6-MONTH PROFESSIONAL CAREER PATHWAY
Professional Machine Learning Engineer certification badge

Professional Machine Learning Engineer

Prepare to develop, evaluate, deploy and operate responsible machine-learning systems with Vertex AI and Google Cloud. This track is designed as preparation for the Google Cloud Professional Machine Learning Engineer certification through three months of structured training followed by an internship opportunity pathway.

Production machine-learning system with a neural model, feature pipelines, accelerated training, deployment endpoints and monitoring guardrails
Professional Machine Learning Engineer exam guide, effective 1 June 2026Official-domain aligned preparation
6 MonthsOfflineOnly 5 SeatsExam Voucher Included*
6 MonthsProfessional pathway
3 MonthsStructured training
Eligible learners*Internship opportunities
₹15K–₹25K/month*Applicable paid opportunities
IncludedCertification preparation
Included*Exam voucher
IncludedProjects and capstone
IncludedCareer support
Opportunity basedPlacement assistance
Maximum 5Learners per batch
UPCOMING PROFESSIONAL COHORT · SEPTEMBER 2026

Training starts 25 September 2026.

ADMISSIONS CLOSE20 September 2026or earlier when a five-seat track is full

Seat figures show current cohort allocations supplied for this admissions update and may change after counselling or payment confirmation. A submitted enquiry does not reserve a seat.

WHY THIS CERTIFICATION MATTERS

Build a credential around
capability—not memorisation.

The official exam is external. This programme focuses on the knowledge, practical reasoning and professional habits that can make preparation useful beyond exam day.

01

Industry recognition

A professional credential can provide an external signal of role-aligned Google Cloud knowledge when it is earned through the official examination.

02

Skills you can explain

Architecture discussions, labs and projects help turn product names into decisions you can defend in interviews and technical reviews.

03

Certification plus experience

The journey connects exam preparation with applied work and an eligibility-based internship phase instead of treating theory as the finish line.

04

Enterprise relevance

Security, reliability, governance, cost and operations are built into the learning path because production cloud work depends on them.

LEARNER SUCCESS STORY · PROFESSIONAL MACHINE LEARNING ENGINEER

From a BCA degree
to a global ML credential.

Vasanthi entered the programme as a trainee with a BCA degree background and a goal to build professional machine learning capability. She passed the Google Cloud Professional Machine Learning Engineer examination and received an internship opportunity through the programme—turning focused preparation into a defining early-career milestone.

Nanapuram Vasanthi, Professional Machine Learning Engineer learner and Machine Learning Engineer Intern
Nanapuram VasanthiMachine Learning Engineer Intern
PROFESSIONAL MACHINE LEARNING ENGINEER

I joined the programme as a trainee with a BCA degree background. Passing the Google Cloud Professional Machine Learning Engineer exam is one of the greatest achievements of my life. I have never felt so happy after an exam; the feeling was difficult to put into words.

The training helped me move from my academic foundation into cloud-based machine learning concepts, practical workflows and focused certification preparation. Earning a globally recognised professional certificate became the highlight of the journey and gave me confidence that I could take on a demanding technical goal.

After clearing the exam, I received an internship opportunity through the programme. The knowledge I built during training will be valuable throughout my career, and the combination of certification preparation and practical exposure has given me a much clearer direction for what comes next.

Nanapuram VasanthiProfessional Machine Learning Engineer learner
WHAT HELPED HER PREPARE

A preparation system built around feedback and practice.

01

BCA-to-cloud bridge

The learning path connected Vasanthi's undergraduate foundation with the cloud, data and machine learning concepts expected in a professional certification.

02

Structured trainee journey

A clear sequence of learning, practice and review helped her approach a demanding professional-level goal one milestone at a time.

03

Applied ML practice

Hands-on work helped turn model development, evaluation, deployment and responsible AI concepts into usable technical knowledge.

04

Exam preparation discipline

Focused revision and certification-aligned practice helped her identify gaps, build confidence and prepare for the official examination.

05

Certification-to-opportunity

After passing the exam, she received an internship opportunity through the programme and a path to continue applying what she learned.

About this learner story: This testimonial describes Nanapuram Vasanthi's individual experience and outcome. Examination results, certification timelines, internship selection and career outcomes vary by learner and are not guaranteed. Google administers and awards its professional certifications; the EduMonk programme is delivered by PRAGYASHAL, a Google Cloud Partner.

