MLOPS CERTIFICATION AUTHORITIES

MLOPS Course Features

MLOPS COURSE LEAD MENTORS

MLOPS COURSE FEE IN INDIA

Live Virtual

Instructor Led Live Online

64,000
39,900

  • IABAC® & JAINx® Certification
  • 4-Month | 400 Learning Hours
  • 20-Hour Live Online Training
  • 20 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship +Job Assistance

Blended Learning

Self Learning + Live Mentoring

38,000
23,900

  • Self Learning + Live Mentoring
  • IABAC® & JAINx® Certification
  • 1 Year Access To Elearning
  • 20 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Classroom

In - Person Classroom Training

64,000
45,900

  • IABAC® & JAINx® Certification
  • 4-Month | 400 Learning Hours
  • 20-Hour Classroom Sessions
  • 20 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship +Job Assistance

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BEST MLOPS CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES FOR MLOPS TRAINING

Why DataMites Infographic

SYLLABUS OF MLOPS CERTIFICATION IN INDIA

MODULE 1: MLOPS INTRODUCTION

• MLOps Overview
• Machine Learning Lifecycle
• Challenges of Tradition Machine Learning lifecycle
• MLOps as a solution.
• MLOps Core Concepts
• MLOps standards and principles

MODULE 2: MLOPS CI/CD/CT PIPELINES

• ML models in Production
• MLOps Continuous Integration (CI)
• MLOps Continuous Delivery (CD)
• MLOps Continuous Training (CT)

MODULE 3: MLOPS MATURITY LEVELS

• Maturity levels, why is it important?
• Various MLOps Maturity Levels
• MLOps Maturity Level 0
• MLOps Maturity Level 1
• MLOps Maturity Level 2

MODULE 4: MLOPS PLATFORMS

• MLOps Architecture
• MLOps Platforms and Tools
• Microsoft Azure ML Foundation
• AWS SageMaker for MLOps

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array –Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series –Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1: DATA SCIENCE ESSENTIALS

• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow

MODULE 2: DATA ENGINEERING FOUNDATION

• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering

MODULE 3: PYTHON FOR DATA SCIENCE

• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures : Series and DataFrame
• Pandas DataFrame key methods

MODULE 4: VISUALIZATION WITH PYTHON

• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations

MODULE 5: R LANGUAGE ESSENTIALS

• R Installation and Setup
• R STUDIO –R Development Env
• R language basics and data structures
• R data structures , control statements

MODULE 6: STATISTICS

• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T Tests
• Direct And Indirect Correlation
• Regression Theory

MODULE 7: MACHINE LEARNING INTRODUCTION

• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic Regression

MODULE 1: LINUX INTRODUCTION

• Introduction to Linux
• Shell Environment Basics
• Understanding Linux Kernel
• Distros in Linux
• Installing Linux in virtual box
• Linux Boot process
• Basic Linux commands

MODULE 2: LINUX SHELL SCRIPTING

• Shell scripting Introduction
• Setting shell script permission and execute
• Shell conditional statements
• IF, IF-ELSE and Nested IF statement
• Looping Statements: WHILE and FOR
• Functions in Shell script

MODULE 3: LINUX FILE MANAGEMENT

• Introduction to Linux file management
• Everything is a file in Linux (files, directories, executables and processes)
• Understanding Linux users, groups and processes, Root and Linux file hierarchy
• Understanding file permissions, CHMOD
• File copying, moving and deleting
• Process control commands (PS and KILL)
• Hand-on file management tasks

MODULE 4: SCHEDULING TASKS

• Introduction to Daemons
• Scheduling task in Linux
• Cron and Crontab
• Hands-on scheduling task in linux

MODULE 5: LINUX PACKAGE MANAGEMENT

• Package Management
• Package Managers & DPKG
• Working with APT & APT GET

MODULE 6: LINUX COMMANDS

Part 1: sudo, pwd, cd, ls, cat, cp, mv, mkdir,
rmdir, rm, touch, locate, find, grep, df, du, head,
tail, diff, tar, chmod, chown, jobs, kill, ping

