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MLOps & AIOps

Course Overview

The MLOps & AIOps Online Training program is designed to bridge the gap between machine learning development and operations, while introducing you to AI-driven IT operations. This course helps you automate, monitor, and deploy ML models efficiently using MLOps and manage infrastructure intelligently using AIOps. Delivered by industry experts, this training includes hands-on labs, real-time projects, and essential tool integrations.

Learn software skills with real experts, either in live classes with videos or without videos, whichever suits you best.

Description

MLOps (Machine Learning Operations) and AIOps (Artificial Intelligence for IT Operations) are transforming the way enterprises manage and scale AI/ML initiatives. This course provides in-depth knowledge and hands-on experience in automating the end-to-end lifecycle of machine learning models, including continuous integration, deployment, testing, and monitoring. You will also explore how AIOps leverages big data and ML to enhance IT operations, improve uptime, and reduce manual interventions.

Throughout the training, learners will work with leading tools like MLflow, Kubeflow, TensorFlow Extended (TFX), Jenkins, Docker, Kubernetes, Prometheus, Grafana, ELK stack, and more to deploy intelligent pipelines and manage ML and IT workflows efficiently.

Course Objectives

By the end of this MLOps & AIOps Online Training, you will be able to:

  • Understand the concepts and lifecycle of MLOps & AIOps

  • Implement CI/CD pipelines for ML models

  • Automate data workflows and model training

  • Monitor, deploy, and manage ML models in production

  • Use tools like MLflow, Kubeflow, TFX for MLOps

  • Apply AIOps practices to enhance IT observability and root cause analysis

  • Manage and deploy models using Docker and Kubernetes

  • Work with logging, monitoring, and alerting systems using ELK, Prometheus & Grafana

  • Develop real-time use cases and integrate ML models into scalable systems

Prerequisites

  • To get the most out of this training, learners should have:

    • Basic understanding of Python and Machine Learning

    • Familiarity with DevOps concepts (optional but beneficial)

    • Knowledge of cloud platforms like AWS, Azure, or GCP (recommended)

    • Prior experience in data science or IT operations is a plus

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Course Curriculum

  • Introduction to DevOps
    • DevOps philosophy and culture
    • DevOps lifecycle and practices
    • Business value and ROI of DevOps
    • DevOps vs traditional development
    • DevOps roles and team structures
  • Linux Fundamentals for DevOps
    • Essential Linux commands and shell scripting
    • File system and process management
    • User permissions and security basics
    • Networking fundamentals
    • Automation with bash scripts
  • Version Control with Git
    • Git fundamentals and workflow
    • Branching strategies (GitFlow, trunk-based)
    • Pull requests and code reviews
    • Git hooks and automation
    • GitHub/GitLab features for collaboration
  • CI/CD Fundamentals
    • Continuous Integration principles
    • Continuous Delivery/Deployment concepts
    • Pipeline design patterns
    • CI/CD tools overview (Jenkins, GitHub Actions)
    • Hands-on: Building your first CI/CD pipeline
  • Infrastructure as Code (IaC)
    • IaC principles and benefits
    • Configuration management with Ansible
    • Infrastructure provisioning with Terraform
    • Cloud-specific IaC (CloudFormation, ARM templates)
    • Hands-on: Automating infrastructure deployment
  • Containerization with Docker
    • Container concepts and architecture
    • Dockerfile best practices
    • Container networking and storage
    • Container registries and management
    • Hands-on: Containerizing applications
  • Container Orchestration with Kubernetes
    • Kubernetes architecture and components
    • Deployments, Services, and Pods
    • ConfigMaps and Secrets
    • Kubernetes networking and storage
    • Hands-on: Deploying applications on Kubernetes
  • Cloud Computing Fundamentals
    • Major cloud providers comparison (AWS, Azure, GCP)
    • Cloud service models (IaaS, PaaS, SaaS)
    • Cloud networking and security
    • Cost optimization strategies
    • Multi-cloud and hybrid cloud approaches
  • Monitoring and Observability
    • Monitoring principles and tools
    • Prometheus and Grafana setup
    • Log aggregation with ELK/EFK stack
    • Distributed tracing with Jaeger/Zipkin
    • Hands-on: Building a comprehensive monitoring stack
  • DevSecOps Integration
    • Security integration in DevOps pipeline
    • Vulnerability scanning
    • Compliance as code
    • Secret management
    • Hands-on: Implementing security in CI/CD

