AI Operations

Artificial Intelligence Operations (AIOps) is the most in demand technical skill these days. It helps to incorporate DevOps principle in AI product development. It's a live instructor-led certification program provided by iNeuron intelligence private limited. Here you will learn various methods to implement AIOps methodology in the ML and DL projects which includes implementation on various clouds like AWS, Azure, GCP and DigitalOcean.

Start Date: 17th July 2021
Class Timings: - 9 AM IST to 11 AM IST
Doubt Clearing Session: Tuesday 10 PM IST 12 AM IST
0.00 (0 Reviews)
Language: English

Course Overview

Complete live technical training will be provided for AIOps with end to end project solution designing.

What you'll learn
  • AIOps
  • Linux foundation
  • GIT foundation
  • GitHub
  • Gitlab
  • Data version control DVC
  • MLFlow
  • Docker foundation
  • Kubernetes Foundation
  • Tensorflow Extend (TFX)
  • Kubeflow
  • AWS AIOps
  • Azure AIOps
  • GCP AIOps
  • Digital Ocean
Requirements
  • Minimum System requirement: Intel Core i3 processor and 4GB RAM or higher
  • Decent internet connection
  • Your Dedication

Course Curriculum

  • Why Linux? Linux types? How to access Linux env in different system
  • Installation of virtual box, WSL, sandbox for windows user
  • Free tier EC2 ubuntu instance
  • SSH and SSH tools
  • Putty
  • Filezilla
  • WinSCP
  • Course Introduction
  • Working with the Shell - I
  • Introduction to Shell
  • Basic Linux Commands: ls, cat, cd, rm, chmod...etc
  • Help for command line
  • Type of Shell: bash, zsh etc
  • Bash Shell
  • Linux Core Concepts
  • Linux Kernel and types
  • Linux file system
  • Linux Boot Sequence
  • Runlevels
  • File Types
  • Filesystem Hierarchy
  • Package Management
  • Package Management Introduction and configuration
  • Linux type based package manager
  • RPM and YUM
  • DPKG and APT
  • Working with the Shell - II
  • File Compression and Archival
  • Searching for Files and Patterns using grep/wildcards etc
  • VI, Nano Editor
  • Security and File Permissions
  • The Security Incident (story)
  • Linux Accounts
  • User Management
  • Access Control Files
  • Account Management
  • File Permissions and Ownership
  • Cronjobs
  • Service management with systemd
  • Working overtime (story)
  • Creating a systemd Service
  • systemd Tools
  • Lab - systemd services
  • What? Why? When? Type? Vendor? Pricing? Industry wise uses of GIT
  • Creation of Github/Gitlab/bitbucket account
  • Local GitHub UI installation, setup with VSCode and Pycharm
  • Local and Remote Repositories installation and configuration
  • GIT Repository initialization
  • command: git log
  • Git Branches
  • What is branching in Git and why we need it?
  • Master/main branch and user-defined branch
  • Checkout and pushing to a branch
  • Merging of branches
  • Project control and management
  • In Remote Repositories
  • Initialization of Remote Repositories
  • Pushing code to the remote repositories
  • Cloning of the remote repositories to local
  • PR (Pull Requests)
  • Fetch and Pull
  • Handling conflict on merging branch
  • Forking of repository
  • Rebasing
  • Resetting and Reverting
  • Stashing
  • DVC
  • What is DVC?
  • Installation
  • Mac OS
  • Windows
  • Linux
  • Get Started
  • Data Versioning
  • Model Versioning
  • Data Access
  • Model Access
  • Data Pipelines
  • Metrics, Parameters, Plots
  • Run, Queue, Compare, Persisting, and Sharing Experiments
  • Clean up
  • DVC Uses
  • Versioning Data and Models
  • Sharing Data and Model Files
  • Data Registries
  • Shared Development Server
  • Project Structure
  • Experiment Management
  • Setup Google Drive Remote
  • Large Dataset Optimization
  • External Dependencies
  • Managing External Data
  • Automate Pipelines with DVC
  • Pipelines & Experiment Automation
  • Common issues with ML experiments
  • Build automated pipelines
  • Build automated pipeline
  • Experiments Management
  • Experimenting with reproducible pipelines
  • Tracking metrics and plots
  • Compare experiment results
  • Build, Test & Deploy
  • Introduction to CI/CD in Machine Learning
  • Build CI/CD pipeline
  • Install GitLab Runner and Trigger CI/CD pipeline
  • Build Machine Learning pipeline
  • Build CI/CD pipeline
  • Trigger CI/CD pipeline
  • Making Continuous Integration work with ML
  • DVC Integration with Project
  • Build a model Prototype
  • Build a prototype with Jupyter Notebook
  • Start to version your code with Git
  • Version your code with Git
  • Create pipelines
  • Automate pipelines and data versioning with DVC
  • Create CI pipeline to build, test, experiment
  • Experimenting with DVC and CML
  • Deploy your model
  • What is MLFLow?
  • Installation
  • MLflow Tracking
  • Where Runs Are Recorded
  • How Runs and Artifacts are Recorded