Part 2: wget, uname, top, history, man, echo, zip,
unzip, hostname, useradd, userdel, apt-get,
nano, vi,jed,alias,unalias,su,htop

MODULE 7: DATABASE CONNECTIVITY

• Installing, configuring and security MySQL
• Executing SQL queries from the terminal
• Querying through shell script
• Running queries from a shell script
• Performing CRUD Operation
• Hands-on Exercise

MODULE 8: LINUX NETWORKING

• Networking in Linux
• Networking commands
• PING, IFCONFIG, Wget
• cURL,SSH, SCP and FTP, learning firewall tools: iptables
• firewalld, DSN and resolving IP adresss
• etc/hosts, etc/hostname, nslookup and dig

MODULE 9: PERMISSIONS & SECURITY

• Types of Account in Linux
• User Management, Group Management
• Files Access Controls, Linux File Permissions
• Modifying File Ownership
• Sudoers in Linux, Special Permissions
• System Management, System tools
• Hard link and Soft link, Aliasing in Linux
• Creating users in Multiple ways

MODULE 1: GIT INTRODUCTION

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2: GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING

• Organize code with branches
• Checkout branch
• Merge branches

MODULE 5: UNDOING CHANGES

• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account

MODULE 1: DVC INTRODUCTION

• DVC Purpose
• Managing Project with DVC
• DVC Workflow
• Tools for DVC version control

MODULE 2: GIT INTRODUCTION

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 3: GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo

MODULE 4: PYTHON DVC PACKAGE

• Python DV Installation
• Project folder setup
• DVC Configuration
• Integrating with Git Repo

MODULE 5: HANDS-ON DVC PROJECT

• Project Data
• DVC pipeline setup
• ML Modeling and Evaluation
• DVC Metrics
• Establishing repeated experiments with DVC

MODULE 1: AMAZON AWS DATA SERVICES

• Introduction to Linux
• Shell Environment Basics
• Understanding Linux Kernel
• Distros in Linux
• Installing Linux in virtual box
• Linux Boot process
• Basic Linux commands

MODULE 2: AWS MLOPS

• Setting shell script permission and execute
• Shell conditional statements
• IF, IF-ELSE and Nested IF statement
• Looping Statements: WHILE and FOR
• Functions in Shell script

MODULE 3: AZURE MLOPS

• 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

MODULE 4: Azure ML Train & Deploy

• Create training compute
• Train ML model
• Register model
• Deploy model in AKS
• Hands-on: Build and Run MLOps

MLOPS TRAINING COURSE REVIEWS

ABOUT MLOPS COURSE IN INDIA

MLOps, an emerging domain combining machine learning and operations, is gaining momentum in India. The global MLOps market, which was valued at $1.4 billion in 2022, is projected to skyrocket to $37.4 billion by 2032. This growth trajectory, highlighted by Allied Market Research, makes MLOps a lucrative career path. DataMites, recognizing this opportunity, offers comprehensive MLOps Courses tailored for the Indian market, enabling learners to integrate machine learning with operations effectively.

Our MLOps Training in India spans four months and is accredited by IABAC and NASSCOM FutureSkills, focusing on practical machine learning applications. Students in India will benefit from engaging in 20 capstone projects and a client project that mirror real-world challenges. The curriculum is designed with 400 hours of intensive learning, taught by Ashok Veda and our team of industry experts and scholars. We provide extensive placement assistance, including interview preparation, resume building, job updates, and networking opportunities. The Flexi Pass option accommodates the busy schedules of Indian professionals and students, offering flexible learning timelines.

Datamites provides offline data analytics courses across multiple cities, including Bangalore, Nagpur, Ahmedabad, Vijayawada, Hyderabad, Mumbai, Delhi, Kolkata, Chennai, Bhubaneswar, Kochi, and Pune.

In India, the demand for MLOps professionals is soaring. As per Glassdoor, the average annual salary for an MLOps Engineer in India is around INR 14,39,508, reflecting both the high demand and the specialized skill set required in this field. (Glassdoor) As an MLOps expert, you will play a vital role in the seamless integration of machine learning into business processes, marking a significant step in the tech evolution in India.

