MLOps Specialization Program

(Batches Start from 4th, 15th & 22nd December 2023)

About The Program

With the belief to build a healthy ecosystem as per the Industry Standards REGex Software brings a Winter Training/Internship Program on “MLOps Specialization”. We organize MLOps Specialization Program for improving the knowledge and skills of the Students/Professionals, so that they can become specialized in the field of MLOps(ML + DevOps) and get their Dream Job in Software Development Field in Big MNCs.

REGex Software Services’s “MLOps Specialization” program is a valuable resource for beginners and experts. This program will introduce you to Machine Learning, Deep Learning, Python, SQL, ETL, Docker, Kubernetes, Hadoop, Spark, Chef, Ansible, Jenkins, Terraform, Openshift, AWS etc. from Basics to Advance. If you want to become Data Scientist, REGex introduce this program for you.

Weekly Duration

20 Hours Per week


Physical (Jaipur)
Online (Google Meet)


10 Months


30 per Batch

What you will Learn



Machine Learning

Deep Learning












Study Material

  • E-Notes
  • Assignments & Poll test
  • 450+ hours on demand Live Video Lectures
  • Access of Recordings & Study Material
  • Mentorship Support
  • Work on multiple Minor Projects & Use Cases
  • Work on Live Projects


  • Able to think out of the box
  • Become expert in multiple technology domains like: Python, Machine Learning, Deep Learning,  Ansible, Docker, Kubernetes and AWS.
  • Understand working of ML models deployment with AWS
  • Build projects on multiple technology domains
  • Work on more than 25 Use Cases & Projects
  • Learn to deploy your models on AWS Sagemaker or Google Cloud Platform

Why Choose Us

Live Sessions

Live Sessions by Expertise Trainers and Access of Recorded Session is also available.

Live Projects
Get a chance to work on Industry Oriented Projects to implement your learning.
24*7 Support
24*7 Mentorship Support available for all Students to clear all of your doubts.
REGex provides Internship / Job opportunities to the best Students in different Companies.

Placement Opportunities in Companies

You can get Internship/Training Opportunities to get placed in HP, DELL, Honeywell, Rightpoint, Frontdoor, Fractal and many more according to your performance.

Package Offered So Far

8 – 10 LPA
32 LPA

Placement Process

  • We’ll train you from first week and TEAM will analyze your performance according to your Assignments & projects.
  • It’s compulsory to complete 75 – 80% assignments and tests to get Placement Opportunities.
  • You will work on different projects with your team with Mentorship Support.
  • After Completion of 6.5th – 7th month, TEAM will guide you about resume making and Team will take your resumes and analyze it accordingly.
  • After this process you will get details about the further process of exam according to your performance and company’s requirement.
  • You will get Opportunities back to back from REGex End via a mail according to your performance.
Important Note: This is Placement Assured program and you will get 6 month more assistance after completion of the program. If you will successfully completed the program and still didn’t get placed within 6 months after completion then you will get 100% Fee refund with 9% interest from REGex.