Read the full success story →Explore the programme →
REAL LEARNERS · PROFESSIONAL MILESTONES

Explore every learner
success story.

See how structured preparation, practical support and persistence helped learners turn professional certification goals into meaningful career steps.

View all success stories →
Bhargavi Bhanu, Cloud Developer Intern · PRAGYASHALProfessional Cloud Developer
LEARNER SUCCESS STORY

Bhargavi Bhanu

Cloud Developer Intern · PRAGYASHAL

Bhargavi moved from campus learning to certification readiness and a Cloud Developer internship through guided preparation, practical labs, mock tests and focused review.

Campus / early careerCertification + internship3 months to exam1:1 guidance
Credential image supplied
Read the complete story
Nanapuram Vasanthi, Machine Learning Engineer InternProfessional Machine Learning Engineer
LEARNER SUCCESS STORY

Nanapuram Vasanthi

Machine Learning Engineer Intern

Coming from a BCA degree background, Vasanthi joined as a trainee, developed practical Google Cloud machine learning capability, passed the Professional Machine Learning Engineer exam and received an internship opportunity through the programme.

BCA graduateCertification + internshipBCA backgroundGlobal ML credential
Credential image supplied
Read the complete story
Gattumeeda Akshaya, Professional ML Engineer Credential HolderProfessional Machine Learning Engineer
LEARNER SUCCESS STORY

Gattumeeda Akshaya

Professional ML Engineer Credential Holder

After a BCA specialising in Artificial Intelligence, Akshaya used PRAGYASHAL career guidance and structured machine learning preparation to gain direction, strengthen her technical confidence and clear the Google Cloud Professional Machine Learning Engineer exam.

BCA graduateCertification milestoneBCA in Artificial IntelligenceCareer-guided learning
Learner-reported milestone
Read the complete story
Gavinolla Swathi Reddy, Machine Learning Engineer Intern · PRAGYASHALProfessional Machine Learning Engineer
LEARNER SUCCESS STORY

Gavinolla Swathi Reddy

Machine Learning Engineer Intern · PRAGYASHAL

Swathi progressed from B.Tech IT project leadership and a strong Python and Java foundation to hands-on Google Cloud preparation, the Professional Machine Learning Engineer certification and a Machine Learning Engineer internship at PRAGYASHAL.

B.Tech ITCertification + internshipB.Tech Information TechnologyProfessional ML certification
Learner-reported milestone
Read the complete story
WHO THIS TRACK IS FOR

A focused route into
machine learning engineer work.

For ML learners, data scientists, AI engineers and software professionals seeking production-oriented cloud ML capability.

Machine Learning EngineerAI EngineerCloud ML EngineerMLOps EngineerGenerative AI Engineer
Career directions are illustrative. Role eligibility depends on prior education, experience, skills, certification status and employer requirements.
OFFICIAL EXAM-GUIDE ALIGNMENT

Know what the exam
is designed to assess.

The preparation plan is organised around the domains in the supplied Professional Machine Learning Engineer exam guide, effective 1 June 2026. Percentages are approximate and may change when the certification owner updates its guide.

DOMAIN 01~13%

Architecting low-code AI solutions

Build and select BigQuery ML, AutoML, AI API and foundational-model solutions based on business, cost, latency and availability needs.

DOMAIN 02~16%

Collaborating across teams to manage data and models

Explore and preprocess data, prototype securely in notebooks and track experiments, lineage and evaluation results.

DOMAIN 03~21%

Scaling prototypes into ML models

Select model and deployment approaches, train and tune models, troubleshoot failures and choose suitable compute accelerators.

DOMAIN 04~20%

Serving and scaling models

Deploy batch and online inference, manage model versions and rollouts, scale serving and design preprocessing and postprocessing.

DOMAIN 05~18%

Automating and orchestrating ML pipelines

Build validated end-to-end pipelines and automate retraining through repeatable CI, delivery and training workflows.

DOMAIN 06~13%

Monitoring AI solutions

Apply responsible AI and security controls, then monitor drift, skew, quality and generative-AI behaviour in production.

OFFICIAL SYLLABUS GUIDE · READ IN PAGE

Explore the complete
Professional Machine Learning Engineer exam guide, effective 1 June 2026.

Read the supplied examination guide below while you compare its official domains with the EduMonk preparation curriculum. Use the full-screen or download option when you want a larger copy.