  • Introduction to MLOps
    • ML lifecycle and MLOps concept
    • MLOps vs DevOps: Key differences
    • MLOps maturity model
    • Challenges in ML system deployment
    • Industry use cases and success stories
  • Data Engineering for ML
    • Data collection and ingestion pipelines
    • Data validation and quality checks
    • Feature engineering at scale
    • Feature stores (Feast, Hopsworks)
    • Hands-on: Building data pipelines with Airflow
  • ML Experimentation & Tracking
    • Experiment management fundamentals
    • Tracking with MLflow and Weights & Biases
    • Hyperparameter optimization techniques
    • Reproducible ML research
    • Hands-on: Setting up experiment tracking
  • ML Version Control
    • Versioning ML code with Git
    • Data versioning with DVC
    • Model versioning strategies
    • Experiment reproducibility
    • Hands-on: Implementing ML versioning
  • ML Model Packaging & Deployment
    • Model serialization formats (ONNX, SavedModel)
    • Model serving with RESTful APIs (FastAPI)
    • Batch inference systems
    • Edge deployment strategies
    • Hands-on: Deploying models as services
  • ML CI/CD Pipelines
    • ML-specific CI/CD challenges
    • Testing ML models and components
    • Automating ML workflows
    • Continuous training and deployment
    • Hands-on: Building an ML CI/CD pipeline
  • Model Monitoring & Management
    • Performance monitoring metrics
    • Data drift and concept drift detection
    • Model retraining strategies
    • A/B testing for ML models
    • Hands-on: Setting up model monitoring
  • ML Infrastructure Orchestration
    • ML workflows with Kubeflow
    • Managed ML platforms (SageMaker, Vertex AI)
    • Resource optimization for ML
    • Scaling ML training and inference
    • Hands-on: Orchestrating ML workflows
  • ML on Edge and Mobile
    • Edge computing for ML
    • Model optimization for edge devices
    • TensorFlow Lite and PyTorch Mobile
    • Federated learning concepts
    • Hands-on: Deploying models to edge devices
  • MLOps for Computer Vision and NLP
    • Specific challenges for CV/NLP models
    • Data pipeline considerations
    • Model optimization techniques
    • Deployment architectures
    • Hands-on: End-to-end CV/NLP deployment

  • Introduction to LLMOps
    • Large Language Model fundamentals
    • LLMOps vs traditional MLOps
    • LLM lifecycle management
    • LLM deployment challenges
    • Business applications of LLMs
  • Foundation Model Management
    • Open-source vs proprietary LLMs
    • Model selection criteria
    • Hosting and serving large models
    • Model weight management
    • Hands-on: Setting up a foundation model
  • Prompt Engineering & Management
    • Prompt engineering fundamentals and patterns
    • Prompt versioning and templates
    • Testing and evaluating prompts
    • Prompt management systems
    • Hands-on: Building a prompt management workflow
  • LLM Fine-tuning & Customization
    • Fine-tuning methodologies
    • Parameter-efficient techniques (LoRA, QLoRA)
    • Domain adaptation strategies
    • Evaluation of fine-tuned models
    • Hands-on: Fine-tuning LLMs for specific tasks
  • Retrieval Augmented Generation (RAG)
    • RAG architecture and components
    • Document retrieval systems
    • Vector databases and embeddings
    • Hybrid search techniques
    • Hands-on: Building a RAG system
  • LLM Deployment Architectures
    • Inference optimization techniques
    • Quantization and distillation
    • Caching strategies
    • Scaling and load balancing
    • Hands-on: Deploying optimized LLMs
  • LLM Evaluation & Testing
    • Evaluation metrics for LLMs
    • Red-teaming and adversarial testing
    • Automated evaluation frameworks
    • Continuous evaluation pipelines
    • Hands-on: Building an LLM evaluation system
  • LLM Observability & Monitoring
    • Output quality monitoring
    • Response time and cost tracking
    • User feedback integration
    • Anomaly detection for LLMs
    • Hands-on: Implementing LLM monitoring
  • Responsible LLM Implementation
    • Alignment techniques
    • Content filtering systems
    • Explainability and transparency
    • Ethical considerations and governance
    • Hands-on: Implementing LLM guardrails
  • Multimodal LLMs
    • Vision-language models
    • Audio-text integration
    • Multimodal embeddings
    • Multimodal fine-tuning strategies
    • Hands-on: Working with multimodal LLMs

