MLOPS CERTIFICATION AUTHORITIES

MLOPS COURSE FEATURES

MLOPS LEAD MENTORS

MLOPS COURSE FEE

Live Virtual

Instructor Led Live Online

1,200
768

  • 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

710
460

  • 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

Corporate Training

Customize Your Training


  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

Enquire Now

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.

images not display images not display

WHY DATAMITES INSTITUTE FOR MLOPS COURSES

Why DataMites Infographic

SYLLABUS OF MLOPS COURSES

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 CAREER SUCCESS STORIES

MLOPS COURSE REVIEWS

ABOUT MLOPS COURSE

The DataMites® MLOps - Course Bundle is a thorough, career-focused course that covers the two main cloud computing infrastructures, AWS and Azure, as well as hands-on MLOPS deployment. This Certified MLOps Engineer Course is compiled in accordance with the current industry standard and will imbibe you with machine learning expertise alongside a firm grasp of DevOps!

 

Did you be aware that by the end of 2024, over 75% of businesses will have put artificial intelligence to use? MLOps is one of the main processes supporting the operationalization of AI. According to a Forbes report, the market for MLOps is predicted to grow to $4 billion by 2025. Because there has been a shortage of MLOps specialists, their demand has been fairly strong.

 

Our MLOps Engineer Training can be taken up by freshers and intermediate learners in the field of MLOps. This is a career-oriented program, designed to impart a strong foundation in the machine learning operations field, its core concepts, and hands-on deployment of MLOps pipelines in the Azure ML platform. 

 

The Certified MLOps Engineer Course adheres to a 3-phase learning methodology;

Phase 1: To ensure a smooth pre-learning process, students will receive pre-course self-study materials and top-notch video sessions.

Phase 2: Over the course of two months, students will get live instruction from knowledgeable instructors and mentors. The phase will help students gain first-hand knowledge of the field.

Phase 3: For a period of two months, students participate in internships and projects and receive specialized mentoring from industry professionals. In this phase, along with experience certification, there are 2 capstone projects. The IABAC Certification, JAINx Certification, and DataMites MLOps Engineer Course Completion Certification are given to students after the training and projects are fully completed.

 

Furthermore, machine learning technologies are enabling the fourth iteration of the revolution. Global business executives are taking notice of MLOps, which is generating new job profiles. Indeed, the right time to take up a career in MLOps!

 

DataMites Certified MLOps Engineer Course Bundle;

  • MLOps Foundation - MLOps Introduction, MLOps CI/CD/CT Pipelines, MLOps Maturity Levels, and MLOps Platforms.

  • Python Foundation - Python Basics, Python Control Statements, Python Data Structures, Python Functions, Python Numpy Package, and Python Panda Package.

  • Data Science Foundation - Data Science Essentials, Data Engineering Foundation, Python for Data Science, Visualization with Python, R Language Essentials, Statistics, and Machine Learning Introduction.

  • Linux Foundation - Linux Introduction, Linux Shell Scripting, Linux File Management, Scheduling Tasks, Linux Package Management, Linux Commands, Database Connectivity, Linux Networking, and Permission & Security.

  • Version Control with Git - GIT Introduction, GIT Repository & GitHub, Commits, Pull, Fetch & Push, Tagging, Branching & Merging, Undoing Changes, and GIT with GitHub & Bitbucket.

  • Data Version Control (DVC) - DVC Introduction, Git Introduction, Git Repository & GitHub, Python DVC Package, and Hands-On DVC Project.

  • MLOps on AWS and Azure -  Amazon & AWS Data Services, AWS MLOps, Azure MLOps, and Azure ML Train & Deploy.

 

DataMites offers a variety of learning choices, including Exclusive Certified MLOps Engineer Classroom Training, Online Certified MLOps Engineer Training, and Outstanding Recorded Sessions. DataMites Certified MLOps Engineer Course Fee ranges from 468.18 USD to 1337.67 USD, according to your selected learning method. 

 

DataMites provides instruction from experts who are knowledgeable about their respective fields. The major goal of our organization is to create and nurture professionals that are courageous in the face of highly challenging analytical complexity. 

 

DataMites MLOps Engineer Training Highlights:

  1. Industry Aligned Curriculum - updated to match the latest industry trends,

  2. Accreditations from IABAC, and Jain University.

  3. 4-Month Program - 400 Learning Hours,

  4. Projects & Internships -20 capstone projects, 1 client project,

  5. Industry Expert Faculty for training and mentorship,

  6. End to End Job Support 

 

Following the emergence of various open-source frameworks, MLOPS has gained prominence over the past few years. According to reports, the MLOps market is expected to expand quickly in the upcoming years and might reach $4.5 billion by the end of 2025

 

The practice of Machine Learning, DevOps, and Data Engineering are combined to form MLOps. It enables businesses to build and maintain ML systems in production with dependability and efficiency. MLOps Engineering is simpler for software experts to pursue as a career, but it is still doable for those without a technical background. Like many others in the profession, you can become an MLOps engineer by studying a variety of programming languages and gaining data science expertise. 