CERTIFICATION PREPARATION CURRICULUM

What you will study
and practise.

The track combines certification-relevant concepts with labs, architecture decisions and applied production scenarios.

01

BigQuery ML, AutoML and AI APIs

Learn the core concepts, service choices, operating considerations and exam-style reasoning connected to this area.

02

Generative AI and foundational-model selection

Learn the core concepts, service choices, operating considerations and exam-style reasoning connected to this area.

03

Data preparation, features and privacy

Learn the core concepts, service choices, operating considerations and exam-style reasoning connected to this area.

04

Secure notebook prototyping and experiment tracking

Learn the core concepts, service choices, operating considerations and exam-style reasoning connected to this area.

05

Custom model training and hyperparameter tuning

Learn the core concepts, service choices, operating considerations and exam-style reasoning connected to this area.

06

CPU, GPU, TPU and distributed training choices

Learn the core concepts, service choices, operating considerations and exam-style reasoning connected to this area.

07

Batch and online model serving

Learn the core concepts, service choices, operating considerations and exam-style reasoning connected to this area.

08

MLOps pipelines, CI/CD/CT and retraining

Learn the core concepts, service choices, operating considerations and exam-style reasoning connected to this area.

09

Responsible AI, security, drift and production monitoring

Learn the core concepts, service choices, operating considerations and exam-style reasoning connected to this area.

HANDS-ON GOOGLE CLOUD LABS

Learn by building
and reviewing.

Each lab connects a concrete technical outcome to the architecture, security, reliability and operational decisions expected from a professional practitioner.

LAB 01

Build a low-code prediction baseline

BigQuery ML · SQL · Model evaluation

Train, compare and explain a baseline model against a defined business metric.

LAB 02

Run a repeatable custom training workflow

Vertex AI · Cloud Storage · Pipelines

Package training, track experiments, tune parameters and retain model lineage.

LAB 03

Deploy and scale online inference

Model Registry · Endpoints · Cloud Run

Version a model, design a rollout and evaluate latency, throughput and cost.

LAB 04

Monitor a responsible production model

Model Monitoring · Cloud Logging · Model Armor

Define drift, quality, security and responsible-AI checks with a response plan.

APPLIED CLOUD WORK

Practise decisions,
not just definitions.

Projects and assignments are designed to help you explain why an architecture or workflow is appropriate, how it can fail and how it should be operated responsibly.

PROJECT FOCUS 01

Frame and evaluate a supervised machine-learning problem

  • Translate requirements into a defensible technical approach
  • Document service choices, risks and operational controls
  • Present the solution for review and improvement
PROJECT FOCUS 02

Design a repeatable Vertex AI training and deployment workflow

  • Translate requirements into a defensible technical approach
  • Document service choices, risks and operational controls
  • Present the solution for review and improvement
PROJECT FOCUS 03

Create a monitored, responsible production ML architecture

  • Translate requirements into a defensible technical approach
  • Document service choices, risks and operational controls
  • Present the solution for review and improvement
YOUR SIX-MONTH PATHWAY

Build readiness.
Earn practical exposure.

PHASE 01 · MONTHS 1–3

Training and exam preparation

Instructor-led learning, guided labs, assignments, architectures, projects, practice assessments, mock examinations and individual reviews.

PHASE 02 · MONTHS 4–6

Eligibility-based internship

Eligible participants may contribute to supervised project, infrastructure, data, deployment, research, documentation or proof-of-concept work.

SECONDARY BENEFIT · CONDITIONAL

₹15K–₹25K monthly stipend*

Stipend eligibility and amount depend on attendance, performance, assessments, technical capability, project allocation and internal evaluation.

MILESTONE-BASED PROFESSIONAL PATHWAY

Your Journey. Ten
Professional Milestones.

Your progress is based on structured learning, practical performance, assessments, projects and professional readiness. Each milestone builds measurable skills toward certification preparation, internship opportunities and career support.

Progress is earned through performance, not simply attendance.
01Foundation
02Practical Skills
03Project
04Capstone
05Certification Readiness
06Internship Eligibility
07Internship Opportunities
08Paid Opportunities
09Career Readiness
10Placement Opportunities
01/ 10
Foundation Readiness

Foundation Readiness

Complete the foundation modules covering cloud concepts, role fundamentals, guided sessions, introductory labs and assignments.

