DeployAI 2026 is planned as an intensive 14-day winter school combining expert lectures, guided tutorials, hands-on laboratories, technical discussions, mentor interaction, case studies, project development, and final demonstrations.
The programme schedule will provide participants with a structured progression from foundational artificial intelligence concepts to the deployment and operation of complete AI systems.
Programme Structure
The programme is expected to include the following types of activities:
1. Expert lectures introducing concepts, methods, tools, and current developments
2. Guided tutorials explaining implementation workflows and best practices
3. Hands-on laboratory sessions involving coding, experimentation, and deployment
4. Academic and industry mentor interactions
5. Case studies based on real-world AI applications
6. Group discussions and problem-solving sessions
7. Project planning and implementation
8. Hackathon or capstone activities
9. Technical demonstrations and presentations
10. Feedback and evaluation sessions
Indicative Daily Format
A typical programme day may include:
Morning Session
Registration or attendance
Expert lecture
Conceptual discussion
Question-and-answer session
Midday Session
Technical tutorial
Demonstration of tools, models, or deployment workflows
Discussion of practical examples
Afternoon Session
Hands-on laboratory
Coding and implementation exercise
Group work or mentor-supported problem solving
Evening or Extended Session
Project development
Hackathon activity
Mentor interaction
Revision, debugging, or preparation for demonstration
The actual timing may vary depending on the topic, invited speaker availability, laboratory requirements, project activities, and institutional arrangements.
Indicative 14-Day Progression
Day 1
AI landscape, AI engineering, deployable AI ecosystem, participant orientation, technical setup, and introduction to the programme.
Day 2
Python for AI, data preparation, data processing, visualisation, and reproducible development environments.
Day 3
Machine learning foundations, supervised learning, training workflows, and evaluation metrics.
Day 4
Feature engineering, model selection, validation, error analysis, and machine learning pipelines.
Day 5
Deep learning foundations, neural networks, optimisation, and model training using modern frameworks.
Day 6
Computer vision, image representation, classification, visual recognition, and deployment-oriented workflows.
Day 7
Natural language processing, text representation, classification, sequence modelling, and practical language applications.
Day 8
Foundation models, generative AI, pretrained models, adaptation methods, and responsible use.
Day 9
Large language models, prompt engineering, application design, evaluation, and safety considerations.
Day 10
Retrieval-Augmented Generation, embeddings, vector databases, document processing, and knowledge-grounded applications.
Day 11
MLOps, experiment tracking, dataset and model versioning, reproducibility, testing, and automation.
Day 12
Model serving, API development, inference pipelines, latency, scalability, and production integration.
Day 13
Docker, cloud deployment, monitoring, security, responsible AI, and maintenance of deployed systems.
Day 14
Capstone project, hackathon completion, end-to-end deployment, technical demonstration, evaluation, feedback, and closing session.
Final Schedule Publication
The final schedule will include:
1. Session date
2. Start and end time
3. Session title
4. Session type
5. Speaker or mentor
6. Venue or laboratory
7. Session description
8. Participant requirements
9. Required software or preparation
10. Schedule updates
The detailed session-wise schedule will be added through the Schedule management section of the microsite admin panel.
Schedule Changes
The organising committee may revise session timings, speakers, venues, laboratories, or activity order when necessary.
All important updates will be communicated through the event website, registered email address, or official event communication channels.
Participants should check the latest schedule before each programme day and follow the instructions issued by the organising team.
Attendance
Participants are expected to attend all required lectures, laboratories, project sessions, mentor interactions, and demonstrations.
Minimum attendance, assignment completion, project participation, or evaluation requirements may apply for certificate eligibility.
Technical Preparation
Participants should bring their laptop, charger, and required accessories to all practical sessions.
Software installation, datasets, account creation, development environments, and other technical preparation instructions may be shared before the programme.
Participants should complete the required setup within the communicated deadline to avoid delays during laboratory sessions.