Skip to content

support@deepneuron.in
+91-843 843 5476
10.30 AM IST

Best IT Training Institutes in Chennai with Placement | DeepNeuron

Just another WordPress site

Home
Courses
Cart
Login
Profile
Blog
Contact Us
- Submit a Ticket
City Sitemap
Pay for Enroll

AWS, Azure, GCP, MLOPS & LLMOps Training Course

Home
Courses
AWS, Azure, GCP, MLOPS & LLMOps Training Course

AWS, Azure, GCP, MLOPS & LLMOps Training Course

Curriculum

21 Sections
115 Lessons
10 Weeks

Expand all sectionsCollapse all sections

Introduction
4
- 1.1
  Course Overview
- 1.2
  Accessing Source Code
- 1.3
  Slide Resources Available
- 1.4
  Quick Hands-On MLOps Demo
MLOps Fundamentals
3
- 2.1
  Introduction to MLOps
- 2.2
  Deep Dive into MLOps Fundamentals
- 2.3
  Understanding the Limitations of DevOps for ML
AWS and MLOps
7
- 3.1
  Introduction to AWS and Its Benefits
- 3.2
  AWS Technical Stack for MLOps
- 3.3
  Assignment: AWS Overview (1 question)
- 3.4
  Software Development Life Cycle (SDLC)
- 3.5
  Overview of SDLC and Its Importance
- 3.6
  Exploring SDLC Types
- 3.7
  Comparing Waterfall, Agile, and DevOps
AWS Specific Tools and Configurations
11
- 4.1
  DevOps Lifecycle Tools in AWS
- 4.2
  Creating and Configuring an AWS Account
- 4.3
  Security Setup: MFA, IAM Accounts, and Policies
- 4.4
  Introduction to S3 Buckets and EC2 Instances
- 4.5
  AWS Specific Tools and Configurations
- 4.6
  Creation of S3 Bucket from Console
- 4.7
  26. Creation of S3 Bucket from CLI
- 4.8
  Version Enablement in S3
- 4.9
  Introduction EC2 instances
- 4.10
  Launch EC2 instance & SSH into EC2 Instances
- 4.11
  Housekeeping Activity
Linux and Bash for MLOps
3
- 5.1
  Linux and Bash for MLOps
- 5.3
  Linux Features and Command Line Basics Refresh on Launching EC2 Instances
- 5.4
  Comprehensive Guide to Linux Commands
Core Concepts
10
- 6.1
  CI/CD Pipeline Introduction
- 6.2
  Getting Started with AWS CodeCommit & Distributed Version Control Systems (DVCS)
- 6.3
  Initial Configuration & Basic Git Commands
- 6.4
  Setting Up Your Git Workspace
- 6.5
  Understanding Git Workflow
- 6.6
  Adding Files to the Staging Area & Understanding Staged Differences
- 6.7
  Unstaging, Resetting, and Reverting Changes in Git
- 6.8
  Working with AWS CodeCommit: Remote Commands, Security, and Integrations
- 6.9
  Cloning, Branching, and Handling Git Branches: Hands-On Parts 1 & 2
- 6.10
  Resolving Git Conflicts, Rebasing vs. Merging, and Using Git Stash
Deployment & Security
8
- 7.1
  Introduction to AWS CodeDeploy & YAML
- 7.2
  First Steps with AWS CodeDeploy: Hands-On Introduction and Deep Dive
- 7.3
  Exploring AWS CodePipeline: Creation and Automation with Manual Approval
- 7.4
  Introduction to Docker: Basics & Installation
- 7.5
  Pull the image from Docker Desktop
- 7.6
  Dockerfile
- 7.7
  Push the Docker Image to ECR
- 7.8
  Hands on – Amazon ECR for AWS CodeBuild
Amazon SageMaker & Feature Engineering
11
- 8.1
  Why Amazon SageMaker is Preferred for Machine Learning Workflows
- 8.2
  Domain Creation, Studio Setup, and Clean-Up Activities in SageMaker
- 8.3
  Feature Engineering Essentials, Data Wrangler Setup, and Transformation Techniques
- 8.4
  Data Quality and Insights Report
- 8.5
  Univariate Analysis & Bias Report
- 8.6
  Target Leakage
- 8.7
  Data Transformation
- 8.8
  Data Transformation – Custom Script
- 8.9
  Export to S3
- 8.10
  Feature Engineering on Sagemaker Notebook Instance
- 8.11
  Summary
Advanced Concepts
3
- 9.1
  Introduction to Infrastructure as Code with AWS CloudFormation
- 9.2
  Deep Dive into CloudFormation: Templates, Intrinsic Functions, and Custom Resources
- 9.3
  Step Functions and Serverless Workflow Management with AWS
Building and Managing MLOps Pipelines
3
- 10.1
  Comprehensive Guide to SageMaker Pipelines: Training and Inference
- 10.2
  Developing Advanced MLOps Pipelines and System Setup
- 10.3
  Execution, Monitoring, and Managing Data Quality within MLOps Pipelines