View requirements
  • Complete the assigned lessons and foundation modules
  • Submit introductory assignments and guided labs
  • Meet the communicated attendance and participation standards
  • Demonstrate the baseline concepts needed for advanced technical learning
OUTCOMEReady for Advanced Technical Learning
02/ 10
Practical Skills Milestone

Hands-On Skill Development

Develop practical Google Cloud capability through configuration, architecture, implementation, troubleshooting and technical assignments.

View requirements
  • Complete the required track-specific Google Cloud labs
  • Demonstrate technical accuracy and practical understanding
  • Apply structured problem-solving during implementation work
  • Meet the expected assignment and lab-performance standard
OUTCOMEPractical Cloud Capability
03/ 10
Project Milestone

Project Completion

Complete guided and intermediate projects that turn individual services and concepts into reviewable applied work.

View requirements
  • Complete the guided project, intermediate project and applied challenge
  • Submit a working implementation with suitable documentation
  • Explain the architecture, decisions and problem-solving process
  • Present the work for review and respond to feedback
OUTCOMEDemonstrable Project Experience
04/ 10
Advanced Project Milestone

Capstone Readiness

Build a Vertex AI machine-learning solution covering governed data, model development, evaluation, deployment, monitoring and responsible-AI controls.

View requirements
  • Submit the proposed architecture and implementation
  • Document assumptions, testing, limitations and operating controls
  • Present the solution and answer technical review questions
  • Complete mentor review and any required improvements
OUTCOMECapstone-Level Technical Readiness
05/ 10
Certification Milestone

Professional Certification Readiness

Prepare for the Google Cloud Professional Machine Learning Engineer certification through exam-domain mapping, practice assessments, mock examinations, gap analysis and focused revision.

View requirements
  • Review every applicable official examination domain
  • Complete the assigned practice assessments and mock examinations
  • Use feedback and gap analysis to revise weaker areas
  • Complete the internal readiness review before attempting the external exam
OUTCOMECertification Exam Readiness

Certification preparation supports readiness but does not guarantee examination success or certification.

06/ 10
Eligibility Milestone

Internship Eligibility Assessment

Complete a structured evaluation of training performance, technical readiness, conduct, communication and available project fit.

View requirements
  • Complete the applicable training, labs, assignments and assessments
  • Submit the required projects and capstone for evaluation
  • Meet attendance, conduct and communication expectations
  • Satisfy internal evaluation and available project requirements
OUTCOMEEligible for Internship Consideration

Completion of training does not automatically guarantee internship allocation.

07/ 10
Professional Opportunity Milestone

Internship Opportunities

Eligible learners may be considered for supervised internal, associated, partner or industry project opportunities based on readiness and availability.

View requirements
  • Meet the applicable eligibility and performance standard
  • Be selected for an available project or professional assignment
  • Follow the project, documentation, Git, team and mentor-review process
  • Complete the assigned implementation, testing, proof-of-concept or presentation work
OUTCOMEOpportunity for Professional Experience

Allocation is subject to eligibility, performance, opportunity availability, business requirements and the applicable policies.

08/ 10
Performance-Based Opportunity

Paid Internship Opportunities

Learners selected for applicable paid opportunities may receive ₹15,000–₹25,000 per month, depending on role, performance, duration and project terms.

₹15,000–₹25,000/ Month*
View requirements
  • Qualify for and be selected into an applicable paid opportunity
  • Meet the ongoing attendance, performance and conduct standards
  • Fulfil the assigned project responsibilities and reporting expectations
  • Remain eligible under the applicable business and internship policies
OUTCOMEPotential Paid Professional Experience

Internship and stipend are not guaranteed programme benefits. Selection, allocation, duration and stipend are subject to eligibility, performance, availability, business needs and applicable policies.

09/ 10
Professional Development Milestone

Career Readiness

Prepare visible professional evidence and practise the communication, assessment and interview skills needed to pursue relevant roles.

View requirements
  • Refine the resume, LinkedIn profile, portfolio and GitHub evidence
  • Complete technical assessments and mock interviews
  • Practise certification-aligned and role-specific interview questions
  • Participate in communication review and career counselling
OUTCOMEInterview & Career Readiness
10/ 10
Career Opportunity Milestone

Placement Opportunities & Career Support

Access relevant job sharing, employer introductions, referrals, staffing or partner opportunities and hiring assessments where available.