  • Introduction to AI & Machine Learning
    • AI concepts and history
    • Types of machine learning
    • Deep learning fundamentals
    • AI ethics and responsible development
    • Current state of AI industry
  • Mathematics for AI
    • Linear algebra fundamentals
    • Probability and statistics
    • Calculus for optimization
    • Information theory basics
    • Hands-on: Math implementation in Python
  • Machine Learning Fundamentals
    • Supervised, unsupervised, and reinforcement learning
    • Feature engineering basics
    • Model selection and evaluation
    • Common ML algorithms
    • Hands-on: Building basic ML models
  • Deep Learning Essentials
    • Neural network architecture
    • Backpropagation and optimization
    • Convolutional neural networks
    • Recurrent neural networks
    • Hands-on: Building deep learning models
  • Transformer Architecture
    • Attention mechanisms
    • Self-attention and multi-head attention
    • Encoder-decoder architecture
    • Positional encodings
    • Hands-on: Implementing transformer models
  • Natural Language Processing
    • Text preprocessing techniques
    • Word embeddings and language models
    • Sequence modeling for text
    • Transformers for NLP
    • Hands-on: Building NLP applications
  • Computer Vision Basics
    • Image processing fundamentals
    • Object detection and recognition
    • Image segmentation
    • Vision transformers
    • Hands-on: Building CV applications
  • Reinforcement Learning
    • RL fundamentals and terminology
    • Value-based methods
    • Policy gradient methods
    • Deep reinforcement learning
    • Hands-on: Building RL agents
  • AI Tools & Frameworks
    • TensorFlow and Keras
    • PyTorch ecosystem
    • Hugging Face transformers
    • JAX for research
    • Hands-on: Working with AI frameworks
  • AI Ethics & Governance
    • Bias and fairness in AI
    • Privacy considerations
    • Explainable AI techniques
    • Regulatory frameworks
    • Hands-on: Implementing ethical AI practices

  • Introduction to AIOps
    • AIOps concept and evolution
    • AIOps vs traditional IT operations
    • Business value of AIOps
    • AIOps implementation challenges
    • AIOps maturity model
  • IT Operations Data Collection
    • Telemetry data collection frameworks
    • Log aggregation systems
    • Metrics collection platforms
    • Data integration strategies
    • Hands-on: Building data collection pipelines
  • AIOps Data Processing
    • Data normalization techniques
    • Time-series processing
    • Event correlation methods
    • Anomaly detection preprocessing
    • Hands-on: Processing operations data
  • Anomaly Detection Systems
    • Statistical anomaly detection
    • ML-based anomaly detection
    • Time-series anomaly detection
    • Multivariate anomaly detection
    • Hands-on: Building anomaly detection models
  • Predictive Analytics for IT
    • Failure prediction models
    • Capacity planning algorithms
    • SLA prediction techniques
    • Resource optimization models
    • Hands-on: Building predictive models for IT
  • Root Cause Analysis & Remediation
    • Automated RCA techniques
    • Causal inference in IT systems
    • Event correlation for troubleshooting
    • Automated remediation frameworks
    • Hands-on: Building RCA systems
  • AIOps Platforms & Integration
    • Commercial AIOps platforms
    • Open-source AIOps tools
    • ITSM integration strategies
    • Incident management automation
    • Hands-on: Implementing an AIOps platform
  • Self-Healing Systems
    • Automated remediation patterns
    • Self-healing infrastructure
    • Chaos engineering practices
    • Resilience testing frameworks
    • Hands-on: Building self-healing capabilities
  • Cloud-Native AIOps
    • Kubernetes observability
    • Microservices monitoring
    • Serverless function monitoring
    • Container health management
    • Hands-on: Cloud-native AIOps implementation
  • AIOps & DevSecOps Integration
    • Security monitoring with AIOps
    • Threat detection models
    • Compliance automation
    • Security incident response
    • Hands-on: Implementing SecOps with AIOps

