SCHOOLOFCOREAI

LIVE ONLINE • INSTRUCTOR-LED • 5-MONTH MLOPS PROGRAMME

MLOps Course in Hyderabad

Learn how ML systems move from reproducible data and training to model release, monitoring, and controlled retraining through a live production-focused MLOps programme. Built for engineers who need to ship and operate ML reliably.

Designed for ML engineers, data scientists, DevOps engineers, and platform teams in Hyderabad's GCC and cloud ecosystem.

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Next cohort: Confirm current dates with admissions

FROM NOTEBOOK → PRODUCTION ML SYSTEM

EXPERIMENT
↓
MODEL CANDIDATE
↓
RELEASE
↓
SERVE
↓
MONITOR
↓
↺ RETRAIN

5 months

Programme duration

₹60,000

One-time fee

Live Online

Instructor-led format

3

Production projects + capstone

Included

Mentorship & career support

Live Online Program

Live MLOps Training in Hyderabad for ML and Platform Engineers

This is a live online, instructor-led programme. You join from Hyderabad (or anywhere) and participate in real-time sessions, mentor reviews, and project builds. The programme runs for 5 months with weekday and weekend batch options.

Hyderabad's GCCs and cloud-native teams in HITEC City, Gachibowli, and Madhapur are scaling production AI systems — creating demand for engineers who can deploy, monitor, and operate ML reliably.

Live Online MLOps Training for Hyderabad Learners

The programme is fully live online. You join real-time sessions from Hyderabad or anywhere in India. There is no in-person requirement — all sessions, reviews, and project work happen remotely with live mentor support.

Where MLOps Skills Fit in Hyderabad Engineering Work

  • · GCC and captive teams scaling ML platforms and pipelines
  • · Cloud-native product teams building AI infrastructure on AWS and Azure
  • · BFSI technology teams needing model governance, registry, and monitoring
  • · Enterprise IT teams moving from pilot ML to production deployment

Who Should Join

Who Should Take This MLOps Course in Hyderabad?

MLOps is for engineers who need to move models from experiments to controlled production. If you work with ML but struggle with deployment, monitoring, or reproducibility, this programme builds the missing engineering layer.

Data Scientists

You have notebook and modelling experience. You need reproducibility, release discipline, monitoring, and controlled retraining to move into production roles.

Best fit if

you can train models but cannot reliably deploy, monitor, or retrain them.

ML Engineers

You handle model engineering. You need lifecycle automation, distributed training, experiment tracking, and release control for production systems.

Best fit if

you build models but lack the infrastructure and release discipline for production.

Data Engineers

You build pipelines and data systems. You need training lifecycle, experiment tracking, and model delivery workflows to bridge into MLOps.

Best fit if

you understand pipelines but need ML-specific lifecycle and serving skills.

DevOps / Platform Engineers

You own CI/CD and infrastructure. You need ML-specific pipelines, model registry, serving patterns, and drift handling.

Best fit if

you know DevOps but need ML-specific deployment, registry, and monitoring.

AI Engineers

You develop AI systems. You need reliable model lifecycle and production ML operations to deploy and maintain AI systems at scale.

Best fit if

you build AI applications but need production lifecycle and operational discipline.

Recommended foundation

Basic Python and Git are required to start. Beginner-level ML understanding (training + evaluation) is helpful but not mandatory. Docker and Kubernetes foundations are built from scratch within the programme.

You may need a foundation course first if

you have no Python or Git experience, or you are looking for a data science fundamentals course rather than production ML engineering.

This is not a beginner Python course, a data science theory course, or a generic DevOps bootcamp. It is a production ML engineering programme.

Machine Learning Course Generative AI Course

MLOps Lifecycle

What You Actually Learn Across the MLOps Lifecycle

The MLOps lifecycle is not a tools list. It is a controlled production workflow where each stage has a responsibility, a failure mode, and an engineering decision that allows the system to move forward.

Data Orchestration and Validation

RESPONSIBILITY

Validate and orchestrate event-driven data inputs. Ensure upstream changes do not silently corrupt training or serving.

FAILURE MODE

Schema drift from upstream sources, missing fields, or silent data quality degradation.

ENGINEERING DECISION

Validation result and quarantine recovery path. Move forward only when data is validated and versioned.

TOOLS

PanderaGreat ExpectationsDagster

Curriculum

MLOps Course Curriculum and 5-Month Learning Path for Hyderabad

The curriculum follows the ML delivery lifecycle. Each month progressively adds capabilities, building from data foundations to a full production MLOps system.