View requirements
  • Complete the applicable career-readiness activities
  • Maintain accurate professional profiles and portfolio evidence
  • Participate in suitable hiring assessments and selection processes
  • Meet the independent requirements set by each employer or opportunity
OUTCOMEAccess to Relevant Career Opportunities

The programme includes placement assistance and career support, but it does not guarantee employment, an interview, compensation or any particular career outcome.

INCLUDED

The learning pathway

Structured training, instructor sessions, labs, assignments, projects, capstone work, certification preparation, assessments, exam voucher subject to terms and career-readiness support.

ELIGIBILITY-BASED OPPORTUNITY

Professional outcomes

Internship allocation, paid opportunities, stipend, employer introductions, interviews, placement and employment depend on performance, selection, availability and third-party requirements.

01Learn
02Practice
03Build
04Complete
05Prepare
06Qualify
07Experience
08Earn
09Prepare
10Pursue Opportunities
THE EDUMONK STANDARD

We guarantee the learning process. Professional opportunities are earned through performance and eligibility.

FROM LEARNING TO EXAM READINESS

A preparation system
with visible checkpoints.

  1. 01

    Learn

    Build the concepts and service-selection reasoning.

  2. 02

    Practise

    Complete guided labs and applied assignments.

  3. 03

    Assess

    Use domain checks to identify knowledge gaps.

  4. 04

    Mock exam

    Work through realistic readiness assessments.

  5. 05

    Review

    Target weak domains with mentor feedback.

  6. 06

    Attempt

    Use the included voucher subject to programme terms.

EXAM VOUCHERIncluded*

Certification preparation with accurate expectations.

An exam voucher is included subject to the selected programme, candidate eligibility, voucher availability and applicable examination policies. Google governs the certification exam, its policies and the certification award. Programme participation does not guarantee exam success or certification.

Sample Professional Machine Learning Engineer certification document supplied as a visual reference
ILLUSTRATIVE CERTIFICATION SAMPLE

See the professional credential learners prepare to attempt.

This supplied sample helps applicants recognise the type of external professional certification associated with this track. It is not an EduMonk certificate and is not issued for programme attendance. The actual certification is awarded only by Google after the candidate satisfies the applicable examination requirements.

Open supplied sample source ↗Review official certification details ↗
Sample design, branding and credential format may change. No certificate number, holder identity or exam result shown here represents an EduMonk learner.
INTERNSHIP OPPORTUNITIES FOR ELIGIBLE LEARNERS

Progress toward professional
internship opportunities.

Training completion is followed by an eligibility assessment—not an automatic internship. Selected learners may contribute to supervised practical work when suitable projects are available.

01Technical assignments
02Cloud labs and projects
03Architecture or code reviews
04Documentation and runbooks
05Git and team workflows
06Project presentations
07Mentor feedback
08Professional evaluation
Focused cohort. Measurable evaluation.

The five-learner batch supports individual lab help, progress review, mentor feedback and project critique. Allocation still depends on readiness and available requirements.

BEFORE YOU APPLY

Understand the fit
and expectations.

The programme includes foundational guidance, but professional progress still requires consistent attendance, deliberate practice and independent revision.

Recommended prerequisites

  • Working familiarity with Python and SQL
  • Foundation in statistics and supervised machine-learning concepts
  • A laptop suitable for notebooks, cloud labs and technical documentation
  • Commitment to offline attendance, assessments and project reviews
SELF-STUDYEDUMONK PROFESSIONAL PROGRAMME
Learning structureSelf-managedInstructor-led, sequenced and reviewed
Certification preparationTheory at your own paceDomain mapping, assessments and mock readiness
Hands-on practiceYou design itGuided labs and mentor-reviewed projects
Exam voucherPurchased separatelyIncluded subject to programme terms
Professional experienceSourced independentlyInternship opportunities for eligible learners, subject to allocation
Career preparationSelf-managedResume, LinkedIn, assessments and mock interviews
CohortNo cohort limitOnly five students per offline batch
PLACEMENT OPPORTUNITIES & CAREER SUPPORT

Build skills.
Prepare for opportunities.

Career support helps learners present their capability and prepare for relevant selection processes. It provides assistance and access where available, but does not guarantee interviews, placement, compensation or employment.

