implementation
- 69.4
  CrewAI for multi-agent systems
- 69.5
  Hands-on: Building your first AI agent
Tool Use & Function Calling
5
- 70.1
  Function calling architecture
- 70.2
  Tool libraries and integration
- 70.3
  API connectivity for agents
- 70.4
  Tool selection reasoning
- 70.5
  Hands-on: Building tool-using agents
Agent Memory Systems
5
- 71.1
  Short-term and working memory
- 71.2
  Long-term knowledge management
- 71.3
  Vector databases for agent memory
- 71.4
  Memory retrieval strategies
- 71.5
  Hands-on: Implementing agent memory
Planning & Reasoning
6
- 72.1
  Planning algorithms for agents
- 72.2
  Chain-of-thought reasoning
- 72.3
  Tree of thought exploration
- 72.4
  Task decomposition techniques
- 72.5
  Goal-oriented behavior
- 72.6
  Hands-on: Building reasoning systems
Multi-Agent Systems
6
- 73.1
  Multi-agent architectures
- 73.2
  Communication protocols
- 73.3
  Role specialization
- 73.4
  Collaborative problem-solving
- 73.5
  Emergent behaviors
- 73.6
  Hands-on: Implementing multi-agent systems
Autonomous Decision Making
5
- 74.1
  Decision theory for agents
- 74.2
  Utility functions and preferences
- 74.3
  Risk assessment and management
- 74.4
  Feedback incorporation
- 74.5
  Hands-on: Building decision-making agents
Agent Evaluation & Testing
6
- 75.1
  Evaluation frameworks for agents
- 75.2
  Benchmarking agent performance
- 75.3
  Simulation environments
- 75.4
  User feedback integration
- 75.5
  Adversarial testing
- 75.6
  Hands-on: Testing agent capabilities
Human-Agent Interaction
5
- 76.1
  Conversational interfaces
- 76.2
  User experience design for agents
- 76.3
  Explainability for agent actions
- 76.4
  Trust building mechanisms
- 76.5
  Hands-on: Designing human-agent interactions
Enterprise Agent Deployment
6
- 77.1
  Agent security considerations
- 77.2
  Scalable agent infrastructure
- 77.3
  Monitoring agent behavior
- 77.4
  Continuous improvement frameworks
- 77.5
  Governance and compliance
- 77.6
  Hands-on: Deploying enterprise-grade agents
Real-World Projects & Job Preparation
0
MLOps End-to-End Project
5
- 79.1
  Building a complete ML system with CI/CD
- 79.2
  Data pipeline construction
- 79.3
  Model training and evaluation automation
- 79.4
  Deployment and monitoring implementation
- 79.5
  Documentation and presentation
LLMOps Production Project
5
- 80.1
  Deploying a production-ready LLM application
- 80.2
  Fine-tuning and optimization
- 80.3
  Prompt management system
- 80.4
  Monitoring and evaluation pipeline
- 80.5
  Cost optimization strategies
AIOps Implementation Project
5
- 81.1
  Building an AIOps system for IT infrastructure
- 81.2
  Anomaly detection implementation
- 81.3
  Predictive maintenance system
- 81.4
  Integration with ITSM tools
- 81.5
  ROI calculation and business value assessment
AI Agent Solution Project
5
- 82.1
  Developing an enterprise AI agent
- 82.2
  Tool integration for specific domains
- 82.3
  Multi-agent collaboration implementation
- 82.4
  User interface and interaction design
- 82.5
  Performance evaluation and optimization
Industry-Specific Implementation
5
- 83.1
  Vertical-specific AI implementation
- 83.2
  Custom solutions for industry challenges
- 83.3
  Compliance and regulation considerations
- 83.4
  Business process integration
- 83.5
  ROI calculation and stakeholder presentation
Future-Proofing Skills
5
- 84.1
  Emerging technologies and trends
- 84.2
  Research paper analysis and implementation
- 84.3
  Community contribution and open source
- 84.4
  Continuous learning strategies
- 84.5
  Building a personal development roadmap
Job Preparation
5
- 85.1
  Portfolio development
- 85.2
  Resume and LinkedIn optimization
- 85.3
  Technical interview preparation
- 85.4
  System design interview practice
- 85.5
  Salary negotiation and career progression
Industry Mentorship
5
- 86.1
  Sessions with industry practitioners
- 86.2
  Career path guidance
- 86.3
  Networking strategies
- 86.4
  Professional development planning
- 86.5
  Job search strategies and support

Want Us to Email you About Special Offers & Updates?

Company

Platform

Get in touch

#219, First Floor, Nagalaya, 3rd Cross Road, Neeladri Nagar, Electronics City Phase 1, Electronic City, Bengaluru, Karnataka 560100

+91-9513216462

info@emexotechnologies.com

Get your free demo class

Get your free demo class

Get your free demo class

Get your free demo class

Get your free demo class

Get Your Free Demo Class

Get your free demo class

Get your free demo class

Get your free demo class

Get your free demo class

Get your free demo class

Want Us to Email you About Special Offers & Updates?

Company

Platform

Resources

Get in touch

Get your free demo class

Get your free demo class

Get your free demo class

Get your free demo class

Get your free demo class

Get Your Free Demo Class

Get your free demo class

Get your free demo class

Get your free demo class

Get your free demo class

Get your free demo class

MLOps & AIOps Certification Training

Curriculum

Want Us to Email you About Special Offers & Updates?

Company

Platform

Resources

Get in touch

Modal title