Program Highlights Engineered for Career Breakthroughs
A high-intensity, career-first learning curriculum developed alongside industry architects to deliver tangible production competence, verified credentials, and guaranteed corporate experience.
6 Months Instructor-Led
Live interactive classes led by Senior Engineers & Tech Leads from top product firms. 150+ hours of live instruction, recorded sessions, and dedicated weekly 1-on-1 doubt clearing.
IBM Learning Ecosystem Access
Authorized access to the IBM SkillsBuild enterprise learning portal, official cloud tools, and verified IBM credentials that authenticate your skills to global employers.
Microsoft Learning Ecosystem Access
Direct access to Microsoft Learn resources, real cloud sandbox architectures, and scenario-based hands-on exercises aligned with industry certification standards.
Projects: Hands-on Portfolio
Build 10+ production-grade end-to-end applications deployed to GitHub and cloud infra. Create a public portfolio of real architectures that impresses technical interviewers.
Credentials: Credential Pathways
Receive verifiable digital badges and accredited certificates recognized across 500+ recruitment partners. Directly shareable on LinkedIn, resumes, and candidate profiles.
Internship*: 6-Month Paid Corporate Internship*
Guaranteed entry into real corporate project teams upon completing capstones. Earn an assured monthly stipend along with an official corporate experience letter.
What You Will Learn
Comprehensive curriculum engineered by Staff Engineers to bridge theoretical concepts and production deployment.
Transition machine learning models from prototype notebooks to reliable software engineering systems.
- ✓ ML System Design: Defining latency requirements, throughput, compute budgets, and failure modes
- ✓ Code Modularization: Refactoring notebooks into clean Python packages with poetry and pytest
- ✓ Data Contracts & Pydantic: Type safety, schema enforcement, and input data validation
- ✓ Config Management: Hydra and OmegaConf for hierarchical experiment configurations
Track experimental runs, compare hyperparameter surfaces, and maintain an enterprise model catalog.
- ✓ MLflow Tracking: Logging parameters, metrics, artifacts, and PyTorch/TensorFlow autologging
- ✓ MLflow Model Registry: Staging, Production, and Archived model lifecycle transitions
- ✓ Hyperparameter Optimization: Distributed Optuna integration with early stopping algorithms
- ✓ Weights & Biases (W&B): Interactive run visualizers, loss curves, and artifact dependency graphs
Maintain immutable data versions and eliminate train-serve skew with centralized feature management.
- ✓ Data Version Control (DVC): Tracking massive datasets in cloud storage (S3/GCS) with Git pointers
- ✓ Data Lineage: Reproducible pipelines with dvc.yaml and automated pipeline stage execution
- ✓ Feature Stores with Feast: Online (Redis) and Offline (Snowflake/BigQuery) unified feature retrieval
- ✓ Automated Data Validation: Great Expectations assertions to detect distribution shifts before training
Orchestrate automated, fault-tolerant training pipelines across distributed compute clusters.
- ✓ Kubeflow Pipelines (KFP): Authoring containerized pipeline components in Python
- ✓ Distributed Training: PyTorch Distributed Data Parallel (DDP) and Ray Train on Kubernetes
- ✓ Apache Airflow for ML: Orchestrating recurring ETL, validation, and training workflows
- ✓ Resource Management: GPU allocation, spot instance fault tolerance, and node affinity scheduling
Expose machine learning predictions via high-throughput, low-latency production microservices.
- ✓ Inference Frameworks: BentoML, TorchServe, and Triton Inference Server with dynamic batching
- ✓ Model Optimization: ONNX Runtime conversion, TensorRT acceleration, and INT8 quantization
- ✓ FastAPI Microservices: Asynchronous request handling, health checks, and Docker containerization
- ✓ Serving Patterns: Shadow deployments, A/B testing splits, and offline batch prediction engines
Continuously audit deployed models for performance degradation, bias, and distribution drift.
- ✓ Data & Concept Drift: Evidently AI, Kolmogorov-Smirnov statistical tests, and PSI (Population Stability Index)
- ✓ Model Observability: Tracking inference latency, prediction distribution, and error rates in Grafana
- ✓ Automated Retraining: Triggering CI/CD retraining workflows when drift thresholds are breached
- ✓ Production Capstone: Building a complete self-healing, automated MLOps continuous delivery platform
Tools & Technologies
Hands-on enterprise stack used across the program.
6-Month Career Transition Roadmap
A structured, milestone-driven progression designed to take you from foundational concepts to production-grade deployment and enterprise employment.
Foundations & Core Engineering
Master fundamental syntax, systems architecture, statistical principles, and core tooling required to build reliable technical foundations.
Advanced Implementation & Labs
Deep-dive into production frameworks, cloud integrations, automated pipelines, and enterprise-grade hands-on laboratory architectures.
Guaranteed Corporate Internship
Work on real corporate projects during your guaranteed 6-month paid internship* with monthly performance stipends and verified industry credentials.
Placement & Career Advancement
Access 1-on-1 interview preparation, resume branding, mock evaluations, and direct recruitment pathways across our 500+ corporate partners.
Production Capstone Projects
Build a portfolio of enterprise-grade projects evaluated by working tech leaders.
Zero-Downtime Model Continuous Retraining Pipeline
Automated GitOps pipeline that triggers training on data drift, registers model artifacts, and deploys canary containers.
Enterprise Real-Time Feature Store Implementation
Centralized feature registry supplying sub-millisecond online inference features and consistent offline training datasets.
Model Performance & Concept Drift Telemetry Hub
Production monitoring dashboard visualizing prediction drift, Kolmogorov-Smirnov statistics, and latency degradation.
High-Performance GPU-Optimized Triton Inference Server
Dynamic batching model serving architecture handling concurrent vision and NLP workloads under 20ms p99.
Career Opportunities & Outcomes
Prepare for top-tier roles across our 500+ corporate recruitment partners.
High-Demand Career Tracks:
Frequently Asked Questions
Everything you need to know about admissions, curriculum, and internships.
This program is beginner-friendly and starts from fundamental core principles before advancing to production-grade enterprise implementations. Prior exposure to basic computing or programming is helpful but not mandatory.
Upon fulfilling course benchmarks and capstone assessments, you are placed into a guaranteed 6-month corporate paid internship* offering competitive monthly performance stipends with verified corporate experience letters and industry credentials.
You will earn official credentials and digital badges aligned with Production ML Aligned, alongside an official AI Campus Certificate of Mastery that is verifiable on LinkedIn and recognized by 500+ corporate hiring partners.
The program fee of ₹80,000 (inclusive of GST) can be paid via 0% interest no-cost EMI starting from ₹6,666/month with zero down payment required.
Graduates qualify for high-growth roles in AI & Data with corporate salary packages typically spanning from ₹10 - 28 LPA, backed by our dedicated 100% placement support team.
Confused about your career path? Don't wait — talk directly to a mentor.
Get transparent, personalized guidance on curriculum, eligibility, scholarship waivers, and the guaranteed monthly paid corporate internship*. No sales pressure — just honest advice from working tech leaders.