01Resume preparation
02LinkedIn optimisation
03Technical assessments
04Mock interviews
05Certification-based interview practice
06Career counselling
07Relevant opportunity sharing
08Internship-to-employment consideration where available
CAREER & INDUSTRY READING

Understand the profession
before choosing the credential.

Three track-specific explainers connect certification preparation with role boundaries, salary context, industry demand and the professional evidence employers can evaluate.

01
SALARY & INDUSTRY DEMAND

Machine Learning Engineer demand: production capability changes the conversation

The market distinguishes experiments from systems that can be operated responsibly.

Organisations increasingly need people who can move beyond a notebook: define an ML objective, prepare governed data, evaluate models, deploy inference, monitor behaviour and manage retraining. Generative AI adds new demand, but it also raises expectations around evaluation, safety, latency, cost and observability.

Salary depends on software-engineering strength, statistics and ML depth, production experience, platform ownership, industry and location. Treat market averages as dated directional evidence, never a guaranteed personal outcome. A stronger portfolio shows evaluation choices, deployment architecture, monitoring signals and limitations rather than only model accuracy.

  • Python, ML evaluation and software quality
  • Vertex AI, MLOps and deployment depth
  • Responsible AI and production ownership
Career and salary outcomes vary by education, experience, location, employer, role scope and market conditions. Certification or programme completion does not guarantee employment or compensation.
02
ROLE COMPARISON

Machine Learning Engineer vs Data Scientist vs AI Engineer

The titles overlap, but the centre of gravity is different.

A Data Scientist often focuses on analysis, experimentation and modelling. A Machine Learning Engineer turns models into dependable services and repeatable pipelines. An AI Engineer may work more broadly with foundation models, retrieval, agents and application integration, depending on the organisation.

This track is strongest for learners who want to own the path from problem framing through training, serving, monitoring and improvement. It deliberately combines modelling judgement with cloud architecture because production ML rarely succeeds as an isolated algorithm.

  • Data scientist: insight and experimentation
  • ML engineer: repeatable model systems
  • AI engineer: intelligent product integration
Career and salary outcomes vary by education, experience, location, employer, role scope and market conditions. Certification or programme completion does not guarantee employment or compensation.
03
PROFESSIONAL BENEFITS

Why professional ML preparation includes MLOps and responsible AI

A useful model must remain useful after deployment.

Model quality can change when incoming data shifts, user behaviour evolves or operational conditions differ from the development environment. MLOps creates repeatable training, evaluation, versioning, release and monitoring practices. Responsible AI adds the discipline to examine harms, security, privacy and inappropriate use.

Preparing across these areas builds a more complete professional narrative. Instead of saying that you trained a model, you can explain how it was evaluated, deployed, observed and governed. The official certification can validate knowledge after the examination; applied evidence shows how you use that knowledge.

  • Repeatable delivery and retraining
  • Monitoring, drift and incident response
  • Responsible and secure AI decisions
Career and salary outcomes vary by education, experience, location, employer, role scope and market conditions. Certification or programme completion does not guarantee employment or compensation.
FLEXIBLE FEE PAYMENT

Start with ₹30,000.
Pay the balance in EMIs.

PAYMENT PARTNERJodo Collect payment partner logoJodo Collect
ADMISSION PAYMENT₹30,000

Paid to begin admission and adjusted against the total programme fee.

REMAINING FEE BALANCEEMI option

Eligible applicants can request conversion of the remaining programme-fee balance into no-cost EMIs through Jodo Collect.

01

Begin admission

Pay ₹30,000 as the admission payment.

02

Request EMI

Apply with Jodo Collect for the remaining programme-fee balance.

03

Complete verification

Submit the requested KYC and eligibility information.

04

Review your schedule

Accept the approved tenure and repayment terms before proceeding.

Important: The ₹30,000 admission payment is part of the ₹1,00,000 programme fee; it is not an additional programme charge. GST is additional. EMI availability, no-cost terms, tenure and approval are subject to applicant eligibility, KYC, credit or lender assessment, Jodo Collect and lending-partner policies, and applicable terms. Processing fees, taxes or other disclosed charges may apply. EduMonk and PRAGYASHAL do not guarantee approval.

Ask about EMI options →
PROGRAMME INVESTMENT

₹1,00,000 + GST

Begin with a ₹30,000 admission payment that is adjusted against the programme fee. Eligible applicants can request Jodo Collect EMI conversion for the remaining fee balance. The exact payment schedule, venue, start date and seat status are confirmed during counselling before admission.