  • Scenario 1: MLFlow on localhost
  • Scenario 2: MLFlow on localhost with SQLite
  • Scenario 3: MLFlow on localhost with Tracking Server
  • Scenario 4: MLFlow with remote Tracking Server, backend and artifact stores
  • Logging Data to Runs
  • Logging Functions
  • Launching Multiple Runs in One Program
  • Performance Tracking with Metrics
  • Visualizing Metrics
  • Automatic Logging
  • Scikit-learn
  • TensorFlow and Keras
  • Gluon
  • XGBoost
  • Pytorch
  • MLFLow Tracker
  • Organizing Runs in Experiments
  • Managing Experiments and Runs with the Tracking Service API
  • Tracking UI
  • Querying Runs Programmatically
  • MLFlow Tracking Servers
  • Storage
  • Networking
  • Logging to a Tracking Server
  • MLflow Projects
  • Overview
  • Specifying Projects
  • Running Projects
  • Iterating Quickly
  • Building Multi Step Workflows
  • MLFLow Models
  • Storage Format
  • Model Signature And Input Example
  • Model API
  • Built-In Model Flavors
  • Model Customization
  • Built-In Deployment Tools
  • Deployment to Custom Targets
  • Model Registry
  • Model Registry Workflows
  • UI Workflow
  • Registering a Model
  • Using the Model Registry
  • API Workflow
  • Adding an MLFlow Model to the Model Registry
  • Fetching an MLlFow Model from the Model Registry
  • Serving an MLFlow Model from Model Registry
  • Adding or Updating an MLFlow Model Descriptions
  • Renaming an MLFlow Model
  • Transitioning an MLFlow Model’s Stage
  • Listing and Searching MLFlow Models
  • Archiving an MLFlow Model
  • Deleting MLFlow Models
  • Setup
  • Why? What? Where? Problem it can solve? Docker types? Cloud based docker containers
  • Installation of specific docker editions based on your system
  • Installing Docker
  • Create and Use
  • Docker Install, Configuration and verify
  • Container VS
  • Windows Containers unlike Linux
  • Inside Containers - Process Monitoring with Command Line Interface(CLI)
  • Private and Public Communication in Containers
  • CLI Management of Virtual Networks
  • Domain Name System(DNS) for Containers can find each other
  • Containers
  • Docker Image
  • Docker Hub Registry predefined Images
  • Images and Their Layers: Discover the Image Cache
  • Image Tagging and Pushing to Docker Hub
  • Create images
  • Using Dockerfile Basics
  • Run Docker Builds
  • Extend Official Images
  • Container Lifetime & Persistent Data
  • Persistent Data: Data Volumes
  • Shell Differences for Path Expansion
  • Persistent Data: Bind Mounting
  • Docker Compose
  • What is Docker Compose ?
  • Docker-compose.yml
  • Compose Commands
  • Add Image Building to Compose Files
  • docker project: Deploy ML model and services using Docker
  • What is Kubeflow?
  • Core Kubeflow components
  • How to set up Kubeflow on Kubernetes
  • How to develop basic ML models in Kubeflow Notebooks
  • How to train and deploy models in Kubeflow
  • How to use Kubeflow Pipelines
  • How to use KFServing to deploy models
  • How to manage logs with Kubeflow Metadata component
  • Katib Hyper Parameter Tuning
  • Kubeflow Pipelines to KFServing
  • GitLab Triggers
  • AWS S3 storage
  • GitLab CI/CD Pipelines
  • Pipelines definition
  • MongoDB cloud Atlas
  • Heroku
  • Logdata
  • Coral for Monitoring
  • Amazon Sagemaker
  • Amazon s3
  • AWS Codebuild
  • AWS Codecommit
  • Sagemaker Training Job
  • Sage Maker Endpoint
  • Amazon Api Gateway
  • Sagemake Model Monitoring
  • Cloudwatch Synthetics
  • Cloudwatch Alarm
  • Create an Azure Machine Learning workspace
  • Setup a new project in Azure DevOps
  • Import existing YAML pipeline to Azure DevOps
  • Declare variables for CI/CD pipeline
  • Create training compute
  • Train ML model
  • Register model
  • Deploy model in AKS
  • Creating Flask application using Python
  • Best practices building Flask App
  • Understanding Docker files and Dependencies
  • Creating container image
  • Walkthrough of different deployment options
  • Serverless deep dive
  • Deploying on GCP App Engine
  • Deploying on Serverless Framework
  • Hosted Kubeflow Pipelines
  • Start Hosted Pipelines
  • cluster permissions
  • Development environment
  • Launch AI Platform notebook
  • CI/CD Production Environment
  • Set up Continuous Integration (CI)
  • Verify CD
  • Droplets
  • File Transfers
  • Gitops
  • Jenkins
  • Creating Jobs
  • Creating pipelines in Jenkins
  • Docker Images
  • Kubernetes Flow
  • Creating Clusters
  • Load testing
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Sudhanshu Kumar

Having 7+ years of experience in Big data, Data Science and Analytics with product architecture design and delivery. Worked in various product and service based Company. Having an experience of 5+ years in educating people and helping them to make a career transition.

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