  • Introduction to Generative AI
    • Generative vs discriminative models
    • Types of generative models
    • Applications of generative AI
    • Business use cases
    • Ethical considerations
  • Foundation Models
    • Pre-training methodologies
    • Transfer learning concepts
    • Scaling laws and emergent abilities
    • Foundation model ecosystems
    • Hands-on: Working with foundation models
  • Text Generation Models
    • Language model architecture
    • GPT and other autoregressive models
    • Text generation techniques
    • Control mechanisms for text generation
    • Hands-on: Building text generation applications
  • Image Generation
    • GAN architecture and training
    • Diffusion models (DALL-E, Stable Diffusion)
    • Text-to-image systems
    • Style transfer and image manipulation
    • Hands-on: Building image generation applications
  • Audio & Speech Generation
    • Speech synthesis technologies
    • Music generation models
    • Audio style transfer
    • Voice cloning considerations
    • Hands-on: Building audio generation applications
  • Video Generation
    • Text-to-video systems
    • Video diffusion models
    • Motion synthesis techniques
    • Temporal consistency methods
    • Hands-on: Building video generation applications
  • Multimodal Generation
    • Cross-modal generation techniques
    • Text-to-3D systems
    • Multimodal understanding
    • Combined generative pipelines
    • Hands-on: Building multimodal applications
  • Generative AI Deployment
    • Serving generative models efficiently
    • Latency optimization
    • Cost management for generation
    • User feedback integration
    • Hands-on: Deploying generative AI services
  • Generative AI for Business
    • Content creation workflows
    • Personalization systems
    • Creative assistance tools
    • Enterprise integration patterns
    • Hands-on: Building business applications
  • Responsible Generative AI
    • Bias detection and mitigation
    • Content filtering systems
    • Attribution and provenance
    • Copyright and ownership issues
    • Hands-on: Implementing responsible AI guardrails

  • Introduction to AI Agents
    • Agent architecture and components
    • Types of AI agents
    • Agent capabilities and limitations
    • Business applications of agents
    • Ethical considerations for autonomous systems
  • Agent Development Frameworks
    • LangChain for agent development
    • AutoGPT architecture
    • BabyAGI implementation
    • CrewAI for multi-agent systems
    • Hands-on: Building your first AI agent
  • Tool Use & Function Calling
    • Function calling architecture
    • Tool libraries and integration
    • API connectivity for agents
    • Tool selection reasoning
    • Hands-on: Building tool-using agents
  • Agent Memory Systems
    • Short-term and working memory
    • Long-term knowledge management
    • Vector databases for agent memory
    • Memory retrieval strategies
    • Hands-on: Implementing agent memory
  • Planning & Reasoning
    • Planning algorithms for agents
    • Chain-of-thought reasoning
    • Tree of thought exploration
    • Task decomposition techniques
    • Goal-oriented behavior
    • Hands-on: Building reasoning systems
  • Multi-Agent Systems
    • Multi-agent architectures
    • Communication protocols
    • Role specialization
    • Collaborative problem-solving
    • Emergent behaviors
    • Hands-on: Implementing multi-agent systems
  • Autonomous Decision Making
    • Decision theory for agents
    • Utility functions and preferences
    • Risk assessment and management
    • Feedback incorporation
    • Hands-on: Building decision-making agents
  • Agent Evaluation & Testing
    • Evaluation frameworks for agents
    • Benchmarking agent performance
    • Simulation environments
    • User feedback integration
    • Adversarial testing
    • Hands-on: Testing agent capabilities
  • Human-Agent Interaction
    • Conversational interfaces
    • User experience design for agents
    • Explainability for agent actions
    • Trust building mechanisms
    • Hands-on: Designing human-agent interactions
  • Enterprise Agent Deployment
    • Agent security considerations
    • Scalable agent infrastructure
    • Monitoring agent behavior
    • Continuous improvement frameworks
    • Governance and compliance
    • Hands-on: Deploying enterprise-grade agents