DATA → VALIDATE → VERSION

Focus: MLOps foundations, Python essentials, ML foundations, Git, data pipelines, versioning (DVC)

DATAVALIDATEVERSION

Data Foundations and Reproducibility

Focus: MLOps foundations, Python essentials, ML foundations, Git, data pipelines, versioning (DVC)

Training and Experiment Tracking

Focus: Distributed training (Ray Train), experiment tracking (MLflow), hyperparameter tuning (Optuna)

Packaging, Registry and Release

Focus: Docker for ML, MLflow Model Registry, model serving (FastAPI), CI/CD for ML

Monitoring, Drift and Retraining

Focus: Prometheus/Grafana monitoring, drift detection (Evidently), controlled retraining pipeline

Managed Cloud Deployment and Capstone

Focus: AWS SageMaker deployment, capstone project, portfolio review, resume & mock interviews

Explore the Complete MLOps Curriculum

Projects

MLOps Projects You Will Build

You build connected production systems across the ML delivery lifecycle — not isolated tool demos. Each project connects to the next, culminating in an AWS SageMaker capstone.

Event-Driven Data Orchestration and Validation

Problem: ML pipelines break when upstream data changes. Without orchestration, validation, and quarantine paths, bad data silently corrupts training and serving.

Redpanda/Kafka↓Dagster↓Pandera↓Valid / Quarantine

Deliverables: Orchestrated pipeline definition, validation suite, quarantine logic, and a data-quality runbook.

Review: Pipeline correctness, validation coverage, orchestration reliability, and quarantine explanation.

RedpandaDagsterPandera

Scope Comparison

MLOps vs ML Engineering, LLMOps and AIOps

MLOps, ML Engineering, LLMOps, and AIOps are related but distinct. Understanding the scope of each helps you choose the right path.

ML Engineering

Model development and engineering — training, evaluation, feature engineering, model architecture.

Learn more

MLOps

Current

Production lifecycle for ML systems — data pipelines, versioning, training, registry, release, serving, monitoring, retraining.

LLMOps

Production lifecycle for language-model systems — LLM fine-tuning, RAG, prompt versioning, vector databases, LLM serving.

Learn more

AIOps

Broader production AI stack combining ML, LLM, and agent operations — multi-model platforms, orchestration, observability.

Learn more

Hyderabad Engineering Context

Learning MLOps From Hyderabad

Hyderabad is one of India's largest GCC and cloud engineering hubs. Companies in HITEC City, Gachibowli, and the financial district are building production AI systems that require MLOps engineers — not just data scientists who can train models, but engineers who can deploy, monitor, and operate ML at scale.

Live Online MLOps Training for Hyderabad Learners

The programme is fully live online. Hyderabad-based engineers join real-time sessions without commuting. The content is contextualized for Hyderabad's GCC and cloud ecosystem — but you can join from anywhere.

Where MLOps Skills Fit in Hyderabad Engineering Work

  • · GCC and captive teams — Microsoft, Amazon, Google, Deloitte, Accenture, NVIDIA
  • · BFSI technology — HSBC, Wells Fargo, Capgemini building risk and compliance ML systems
  • · Cloud-native product teams — ServiceNow, ValueLabs, Genpact scaling AI platforms
  • · Enterprise IT — Infosys, Tech Mahindra managing ML lifecycle for global clients

Local Engineering Ecosystem

AI and Engineering Companies in Hyderabad

Microsoft

Cloud & AI Platform

Amazon

AWS & ML Infrastructure

Google

Vertex AI & ML Ops

Qualcomm

AI Model Deployment

Deloitte

MLOps Consulting

Accenture

Enterprise ML Pipelines

NVIDIA

ML Platform Automation

ServiceNow

AI Infrastructure

HSBC Tech

BFSI AI/ML Ops

Tech Mahindra

Data & ML Platform Ops

Infosys

AI Platforms & ML Lifecycle

Capgemini

Cloud ML Pipelines

Companies listed represent Hyderabad's engineering ecosystem. Not hiring partners. No placement guarantee.

How the Program Is Taught

How the MLOps Program Is Taught

The programme follows a Build → Review → Debug → Operate loop. You learn concepts in live sessions, build systems in assignments, get mentor reviews on engineering quality, and develop the operational mindset needed for production ML.

LEARN→BUILD→REVIEW→BREAK→DEBUG→OPERATE

Live Technical Sessions

Live instructor-led teaching on each lifecycle stage — not recorded videos.

Production System Builds

Each assignment builds a connected piece of the ML delivery lifecycle, not isolated demos.

Engineering Reviews and Debugging

Mentors review your work against production standards. You debug real failure modes, not toy examples.

Lifecycle and Failure Thinking

You learn to think in terms of lifecycle stages, failure modes, and engineering decisions — not tool buttons.