Structured professional trainingInstructor-led sessionsGuided labs and assignmentsProjects and capstoneCertification preparationPractice and mock assessmentsExam voucher subject to termsInternship eligibility assessmentCareer-readiness supportPlacement assistance

Payment clarification: the programme fee is charged for training, curriculum, instruction, learning resources, assessments, projects, certification preparation and career-readiness support. Payment does not create an entitlement to an internship, stipend, interview, placement or employment. EMI approval is subject to third-party eligibility and terms.

BEFORE YOU APPLY

Important programme
questions.

Is this programme administered by Google?

No. This is an EduMonk professional certification preparation programme delivered by PRAGYASHAL, a Google Cloud Partner. Google Cloud certification examinations, eligibility, policies and certification awards are governed by Google and the applicable examination provider.

Is the certification exam voucher included?

An exam voucher is included subject to the selected track, candidate eligibility, voucher availability, applicable programme terms and the examination policies in force at the time.

Is internship guaranteed after completing training?

No. Completion of training leads to an internship eligibility assessment, not automatic allocation. Consideration depends on attendance, assessments, lab and project performance, capstone completion, conduct, communication, technical readiness, internal evaluation and suitable project availability.

Will every learner receive a ₹15,000–₹25,000 stipend?

No. The range applies only to learners selected for applicable paid internship opportunities. Selection, allocation, duration and stipend depend on eligibility, performance, project availability, business needs and the applicable policies.

Is placement guaranteed?

No. The programme includes career-readiness support, relevant opportunity sharing and placement assistance where available. Employers independently decide who is interviewed, selected and hired, so employment and compensation are not guaranteed.

What happens if I am not selected for an internship?

You can continue using your completed labs, projects, capstone evidence, certification preparation and career-readiness work. The team may provide suitable portfolio, assessment, interview and opportunity-sharing support under the applicable programme terms, but later allocation is not promised.

How is internship eligibility determined?

Eligibility is evaluated using completion of training, attendance, assignments, assessments, lab performance, project and capstone quality, technical understanding, problem solving, professional conduct, communication, internal evaluation and the requirements of available projects.

Does the programme guarantee certification or employment?

No. Certification requires the candidate to satisfy the external examination requirements, and final hiring decisions remain with employers. The programme provides preparation and career support, but neither certification success nor employment is guaranteed.

Where is the programme delivered?

The programme is delivered offline at designated corporate office locations. The assigned venue, schedule and upcoming batch details are confirmed during counselling before admission.

What does the programme fee cover?

The ₹1,00,000 fee plus applicable GST covers structured training, curriculum, instructor sessions, learning resources, labs, assignments, projects, capstone work, assessments, certification preparation, mock examinations, the exam voucher subject to terms and career-readiness support. Payment does not create an entitlement to an internship, stipend, placement or employment.

Can the fee be paid in instalments?

Yes. You can begin admission with ₹30,000, which is adjusted against the ₹1,00,000 programme fee. Eligible applicants can request conversion of the remaining programme-fee balance into no-cost EMIs through Jodo Collect. GST is additional. EMI availability, tenure and approval are subject to KYC, applicant eligibility, credit or lender assessment, Jodo Collect and lending-partner policies, and applicable terms. Processing fees, taxes or other disclosed charges may apply; approval is not guaranteed.

IMPORTANT PROGRAMME DISCLAIMER

This programme provides structured training, practical learning, certification preparation, assessments, projects, internship eligibility evaluation, career-readiness support and placement assistance. Internship allocation is not automatic and is subject to eligibility, performance, conduct, technical readiness, project availability, business requirements and applicable policies. Paid internships and the stated stipend range apply only to selected opportunities and are not guaranteed. Certification preparation and an exam voucher do not guarantee examination success or certification. Placement assistance provides access to support and relevant opportunities but does not guarantee interviews, employment, role, employer, location, salary or any other career outcome.

YOUR CAREER JOURNEY STARTS WITH MILESTONE ONE

Begin the Machine Learning Engineer
professional pathway.

Starts 25 SeptemberMaximum 5 LearnersCertification PreparationInternship Opportunities*Career Support

*Eligibility, selection, availability and programme terms apply. No professional outcome is guaranteed.

Apply for counselling →Compare with other tracks ↑