Placed Students//Partnership

What People Tell About Us

Placed Students

Course Content

Python with DSA

  • Setting up the environment
  • Jupyter NoteBook
  • Know about Importance of Competitive Programming
  • Key to get a JOB in Product Based Company, Start preparing for it
  • Data structures and abstract data types
  • OOPs Concepts
  • What is an array data structure
  • Arrays related interview questions
  • Linked list data structure and its implementation
  • Stacks and queues
  • Related interview questions
  • Algorithmic Thinking, Peak Finding
  • Models of Computation, Python Cost Model, Document Distance
  • What are binary search trees
  • Practical applications of binary search trees
  • Problems with binary trees
  • Binary Search Trees
  • BST Sort
  • Balanced trees: AVL trees and red-black trees
  • AVL Trees, AVL Sort
  • Insertion Sort, Merge Sort
  • Heaps and Heap Sort
  • Counting Sort
  • Radix Sort
  • Lower Bounds for Sorting and Searching
  • Associative arrays and dictionaries
  • How to achieve O(1) constant running time with hashing
  • Ternary search trees as associative arrays
  • Hashing with Chaining
  • Simulation Algorithms
  • Table Doubling, DNA Sequence Matching
  • Integer Arithmetic
  • Karatsuba Multiplication
  • Square Roots
  • Newton’s Method
  • Shortest path algorithms
  • Dijkstra’s algorithm
  • Speeding up Dijkstra
  • Bellman-Ford algorithm
  • What are spanning trees
  • Kruskal Algorithm
  • Sorting algorithms
  • Bubble sort, selection sort and insertion sort
  • Quicksort and merge sort
  • Non-comparison based sorting algorithms
  • Counting sort and radix sort
  • Memoization, Subproblems, Guessing, Bottom-up; Fibonacci, Shortest Paths
  • Parent Pointers; Text Justification, Perfect-Information Blackjack
  • String Subproblems, Pseudo Polynomial Time.
  • Parenthesization, Edit Distance, Knapsack
  • Computational Complexity
  • Algorithms Research Topics
  • String Subproblems, Pseudo Polynomial Time.
  • Parenthesization, Edit Distance, Knapsack

Machine Learning

  • Machine learning applications
  • ML vs DL
  • Basics of Python [Syntax]
  • Working with Pandas, Numpy & Matplotlib
    ■ Working with Missing Data
    ■ Data Grouping
    ■ Data Subsetting
    ■ Merging & Joining Data Frames
  • Importing Libraries & Datasets
  • Munging & handling missing data
  • Splitting the dataset into Training set & Test set
  • What is actual machine learning
  • Various Aspects of Data – type, Variables & Category
  • Machine Learning & its Various types
  • About Supervised & Unsupervised Learning
  • Understanding Simple Linear Regression
  • Understanding the P -Value
  • Support Vector Regression
  • K nearest neighbours
  • Logistic regression
  • Naive Bayes
  • Decision trees
  • Random forests
  • Bagging Boosting
  • Maximum likelihood classifier
  • Support vector machines
  • Principal Component analysis (PCA – Theory)

  • PCA with Case-Study

  • Linear Discriminant Analysis(LDA) for Dimension Reduction

  • Feature Selection to Select the Most Relevant Predictors

  • Confusion matrix

  • Accuracy Paradox

  • CAP Curve

  • K-Mean Clustering Intuition

  • K-Mean selecting Numbers of Cluster

  • About Reinforcement Learning


  • Introduction to DBMS
  • Types of Data models, levels of abstraction
  • Relational DBMS v/s non relational DBMS
  • Create & Alter Table
  • Data Warehouse v/s data mining
  • Proprietary DBMS software v/s open source DBMS
  • Introduction to SQL and structure of SQL
  • Types of keys and constraints 
  • Normalization, types of dependencies and anomalies
  • 1 NF, 2NF, 3NF and BCNF
  • Conversion from 1 NF to 2NF, 3NF and BCNF
  • DDL – Create, Drop Alter Queries
  • DML – Delete, Insert, Merge, Select, Insert Queries
  • DCL – Grant, Revoke Queries
  • TCL – Commit, Rollback, Savepoint
  • Data retrieval 
  • Table creation- at row level, at column level
  • Types of functions – single row and multiple row
  • Types of joins-inner, outer, self and theta
  • Pattern matching using like operator
  • Union, Intersection, Union all
  • Arithmetic, comparison, and logical operators using SQL
  • Order by clause
  • Group/Aggregate functions – SUM, AVG, MIN, MAX, COUNT, STDDEV
  • Group by clause – where v/s having
  • Group by clause with having
  • Nested Queries
  • Functional Dependency
  • Closure of Attributes
  • Indexing
  • Transaction and Concurrency Control
  • Transaction in DBMS
  • ACID Propertise in DBMS
  • Constraints in SQL
  • Sql Queries & Sub Queries
  • SQL Stored Procedure
  • View, Cursor & Trigger in SQL
  • Common Table Expession
  • Replace Null and Coalesce Function
  • Running Total In SQL

Deep Learning

  • NLP Intuition

  • Types of NLP

  • Classification vs Deep Learning Models

  • The Neuron

  • Activation function

  • How Neural network work & learn by itself

  • Gradient Descent

  • Stochastic Gradient Descent

  • Ethics of Deep Learning

  • What are convolutional neural networks [CNN] ?