Packaging, Deployment, and Kubernetes
3
- 11.1
  Packaging ML Models and Introduction to MLOps with MLFlow
- 11.2
  Kubernetes Basics: Introduction and Hands-On Experience
- 11.3
  Advanced AWS CodeDeploy Techniques and Deep Dive into AppSpec.yml
LLMOps
4
- 12.1
  Introduction to LLM Ops and CI/CD
- 12.2
  Overview of LLM Ops
- 12.3
  Importance of CI/CD in LLM development and deployment
- 12.4
  Benefits of implementing CI/CD practices
Fundamentals of Large Language Models (LLMs)
3
- 13.1
  Understanding LLMs and their architectures
- 13.2
  Types of LLMs (e.g., GPT, BERT, T5)
- 13.3
  Use cases and applications of LLMs
Development Environment Setup
3
- 14.1
  Setting up a local development environment
- 14.2
  Installing necessary tools and frameworks
- 14.3
  Configuring version control systems (e.g., Git)
Data Preparation and Preprocessing
3
- 15.1
  Collecting and cleaning training data
- 15.2
  Data annotation and labeling techniques
- 15.3
  Feature engineering and data preprocessing pipeline
Model Training and Evaluation
3
- 16.1
  Techniques for training LLMs
- 16.2
  Hyperparameter tuning and model optimization
- 16.3
  Model evaluation metrics and techniques
Continuous Integration (CI) for LLM Development
3
- 17.1
  Implementing CI pipelines for LLM projects
- 17.2
  Automated testing and validation
- 17.3
  Code quality checks and linting
Continuous Deployment (CD) for LLM Models
3
- 18.1
  Automating the deployment process
- 18.2
  Containerization and orchestration tools (e.g., Docker, Kubernetes)
- 18.3
  Deploying LLM models to production environments
Use Cases and Applications
12
- 19.1
  Question Answering
- 19.2
  Building an LLM-based question answering system
- 19.3
  Deploying the question answering model using CI/CD
- 19.4
  Text Generation
- 19.5
  Developing an LLM-based text generation application
- 19.6
  Automating the deployment of the text generation model
- 19.7
  Sentiment Analysis
- 19.8
  Implementing an LLM-based sentiment analysis pipeline
- 19.9
  Integrating the sentiment analysis model into a CI/CD workflow
- 19.10
  Named Entity Recognition (NER)
- 19.11
  Creating an LLM-based NER system
- 19.12
  Setting up a CI/CD pipeline for NER model deployment
Capstone Projects
12
- 20.1
  Knowledge Base Assistant
- 20.2
  Building an LLM-powered knowledge base assistant
- 20.3
  Implementing CI/CD for the assistant’s backend and frontend
- 20.4
  Creative Writing Tool
- 20.5
  Developing an LLM-based creative writing tool
- 20.6
  Automating the deployment and scaling of the writing model
- 20.7
  Customer Support Chatbot
- 20.8
  Creating an LLM-based customer support chatbot
- 20.9
  Setting up a CI/CD pipeline for chatbot training and deployment
- 20.10
  Content Generation Platform
- 20.11
  Building an LLM-powered content generation platform
- 20.12
  Implementing CI/CD for the platform’s backend and frontend
Best Practices and Future Trends
3
- 21.1
  Industry best practices for LLM Ops and CI/CD
- 21.2
  Emerging techniques in LLM development and deployment
- 21.3
  Continuous learning and model updates

This content is protected, please login and enroll in the course to view this content!

Configuring version control systems (e.g., Git)

Data annotation and labeling techniques

About Company

DeepNeuron combines a unique approach to the ideation and creation of the course content. It then collaborates with SMEs for training. Further, it offers its learners lifelong support and lifetime access to the course materials.

Contact Us

Email : sales@deepneuron.in
Phone : +91 84384 35476

Connect With Us

Refund Policy

Copyright © 2025 Best IT Training Institutes in Chennai with Placement | DeepNeuron - WordPress Theme : By Sparkle Themes

Search for:

Home
Courses
Cart
Login
Profile
Blog
Contact Us
- Submit a Ticket
City Sitemap
Pay for Enroll

Home
Courses
Cart
Login
Profile
Blog
Contact Us
- Submit a Ticket
City Sitemap
Pay for Enroll

Modal title

Main Content