Advanced Machine Learning & Production AI Systems
CodeWave Academy's Advanced Machine Learning Bootcamp is Sri Lanka's hands-on training program for engineers and researchers who want to go beyond model training and build production-grade AI systems. Unlike introductory machine learning courses in Sri Lanka, this 12-week program covers deep learning with PyTorch, computer vision, NLP, MLOps, and end-to-end ML system design — the skills companies actually hire ML engineers for.
12 weeks · PyTorch · MLOps · 3 end-to-end production ML systems · taught by Gihan, Lead AI & ML at Ascentic
CodeWave Academy's Advanced Machine Learning Bootcamp is a 12-week program in Sri Lanka covering PyTorch, computer vision, NLP, MLOps, and production ML system design. The fee is LKR 64,000 (33% off the standard LKR 96,000) and sessions run live online with weekly consultation.
CodeWave Academy is a coding and machine learning bootcamp provider based in Sri Lanka offering live online programs for software engineers and professionals.
The Advanced Machine Learning Bootcamp runs for 12 weeks (3 months) with 36 hours of live training, 1 hour / week consultation, and ~7 hours / week of take-home work.
Students build three end-to-end production machine learning systems plus a capstone project reviewed by Gihan, Lead AI & ML at Ascentic.
Prerequisites include comfortable Python skills, basic supervised learning knowledge, and experience training at least one ML model with scikit-learn, TensorFlow, or PyTorch.
The next batch of the Advanced Machine Learning Bootcamp starts on 12 May 2026.
Graduates receive a CodeWave Academy Certificate of Completion and portfolio projects suitable for ML Engineer, AI Engineer, and Data Scientist roles in Sri Lanka or remote international teams.
Go beyond notebook ML. Design, deploy, and operate production AI systems — with weekly 1:1 feedback from an industry AI lead.
Most machine learning courses in Sri Lanka stop at training a model in a notebook. But in industry, that's where the real work begins. Companies hiring ML engineers in Colombo and abroad expect you to design evaluation strategies, deploy models as APIs, version experiments, build retraining pipelines, and make engineering trade-offs between accuracy, latency, cost, and scalability.
This bootcamp is built around that gap. Over 12 weeks, you'll design and implement three end-to-end machine learning systems based on real-world business use cases — not toy datasets — and graduate thinking like an industry ML engineer and AI architect.
By the end of the program, you will be able to:
Live lectures + hands-on labs: 3 hours/week · Weekly consultation: 1 hour / week · Take-home: ~7 hours / week
| Week | Topic | Assignment |
|---|---|---|
| Week 1 | Fundamentals of Machine Learning | Build and evaluate a baseline ML model for a tabular dataset |
| Week 2 | Fundamentals of Deep Learning (PyTorch) | Implement a simple feedforward neural network in PyTorch |
| Week 3 | Computer Vision | Train an image classifier using CNNs on a real dataset |
| Week 4 | Natural Language Processing | Build a text classification or sentiment analysis model |
| Week 5 | Transfer Learning | Fine-tune a pretrained vision or NLP model |
| Week 6 | Feature Engineering + Data-Centric ML | Improve an existing model through feature engineering and error analysis |
| Week 7 | Model Evaluation & Retraining | Design evaluation metrics and create a retraining strategy |
| Week 8 | Model Deployment Fundamentals | Deploy a trained model as an API service |
| Week 9 | MLOps Fundamentals + Model Versioning | Track experiments and version multiple model iterations |
| Week 10 | Production ML System Design + Trade-offs | Design an end-to-end ML architecture for a business use case |
| Week 11 | Capstone Project Sprint | Build the selected end-to-end production-grade ML solution |
| Week 12 | Final Project Demo + Scalability + Future Extensions | Finalize project, architecture review, and roadmap |
A fast-paced refresher of machine learning fundamentals, then straight into deep learning with PyTorch. You'll build and evaluate a baseline ML model and implement a feedforward neural network from scratch.
Train an image classifier using convolutional neural networks (CNNs) on a real dataset.
Build a text classification or sentiment analysis model and understand how modern NLP systems work.
Fine-tune pretrained vision and NLP models — the technique behind most production AI today.
Improve model performance through systematic feature engineering and error analysis.
Design evaluation metrics that matter to the business and create retraining strategies.
Deploy a trained model as an API service — turning a notebook into software.
Track experiments and version multiple model iterations using industry MLOps workflows.
Design an end-to-end ML architecture for a business use case, weighing accuracy, latency, cost, and scalability.
Build your selected end-to-end, production-grade ML solution.
Present your capstone, complete an architecture review, and map your roadmap as an ML engineer.
Lead AI & ML · Ascentic · 7+ years experience
Gihan leads AI and machine learning at Ascentic, with 7+ years building and shipping production ML systems. He brings together industry delivery, university teaching, and research supervision — so you learn how models are trained, evaluated, deployed, and maintained in real environments.
Course fee: LKR 64,000 (33% off the standard LKR 96,000 fee). Flexible payment plans available at registration.
Next batch starts: 12 May 2026
Basic ML courses in Sri Lanka teach you to train models in notebooks. This advanced bootcamp teaches you to build complete production systems: deployment, MLOps, retraining pipelines, monitoring, and architecture design — the skills required for actual ML engineering roles.
Yes. You should be comfortable with Python and have trained at least one ML model using a library like scikit-learn, TensorFlow, or PyTorch. This course is not intended for absolute beginners.
Yes. All sessions are live online from Sri Lanka. Each week includes a 3-hour live lecture and hands-on lab plus a 1-hour consultation session.
The program runs for 3 months (12 weeks) with 36 hours of live training, weekly consultations, and around 7 hours of take-home assignments per week — including three end-to-end projects and a capstone.
The course fee is LKR 64,000 (33% off the standard LKR 96,000 fee). Flexible payment options are available at registration.
Graduates are prepared for roles such as Machine Learning Engineer, AI Engineer, and Data Scientist — in Sri Lankan tech companies or remote international roles. The curriculum is specifically designed around what these roles require in production environments.
Yes. Graduates who complete the program receive a CodeWave Academy Certificate of Completion, along with three production-grade capstone projects for your portfolio.
Yes. The program is structured for working professionals — one live evening/weekend session per week plus take-home work, with a dedicated weekly consultation slot.