  • CNN Architecture

  • CNN Code preparation

  • Recurrent Neural Networks

  • Several layers

    ■ ReLLu Operation

    ■ Pooling

    ■ Flattering

    ■ Full Connection

  • Statistics

  • Sample Selection

  • Probability Theory

  • Hypothesis

  • Model Relationship

  • Model Fit

  • Descriptive Statistics

  • Types of Data

  • Qualitative Data

  • Histograms

  • Different Plots

  • Centrality and Spread

  • Outliers

  • Median, Mean, Mode

Computer Vision

  • What is computer vision & its application

  • Face Detection

    ■ Adding more features & Categorization

    ■ Object Detection & Image creation

    ■ Working with Images & vectors

  • Facial Expression Recognition in Code (Binary / Sigmoid /Logistic Regression)

  • Object detection
  • Neural style transfer
  • YOLO
  • RCNNs
  • Resnet 50
  • Tensorboard
  • What are Vectors
  • Working with word Analogy
  • Text Classification
  • Pre Trained word vectors from word2vec
  • Language Models
  • Introduction to Tensorflow / Keras
  • Most used right now
  • Resources gathering
  • Keras dealing with Missing Data
  • Dealing with Categorical data

ETL Talend

  • Creating a project

  •  Creating your first Job

  • Running a Job
  • Importing Resources into your Project
  • Preparing the Data Sources: Working with
    Structured Files
  • Preparing the Data Sources: Read Data from
  • Joining Data Coming from Multiple Data Sources
    using tMap
  • Transforming Data
  • Starting Talend Studio
  • Creating your first Job
  • Running a Job
  • Using the component help
  • Designing a Job using best practices
  • Documenting a Job
  • Working with delimited files
  • Working with hierarchical files
  • Creating tables in MySQL databases
  • Reading data from MySQL database tables
  • Applying best practices
  • Using delimited file metadata
  • Using XML file metadata
  • Using database metadata
  • Using generic schemas
  • Updating metadata
  • Mapping data using tMap
  •  Joining data using tMap
  • Capturing join rejects
  • Filtering data and capturing filtering rejects
  • Using other data processing components
  • Creating a built-in context variable
  • Connecting to databases using context variables
  • Creating a context group in the repository
  • Loading context variables from a flow
  • Building a stand-alone Job
  • Building a new version of the Job
  • Building a Docker image
  • Managing files
  • Processing files
  • Managing Job execution using a master Job
  • Detecting and handling basic errors
  • Raising a warning

Big Data Tools

● Data Warehouse ● History of Data Warehousing ● Need for Data Warehouse ● Data Warehouse Architecture ● Data Mining Works with DWH ● Features of Data warehouse ● Data Mart ● Application Areas
● Dimension modeling ● Fact and Dimension tables ● Database schema ● Schema Design for Modeling ● Star, SnowFlake ● Fact Constellation schema ● Use of Data mining ● Data mining and Business Intelligence ● Types of data used in Data mining ● Data mining applications ● Data mining products
● What’s Big Data? ● Big Data: 3V’s ● Explosion of Data ● What’s driving Big Data ● Applications for Big Data Analytics ● Big Data Use Cases ● Benefits of Big Data
● What is distributed computing ● Introduction to Map Reduce ● Map Reduce components ● How MapReduce works ● Word Count execution ● Suitable & unsuitable use cases for MapReduce
● Architecture ● Basic Syntax ● Import data from a table in a relational database into HDFS ● import the results of a query from a relational database into HDFS ● Import a table from a relational database into a new or existing Hive table ● Insert or update data from HDFS into a table in a relational database
● Define a Hive-managed table ● Define a Hive external table ● Define a partitioned Hive table ● Define a bucketed Hive table ● Define a Hive table from a select query ● Define a Hive table that uses the ORCFile format ● Create a new ORCFile table from the data in an existing non-ORCFile Hive table ● Specify the delimiter of a Hive table ● Load data into a Hive table from a local directory ● Load data into a Hive table from an HDFS directory ● Load data into a Hive table as the result of a query ● Load a compressed data file into a Hive table ● Update a row in a Hive table ● Delete a row from a Hive table ● Insert a new row into a Hive table ● Join two Hive tables ● Use a subquery within a Hive query
● An overview of functional programming ● Why Scala? ● REPL ● Working with functions ● objects and inheritance ● Working with lists and collections ● Abstract classes
● Spark SQL Overview ● Spark SQL Architecture ● What is Spark streaming? ● Spark Streaming example
● What are dataframe ● Manipulating Dataframes ● Reading new data from different file format ● Group By & Aggregations functions