 

MLOps will support organizations of all stripes in developing effective plans, managing, and succeeding in the future in a move that highlights the significance of this profession. The US average compensation for an MLOps Engineer is $90,529. An MLOps Engineer in the UK can expect to earn an annual salary of GBP 35,496. MLOps Engineer Salary in India is INR 9,31,576 on average. (Glassdoor.com)

 

It's quite challenging to distinguish yourself from the talented pool of machine learning and MLOps engineers out there. Through our Certified MLOps Engineer Course, we seek to mold professionals in the domain who are adept to conquer the challenges of machine learning and artificial intelligence industries. So why not enroll in the MLOps Engineer Certification Training and gain mastery over the machine learning industry?

ABOUT DATAMITES MLOPS COURSES

Machine learning is a subset of artificial intelligence that emphasizes a machine's ability to imitate intelligent human behaviour. Artificial intelligence systems are used to complete complex tasks in just the same way that humans solve problems. Machine learning is a technique of AI that can be harnessed in a multitude of ways.

The discipline of delivering machine learning (ML) models through repeatable and efficient workflows is known as machine learning operations (MLOps).

Machine Learning Operations (MLOps) is an acronym for Machine Learning Operations. MLOps is a basic component of Machine Learning engineering that focuses on optimising the process of deploying machine learning models, as well as maintaining and monitoring them.

The cyclical process that data science initiatives follow is known as the machine learning life cycle. It lays out each step that a company should take to generate tangible economic value from machine learning and artificial intelligence (AI).

Machine Learning's Mechanisms Machine learning uses two techniques: supervised learning, which entails training a model on known input and output data to predict future outputs, and unsupervised learning, which involves identifying interrelationships and in patterns in input data.

MLOps intends to fast scale up machine learning ML model delivery in order to obtain corporate insights from data. Many companies have created a new profession called ML engineer to assure MLOps performance. 

Machine Learning Operations (MLOps) provides a technical backbone for managing the machine learning lifecycle through automation and scalability, allowing businesses to overcome many of the hurdles on the path to AI with ROI. AI and machine learning projects should influence your company's destiny.

  • Obtaining information.

  • Analyze data.

  • Transformation and preparation of data.

  • Training and development model

  • Validation of the model

  • Serving as a model.

  • Observation of the model

  • Model retraining 

From model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business KPIs, MLOps covers the complete lifecycle. MLOps is a subset of ModelOps, according to Gartner.

An end-to-end enterprise-grade platform for managing the complete machine learning and deep learning product life-cycle for data scientists, data engineers, DevOps, and managers. Data science is democratised with this AI platform, which automates end-to-end machine learning at scale.

The purpose of an MLOps team is to automate machine learning model integration into the core software system or as a service component. This tends to require thoroughly automating the ML workflow without the need for human interaction.

DevOps is a set of methods aimed at shortening the development life cycle of a system and delivering high-quality software on a continuous basis. MLOps, on the other side, is the automated and commercialization of machine learning applications and workflows.

The success of ML model deployment in production is still strongly reliant on two crucial factors: code and data. Acknowledging the correlation between the two is fundamental.

Machine learning operations, or MLOps, is quickly becoming one of the most popular fields. Machine learning and artificial intelligence hiring has increased by 74% yearly over the last four years.

MLOps facilitates communication between all parties involved in the development of machine learning technology. As marketers, we can take some lessons from this and apply them to our own businesses. Clear norms and practices are beneficial to every firm. 

MLOps is an excellent job choice.  MLOps Engineer is a popular job role in terms of compensation, growth in listings, and overall demand. People with machine learning skills are in high demand and short supply, which helps to explain why these professions are so valuable.

In the field of machine learning, an MLOps engineer effectively performs the same duties as a DevOps engineer. Everything that happens after the machine learning model is developed is overseen by an MLOps engineer. They deploy the model, test it to ensure it is functioning properly, and optimize the code for minimal latency.

According to Gartner, the field of Artificial Intelligence and Machine Learning will employ 2.3 million people by 2022. A Machine Learning Engineer and MLOps Engineers remuneration is significantly greater than that of other job categories.

A Data Scientist is a business-focused scientist who uses Machine Learning algorithms to study, find, and solve problems within the firm. MLOps engineers use data engineering methodologies and devops tools to bridge the gap between testing and production in your company's software.

Learning MLOps is worthwhile. The significance of 'MLOps,' or what drives the development of this proposed method in today's era of Artificial Intelligence, can be seen in ML workflows that can consistently, cheaply, precisely, and even at scale repeat the outputs of the data scientist's algorithms and ML production.

Machine Learning topped LinkedIn's Emerging Jobs list, with a 9.8-fold increase in five years. MLOps is a skill that most people who want to work in the data sector have. Having technical expertise is a requisite for those wishing to acquire the MLOps Certification Training.

The price of an MLOps course is determined by the level of training you require. Depending on which training provider you choose for your MLOps classroom training, however, prices range from $469.06 to $1340.17.

