MLOps & AIOps Course in Hyderabad
Operationalise machine learning models and apply AI to IT operations
The MLOps and AIOps course covers two connected areas. In the MLOps part you learn to take machine learning models from notebooks to reliable services: reproducible environments, data and model versioning with Git and DVC, experiment tracking and a model registry with MLflow, training pipelines, packaging with Docker, serving with FastAPI, CI/CD with GitHub Actions, deployment on Kubernetes at a working level, and monitoring for data drift and model performance. In the AIOps part you learn how machine learning supports IT operations: collecting logs, metrics and traces, anomaly detection on time series, alert noise reduction and event correlation, log pattern mining, incident triage and root-cause analysis support, forecasting capacity and automated remediation, using tools such as Prometheus, Grafana, OpenTelemetry and the Elastic stack. The course suits data scientists, developers, DevOps and operations professionals with intermediate Python and basic machine learning. A capstone connects both: a deployed model with monitoring and an anomaly detector on operational data. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
MLOps Fundamentals & Lifecycle
- •From notebook to production: data, features, training, deployment and monitoring
- •MLOps maturity levels and team roles
- •Common failure modes: leakage, drift and training-serving skew
- •Lab: Review a notebook and list production gaps
- •Outcome: Describe the full model lifecycle
Python Engineering & Git for ML
- •Structuring ML projects, configuration and logging
- •Unit and data tests with pytest
- •Git branching and code review for ML code
- •Lab: Refactor a notebook into a tested Python package
- •Outcome: Write production-quality ML code
Data & Model Versioning
- •Versioning datasets and models with DVC
- •Data validation with schema and quality checks
- •Reproducible pipelines and lineage
- •Lab: Version a dataset and reproduce an old experiment
- •Outcome: Reproduce any model you have trained
Experiment Tracking & Model Registry
- •MLflow tracking: parameters, metrics and artifacts
- •Model packaging and the registry; aliases and approval flow
- •Comparing runs and selecting models
- •Lab: Track experiments and promote a model through the registry
- •Outcome: Manage models like software releases
Training Pipelines & Orchestration
- •Pipeline design: ingest, validate, train, evaluate and register
- •Orchestration with Apache Airflow or similar tools
- •Hyperparameter tuning and retraining triggers
- •Lab: Build a scheduled training pipeline
- •Outcome: Automate model retraining
Model Serving & Containers
- •Batch, online and streaming serving patterns
- •Serving with FastAPI and packaging with Docker
- •Performance, latency and model formats such as ONNX
- •Lab: Containerise and load test a prediction API
- •Outcome: Serve models reliably
CI/CD & Kubernetes for ML
- •CI for ML: tests, data checks and model evaluation gates
- •CD with GitHub Actions; canary and shadow deployments
- •Kubernetes basics for model services; Helm overview
- •Lab: Build a pipeline that deploys a model after tests pass
- •Outcome: Release models automatically and safely
Model Monitoring & Governance
- •Data drift, concept drift and performance monitoring
- •Evidently or similar tools; alerts and retraining triggers
- •Model documentation, fairness checks and audit trails
- •Lab: Detect drift on a production-like stream and trigger an alert
- •Outcome: Keep models healthy after release
Observability Foundations for AIOps
- •Logs, metrics and traces; OpenTelemetry basics
- •Prometheus, Grafana and the Elastic stack
- •Service level indicators and objectives, alerting practices
- •Lab: Instrument a service and build a dashboard
- •Outcome: Collect the data AIOps needs
Anomaly Detection & Forecasting for Operations
- •Time series basics: seasonality, trend and baselines
- •Statistical and machine learning anomaly detection, including isolation forest and autoencoders
- •Forecasting capacity and load with Prophet or ARIMA-style methods
- •Lab: Detect anomalies in CPU and latency metrics
- •Outcome: Flag unusual behaviour with fewer false alarms
Log Analytics, Event Correlation & Root Cause Support
- •Log parsing, template mining and clustering
- •Alert deduplication and correlation using time and topology
- •Assisting incident triage and root-cause analysis with ML and LLM summaries
- •Lab: Cluster logs and group related alerts into incidents
- •Outcome: Reduce alert noise and speed up triage
Automated Remediation & Incident Workflows
- •Runbook automation with scripts, Ansible or workflow tools
- •ChatOps and ticketing integration
- •Safe automation: approvals, limits and rollbacks
- •Lab: Automate a safe remediation for a recurring alert
- •Outcome: Close the loop from detection to action
Capstone & Interview Preparation
- •Capstone: deploy a model with CI/CD and monitoring, plus an anomaly detector on operational metrics
- •Architecture documentation and GitHub portfolio
- •MLOps and SRE-style interview practice
- •Outcome: A portfolio project covering both areas
Tools Covered
Projects Included
- Reproducible Training Pipeline: Version data with DVC and track experiments in MLflow; deliverable is a pipeline that reproduces a result from a commit.
- Model Serving and CI/CD: Containerise a model API and deploy through GitHub Actions with tests; deliverable is a working pipeline and rollout notes.
- Drift Monitoring Dashboard: Monitor input and prediction drift with alerts; deliverable is a dashboard and a retraining trigger.
- Operational Anomaly Detector: Detect anomalies in service metrics and reduce false alarms; deliverable is a notebook, a service and a tuning report.
- Capstone MLOps and AIOps Platform: Connect a deployed model with monitoring and automated alert correlation; deliverable is a GitHub repository and architecture diagram.
Start Your Journey Today
Interested in MLOps & AIOps? Fill in your details and we'll get back to you.
Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the MLOps & AIOps course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on MLOps & AIOps.
Certificate of Completion
MLOps & AIOps
This certifies that
[Your Name]
has successfully completed the
MLOps & AIOps
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured MLOps & AIOps course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
What does the MLOps & AIOps course syllabus cover?
What is AIOps?
How is this different from the published MLOps course?
Do I need machine learning knowledge before joining?
Is Kubernetes required?
How is this different from the LLMOps course?
Are there certifications linked to this course?
Which jobs can I target after this course?
How long is the MLOps & AIOps course at GloryTecks, and how many hours a week?
Is the MLOps & AIOps course available as live online training?
What is the fee for the MLOps & AIOps course in Hyderabad?
Will I get a certificate after the MLOps & AIOps course?
What placement support do MLOps & AIOps students get?
Best MLOps & AIOps Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include MLOps Engineer, Machine Learning Engineer, AIOps Engineer, Site Reliability Engineer (entry level); support is guidance with the job search, not a guarantee of a job.