Enrolling in our MLOps course in India is more than just an educational pursuit; it's a step towards becoming a part of India's growing tech revolution.

ABOUT DATAMITES MLOPS TRAINING IN INDIA

MLOps, or machine learning operations, is about delivering ML models in a repeatable and efficient way, crucial in today’s data-driven industries in India.

In India, MLOps is used to scale machine learning model delivery rapidly, essential for gaining business insights from data. The role of ML engineers, crucial for MLOps efficiency, is becoming increasingly relevant in the Indian tech industry.

For Indian businesses, MLOps is key to managing the machine learning lifecycle. It offers a structured approach to handling automation and scalability challenges in AI projects, driving ROI and business success.

The stages include data gathering, analysis, preparation, model training, validation, deployment, monitoring, and retraining, all critical for the Indian market's dynamic data landscape.

MLOps in India encompasses the entire lifecycle from model creation, orchestration, deployment, to ongoing management and governance, aligning with global standards.

India is witnessing a surge in the field of MLOps, reflecting a global trend. The demand for machine learning and AI skills has seen a substantial increase, making it a promising career choice.

MLOps fosters collaboration and standardization in machine learning technology development, vital for Indian businesses looking to leverage AI for growth.

MLOps Engineer is a lucrative career choice in India, offering competitive salaries and growth opportunities due to the scarcity of skilled professionals in this field.

In the Indian context, an MLOps Engineer manages everything post-ML model development, ensuring efficient deployment, testing, and optimization for local market needs.

According to Allied Market Research, the global MLOps (Machine Learning Operations) market is on an impressive growth trajectory. Valued at $1.4 billion in 2022, it is projected to surge to a remarkable $37.4 billion by 2032. This projection translates to a compound annual growth rate (CAGR) of 39.3% from 2023 to 2032. This rapid growth is indicative of the increasing integration of machine learning in various industries and the growing need for efficient operations in managing machine learning workflows.

While Data Scientists in India focus on leveraging ML algorithms for business problem-solving, MLOps Engineers work on implementing these solutions effectively in production environments.

Given the growing AI market in India, learning MLOps is increasingly relevant and valuable, ensuring efficient and scalable ML workflows.

MLOps is ideal for those in India aspiring to join the data sector. Technical knowledge is recommended for those seeking the MLOps Certification Training.

MLOps course fees in India range from INR 40,000 to INR 80,000 depending on the training level and provider.

The average salary for an MLOps Engineer in India is approximately INR ₹14,39,508 per year, as per Glassdoor.

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FAQ'S OF MLOPS TRAINING IN INDIA

DataMites offers a globally recognized curriculum, tailored for the Indian market with over 25,000 students, a three-step learning process, real-world projects, IABAC certification, and internship opportunities at Rubix, catering to the specific needs of the Indian tech industry.

Learners in India will become proficient in MLOps tools and practices for deploying and managing ML systems, with a focus on applications relevant to the Indian market.

The course duration is 4 months, with flexible weekday and weekend sessions to suit the schedules of Indian professionals and students.

The fee for MLOps Certification Training in India ranges from INR 25,095 to INR 64,000, offering a range of options to suit different budgets.

DataMites provides classroom training in major Indian cities like Bangalore, Chennai, Pune, Hyderabad, and Mumbai, with online training options for other locations.

Flexi-Pass provides access to DataMites sessions for three months, offering flexibility for learners in India to manage their schedules.

Yes, learners will receive an IABAC® certification and a course completion certificate, globally recognized and valued in the Indian market.

A photo ID is required for certification and exam scheduling in India.

Missed classes can be covered through recorded sessions, offering flexibility for learners in India.

DataMites provides a free trial class in India, allowing prospective students to experience the training before enrolling.

Payments can be made via various methods including cash, net banking, check, debit/credit card, PayPal, Visa, Mastercard, and American Express, catering to the preferences of Indian learners.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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