As per Glassdoor.com,

  • The national average salary for a MLOps Engineer is USD $90,529 per year in the United States.

  • The national average salary for a MLOps Engineer is GBP 35,496 per annum in the UK.

  • The national average salary for a MLOps Engineer is INR 9,31,576 per year in India.

View more

FAQ’S OF MLOPS TRAINING COURSE

  • The International Association of Business Analytics Certification has approved DataMitesTM as a global institute for data science (IABAC).

  • In the courses we provide, we have over 25,000 students enrolled.

  • We offer a three-step learning process. Candidates will be given with self-study videos and books in Phase 1 to assist them in gaining adequate knowledge of the material. The primary phase of rigorous live online training is Phase 2. The projects and placements will be released in the third phase.

  • Throughout the programme, real-world projects and critical case studies are presented.

  • You will earn the IABAC certification, which is a global qualification, after completing the training.

  • After finishing your course, you will have the opportunity to intern at Rubix, a global technology business specialising in artificial intelligence.

Participants will learn about MLOps tools and best practices for deploying, assessing, analyzing, and running production machine learning systems on Google Cloud at the DataMites MLOps Certification Courses

The MLOps Certification Training will take place over the course of 3 months. On weekdays and weekends, training sessions are held. You may select any of them based on your availability.

At DataMites the MLOps Certification Fee is 428.23 USD, the discounted price at which we give you our training is 334.55 USD. DataMites MLOps Training fee in Europe is 374.67 EUR to which you can enroll at just 292.71 EUR. The DataMites, MLOps Certification Training fee in India, is 32,000 that is available to you at just 25,000 INR. 

In other countries:

  • Canada - 543.71 CAD 

Discounted price - 424.77 CAD

  • Australia - 601.31 AUD 

Discounted price - 469.77 AUD

  • Germany - 374.67 Euro 

Discounted price - 292.71 Euro

  • Switzerland - 395.42 CHF 

Discounted price - 308.93 CHF

  • UAE - 1572.86 AED 

Discounted price - 1228.80 AED

  • Saudi Arabia - 1606.89 SAR 

Discounted price - 1255.39 SAR

  • South Africa - 6612.28 ZAR 

Discounted price - 5165.84 ZAR

It all varies on the course and training mode you select.

  • The complete machine learning life cycle is effectively managed.

  • Professionals in the field of MLOps are in high demand.

  • There are numerous job opportunities available.

  • a steady job

  • High-Paying

Datamites does offer classroom training only in India. For other locations we provide online training in MLOps. We would be happy to host one in additional areas based on the applicants' requests and the availability of other prospects in that specific location.

We are committed to providing you with certified and highly qualified trainers who have decades of industry experience and are well-versed in the subject matter.

Our Flexi-Pass for MLOps Certification Training allows you to attend Datamites sessions for a period of three months to clarify any query or revision.

We'll give you an IABAC® certification, which means your talents will be recognised all around the world.

Of course, once you've completed your course, we'll provide you with a MLOps Course Completion Certificate.

Yes. Photo ID evidence, such as a National ID card or a driver's licence, are necessary to issue the participation certificate and schedule the certification exam.

You don't have to be concerned about it. Simply contact your professors about it and arrange for a lesson that fits within your schedule. Each session of Online MLOps Training will be filmed and uploaded so you can easily learn what you missed at your own speed and in the comfort of your own home.

Yes, you will be given a free trial class to give you an idea of how the training will be conducted and what will be covered during the session.

Yes, we have a specialised Placement Assistance Team (PAT) that will assist you with job placement when the course is completed.

The DataMites Placement Assistance Team (PAT) helps applicants complete all of the necessary steps to start their Data Science career. PAT provides a wide range of services, including:

1. Make a professional connection

2. Putting Together a Resume

3 mock interviews with specialists in the sector

4. Talk about interview questions.

The DataMites Placement Assistance Team (PAT) provides career coaching sessions for applicants with the goal of supporting them in recognising their purpose once they reach the corporate world. Students are walked through the various alternatives available in the Data Science field by industry experts, allowing them to have a complete picture of their options. They'll also learn how to overcome the many obstacles they're likely to face as a beginner to the profession.

We provide a variety of learning alternatives, including live online, self-study, and classroom training. You have complete freedom to choose your preference.

 

Learning Through Case Study Approach

Theory → Hands-on → Case Study → Project → Model Deployment

Yes, of course, it is important that you make the most of your training sessions. You can of course ask for a support session if you need any further clarification.

We accept payment through;

  • Cash
  • Net Banking
  • Check
  • Debit Card
  • Credit Card
  • PayPal
  • Visa
  • Master card
  • American Express

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.

View more

MLOPS COURSE PROJECTS

MLOPS JOB INTERVIEW QUESTIONS

Global MLOPS COURSES Countries

popular career ORIENTED COURSES

DATAMITES POPULAR COURSES


HELPFUL RESOURCES - DataMites Official Blog