Certification & Career

MLOps Certification and Career Support

Course Completion Certificate

You receive a course completion certificate after completing all assignment-based skill alignment requirements. Your certificate is backed by your project work and submission outcomes — the real proof of what you can build.

Portfolio Evidence

Your certificate is backed by reviewed, production-ready projects — repositories, pipelines, tests, and runbooks as verifiable proof.

Career Support

Resume review, GitHub/project presentation, mock interviews, interview preparation, job-search guidance, and referral connections where available.

No Placement Guarantee

We do not guarantee placement. We prepare you with real production skills, portfolio evidence, and interview readiness.

Market Evidence

MLOps Engineer Salary and Hiring in Hyderabad

MLOps is one of the highest-paying AI engineering roles in Bangalore. Companies are actively hiring engineers who can move models from experiments to controlled production.

MLOps Engineer

Glassdoor Bangalore, Jul 2026

1-3 yrs

₹8L – ₹16L/yr

MLOps Engineer

Glassdoor Bangalore, Apr 2026

4-6 yrs

₹16L – ₹23L/yr

MLOps Engineer

Glassdoor Bangalore, Mar 2026

4-6 yrs

₹28L – ₹32L/yr

MLOps Engineer

Glassdoor Bangalore, Jun 2026

10-14 yrs

₹51L – ₹59L/yr

TYPICAL (HYDERABAD)

₹12.7L – ₹16.5L / year (3-6 years experience)

SENIOR (9-12 YRS)

₹22L – ₹25L / year (9-12 years experience)

Companies hiring MLOps engineers in Hyderabad

Fractal AnalyticsDeloitteTredenceCognizantAccentureSigmoidImpact AnalyticsNielsenIQ

Salary data sourced from AmbitionBox (178 salaries, updated Aug 2026) and Glassdoor Bangalore (50 salaries, Aug 2026). Ranges vary by experience, company, and skills.

Job titles this programme prepares you for

  • · MLOps Engineer / ML Platform Engineer
  • · ML Engineer (Production)
  • · Data / ML Infrastructure Engineer
  • · Model Reliability / Model Ops Engineer

Why MLOps is in demand now

  • · Companies productize AI → need reliable pipelines
  • · Compliance & cost control push for strong Ops
  • · Upskilling wave among engineers in India
  • · Bengaluru has 69 reported MLOps salaries — highest among Indian cities
Explore the Complete MLOps Curriculum

Course Fee and Cohort

MLOps Course Fee, Format and Next Cohort in Hyderabad

The fee is a one-time payment for the full 5-month live programme. There is no hidden cost — mentorship, career support, and certificate are all included.

FEE

₹60,000

DURATION

5 months

FORMAT

Live online

LEVEL

MLOps Specialization

COHORT

Admissions Open

MENTORSHIP

Included

What the fee covers

  • · 5 months live instructor-led programme
  • · Hands-on assignments and 3 production projects
  • · Mentor support and engineering reviews
  • · Career preparation: resume, GitHub, mock interviews
  • · MLOps Course Completion Certificate

Career support

Career support is included — resume review, GitHub/project presentation, mock interviews, interview preparation, job-search guidance, and referral connections where available. No placement guarantee.