● Introduction of HBase
● Comparison with traditional database
● HBase Data Model (Logical and Physical models)
● Hbase Architecture
● Regions and Region Servers
● Partitions
● Compaction (Major and Minor)
● Shell Commands
● HBase using APIs


  • Introduction
  • Docker Basics
  • Networking
  • Storage
  • Dockerfile
  • Managing Containers
  • Docker Compose
  • Introduction to Docker Swarm
  • Security
  • Configuration Management (Ansible)
    • What & Why ?
    • Ad-hoc Commands
    • Playbooks
    • Facts
    • Handlers
  • Introduction
  • Basic Concepts
  • Pods
  • Replication controller and Replica-Sets
  • Deployment
  • Services
  • Kubernetes in cloud
  • Networking
  • Storage
  • Scaling
  • Authentication

AWS (For Deployment)

  • Create your own ML models
  • Deploy ML models
  • AWS Recognition
  • ML & DL Projects
    • Titanic Classification
    • Carbon Emissions
    • Car price analysis
    • Drug Prediction
    • Hand written Digit Recognition
    • CIFAR 10
    • CIFAR 100
    • Cats vs dogs
    • Intel scene Classification
    • Transfer Learning
    • Object Detection
  • Power BI Projects & Use Cases
    • Importing Models from Excel to Power BI
    • Snowflake Schemas in Power BI
    • Showing Key metrics with Cards & KPI Visuals
  • BigData Projects
    • Zomato Analysis
    • Cricket & Football Data Analysis
    • Flipkart Product Data Analytics

Operating System

  • What are OS Component ?
  • File Management
  • Process Management
  • I/O Device Management
  • Network Management
  • Deadlock
  • Main Memory Management
  • Secondary Storage Management
  • Security Management
  • Other Important Activities

Computer Networks

  • OSI Model

  • TCP/IP Model

  • Network topologies and Ethernet

  • Internet Protocol v4 and v6

  • Media Access Control and Address Resolution Protocols

  • Access points, routers, modems

  • Firewalls, TCP ports, UDP ports

  • Routing protocols

Extra Sessions

Additinal Session on GIT, Linux, Docker, AWS Basics, Jenkins and many more for all students.

Fee Structure

Indian Fee (Physical)

Price: ₹1,79,999/- (Flat 75% off) => ₹49,999/- 

Indian Fee (Online)

Price: ₹1,79,999/- (Flat 75% off) => ₹49,999/- 

International Fee

Price: $4000 (Flat 75% off) => $1,000 

Fee can be paid as No Cost EMI @5000/month

Cashback Policy

  • You will get your Unique Referral Code after successful paid registration.
  • You will get ₹2500 Cashback directly in your account for each paid registration from your Unique Referral Code on monthly basis(After Closing Registrations of this program) .
  • For Example:- If we received 10 paid registration from your Unique Referral Code then you will receive ₹2500*10 = ₹25,000 on monthly basis.
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Industrial Internship/Training Program – December 2023

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Enroll Now

(Batches Start from 4th, 15th & 22nd December 2023)

*It will help us to reach more
*Extra off is applicable on 1 time payment only. Seats can be filled or Price can be increased at any time. Refund policy is not available*