  • MLOps End-to-End Project
    • Building a complete ML system with CI/CD
    • Data pipeline construction
    • Model training and evaluation automation
    • Deployment and monitoring implementation
    • Documentation and presentation
  • LLMOps Production Project
    • Deploying a production-ready LLM application
    • Fine-tuning and optimization
    • Prompt management system
    • Monitoring and evaluation pipeline
    • Cost optimization strategies
  • AIOps Implementation Project
    • Building an AIOps system for IT infrastructure
    • Anomaly detection implementation
    • Predictive maintenance system
    • Integration with ITSM tools
    • ROI calculation and business value assessment
  • AI Agent Solution Project
    • Developing an enterprise AI agent
    • Tool integration for specific domains
    • Multi-agent collaboration implementation
    • User interface and interaction design
    • Performance evaluation and optimization
  • Industry-Specific Implementation
    • Vertical-specific AI implementation
    • Custom solutions for industry challenges
    • Compliance and regulation considerations
    • Business process integration
    • ROI calculation and stakeholder presentation
  • Future-Proofing Skills
    • Emerging technologies and trends
    • Research paper analysis and implementation
    • Community contribution and open source
    • Continuous learning strategies
    • Building a personal development roadmap
  • Job Preparation
    • Portfolio development
    • Resume and LinkedIn optimization
    • Technical interview preparation
    • System design interview practice
    • Salary negotiation and career progression
  • Industry Mentorship
    • Sessions with industry practitioners
    • Career path guidance
    • Networking strategies
    • Professional development planning
    • Job search strategies and support
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Who can learn this course

This course is ideal for:

  • Machine Learning Engineers looking to streamline deployment

  • DevOps Engineers aiming to integrate ML workflows

  • Data Scientists interested in model lifecycle management

  • IT Operations Teams seeking automation through AIOps

  • Software Engineers who want to work with intelligent systems

  • Cloud Engineers working on ML infrastructure

  • Freshers & Enthusiasts with a passion for ML/AI automation

Average package of course (MLOps & AIOps)

100% Avg
salary hike
3L Avg
Package
Training Features
Comprehensive Course Curriculum

Elevate your career with essential soft skills training for effective communication, leadership, and professional success.

Experienced Industry Professionals

Learn from trainers with extensive experience in the industry, offering real-world insights.

24/7 Learning Access

Enjoy round-the-clock access to course materials and resources for flexible learning.

Comprehensive Placement Programs

Benefit from specialized programs focused on securing job opportunities post-training.

Hands-on Practice

Learn by doing with hands-on practice, mastering skills through real-world projects

Lab Facility with Expert Mentors

State-of-the-art lab facility, guided by experienced mentors, ensures hands-on learning excellence in every session

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Reviews

The MLOps course was a game-changer, covering deployment, monitoring, and tools like Docker and Kubernetes with hands-on labs. A must for AI engineers!

Angie M. Subhasmita Pradhan
course : MLOps & AIOps
Talk to Advisor +91 8179191999

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