Get Syllabus & Fees View Full MLOps Curriculum Talk to a Course Advisor

FAQ

MLOps Course in Hyderabad — FAQs

Is this MLOps course in Hyderabad online or in-person?
It is a live online, instructor-led programme. You join from Hyderabad (or anywhere) and participate in real-time sessions, mentor reviews, and project builds. There is no in-person requirement — the entire programme is designed for remote, live participation.
What roles can I target after the MLOps course in Hyderabad?
MLOps Engineer, ML Engineer (Production), AI Platform Engineer, Data/ML Infrastructure Engineer, and Model Reliability Engineer. Hyderabad's GCCs in HITEC City and Gachibowli — including Microsoft, Amazon, Deloitte, NVIDIA, and HSBC — actively hire for these roles.
Is there an EMI or installment option for the fee?
Yes, EMI options are available. Contact our team for current installment plans and payment details.
How is this MLOps course different from other institutes in Hyderabad?
The difference is production depth. You build the full ML delivery lifecycle — data validation, versioning, distributed training, model registry, CI/CD, serving, monitoring, drift detection, and retraining — as one connected system, not isolated tool tutorials. Sessions are live, projects are reviewed against production criteria, and the goal is work you can explain and defend in interviews.
Do I need to be in Hyderabad to attend this course?
No. The programme is fully live online. While the content is contextualized for Hyderabad's GCC and cloud ecosystem — companies in HITEC City, Gachibowli, and the financial district — you can join from anywhere in India or internationally.
What is the MLOps engineer salary in Hyderabad?
MLOps engineers in Hyderabad typically earn ₹10L to ₹15L per year with 3-6 years of experience, with senior roles reaching ₹18L to ₹25L. Hyderabad's GCCs and cloud teams offer competitive compensation, though Bangalore generally has a higher salary band. Salaries vary by company, cloud skills, and production experience.
What is the School of Core AI MLOps Certification Course?
A 5-month live online programme covering the full ML delivery lifecycle — data validation, versioning, distributed training, experiment tracking, model registry, CI/CD, serving, monitoring, drift detection, and controlled retraining. You build connected production systems across the lifecycle, culminating in an AWS SageMaker capstone.
How does MLOps differ from traditional DevOps?
DevOps manages software release cycles. MLOps handles the additional complexity of ML systems — data drift, model decay, training-serving skew, and the need for reproducible experiments. The infrastructure and lifecycle discipline is different because ML systems depend on data, models, and code together, not just code.
Is this MLOps course suitable for Data Scientists, ML Engineers, or DevOps professionals?
Yes. Data scientists learn reproducibility, release discipline, and monitoring. ML engineers learn lifecycle automation, distributed training, and serving. DevOps engineers learn ML-specific pipelines, model registry, and drift handling. Basic Python and Git are required — Docker and Kubernetes foundations are built from scratch in the programme.
What is the duration and format of the MLOps certification programme?
5 months, live online, instructor-led. Weekday and weekend batches are available. The programme follows a Learn → Build → Review → Break → Debug → Operate loop, moving from data foundations to a full production MLOps system with an AWS SageMaker capstone.
What are the technical prerequisites to join the MLOps course?
Basic Python and Git are required. Beginner-level ML understanding (training and evaluation) is helpful but not mandatory. Docker and Kubernetes foundations are built from scratch within the programme — no prior DevOps experience is needed.
Which tools and platforms are covered in the curriculum?
Pandera and Great Expectations for validation, DVC for versioning, Ray Train for distributed training, MLflow for tracking and registry, Docker/Kubernetes/Helm for deployment, FastAPI and TorchServe for serving, Prometheus/Grafana/Evidently for monitoring and drift detection, and AWS SageMaker for managed cloud deployment.
What kind of MLOps projects will I build?
Five connected production builds plus a capstone: event-driven data orchestration, reproducible versioning, distributed training with experiment tracking, model registry with CI/CD release, drift detection with controlled retraining, and an AWS SageMaker deployment capstone. Each project connects to the next — not isolated tool demos. See the production projects behind the curriculum at /mlops/projects/ and inspect the end-to-end implementation at /mlops/projects/end-to-end-mlops-project/.
How is MLflow used in this course?
MLflow Tracking is used for experiment tracking — logging parameters, metrics, and artifacts across training runs. MLflow Model Registry is used for versioning, promoting, and managing model lifecycle through governed registry gates. You learn to compare runs, select candidates, and manage the transition from staging to production.
How does the course teach Kubernetes for ML workloads?
You learn production patterns for ML on Kubernetes — Helm for package management, deployment strategies for inference APIs, and resource management for training and serving workloads. The focus is on using Kubernetes as part of the release pipeline, not as a standalone DevOps topic.
Does the course cover model drift, data quality, and observability?
Yes. You implement monitoring with Prometheus and Grafana, drift detection with Evidently, and a controlled retraining pipeline that validates challenger models before promotion. The goal is to detect drift, alert on it, and respond with a validated retraining decision — not just observe dashboards.
Will I receive a certificate upon completion?
Yes. You receive an MLOps Course Completion Certificate after completing all assignment-based skill alignment requirements and the capstone project. The certificate is backed by reviewed, production-ready project work — repositories, pipelines, tests, and runbooks.
How does the career support work?
Career support includes resume review, GitHub and project presentation review, mock interviews, interview preparation, job-search guidance, and referral connections where available. We do not guarantee placement — the focus is on preparing you with real production skills and interview readiness.
What is the difference between MLOps and LLMOps?
MLOps covers the production lifecycle for traditional ML systems — data pipelines, training, registry, serving, monitoring, and retraining. LLMOps covers the production lifecycle for language-model systems — LLM fine-tuning, RAG, prompt versioning, vector databases, and LLM serving. This course teaches the core MLOps foundation; both rely on the same CI/CD and monitoring principles.

MLOps Course Reviews

Honest feedback from working professionals who upskilled with SCAI.

4.9/ 5

Read MLOps Course reviews on Google — real experiences from learners covering mentorship quality, project depth, and career transitions.

Next Step

Get the MLOps Course Syllabus and Fee Details

Tell us your current role, technical background and what you want to build with MLOps. The team can help you decide whether this 5 months programme is the right next step for your career in Hyderabad.

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