2026-10: Advanced AI for Professionals (Webinar 5)
Section outline
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Advanced AI for Professionals
A hands-on session on deep learning, large language models, and the engineering practices that carry AI systems from training to production.
Learning Goals:
- Build and train neural networks in PyTorch, and apply data augmentation to improve how well a model generalizes
- Explain how convolutional and transformer architectures are applied to computer vision, and when transfer learning is the right choice
- Understand how large language models are built, adapted, and applied
- Weigh quantization, pruning, and distillation against latency, cost, and hardware constraints, and package a model for reliable serving
- Design a data pipeline from ingestion through feature serving, with the validation, lineage, and drift monitoring that keep it trustworthy
- Plan how an AI system is operated and evolved after deployment, using MLOps principles and maturity levels as a guide
Participants may complete the materials at their own pace. To access the workshop materials, please complete your enrollment through the link below:
→ Register/Enrol in this Course
Participants may also explore and enroll in the previous installments of the DOST-NAIRA AIgnite Academy National AI Capacity Building Program, including courses on AI fundamentals, AI project design, AI application development, and applied AI for professionals:
→ View Previous and Available Courses
Have questions or need help with the workshop materials?
→ Post your questions in the Q&A Forum and Consultation. Participants may raise questions and discuss the course content with the resource speakers and facilitators.
To receive your Certificate of Participation, please complete the Pre-Test, Post-Test, and Training/Workshop Evaluation Form.
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Before beginning the workshop sessions, please take the Pre-Test to gauge your current understanding of the topics to be covered and establish a baseline for measuring your learning progress.
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Deep Learning I
Deep learning fundamentals — how neural networks are built and trained, from layers and activation functions through forward propagation and backpropagation. Covers the major architectures (MLP, CNN, RNN, LSTM, GAN, GNN), the PyTorch and TensorFlow landscape, data augmentation, and a hands-on PyTorch implementation.
→ Slide Deck: Deep Learning I
Notebook link
→ https://github.com/SnowArchitect60/deep-learning-i
Some useful links
→ https://softwaremill.com/ml-engineer-comparison-of-pytorch-tensorflow-jax-and-flax/
→ https://arxiv.org/pdf/2508.04035
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Deep Learning II
Computer vision with convolutional networks and transformers — convolutions, stride, padding, and pooling, then Vision Transformers and the Swin Transformer’s shifted-window attention. Closes with transfer learning and an overview of emerging 3D computer vision for robotics and autonomous vehicles.
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Large Language Models
How large language models are built, adapted, and put to work — and what they can and can’t be trusted to do.
Session materials are being finalized and will be posted here soon.
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Model Optimization & Deployment
Taking a trained model from research checkpoint to production — quantization, pruning, distillation, and graph-level optimization, then packaging, serving, and the monitoring that keeps it working once it’s live. Treats the pipeline as a continuous loop, not a one-time handoff.
→ Slide Deck: Model Optimization & Deployment
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Data Pipelines for AI
The full path data takes to become a working AI system — ingestion, storage, processing, feature stores, and serving — plus the validation, lineage, and drift monitoring that keep it honest once it’s running.
→ Slide Deck: Data Pipelines for AI
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MLOps & AI Systems Engineering
Why benchmark accuracy doesn’t predict production success — AI systems engineering from framing through evolution, then MLOps proper: how it differs from DevOps, its governing principles, and the three maturity levels.
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Participants may submit their questions through the Q&A Forum and Consultation while completing the asynchronous workshop materials, providing an opportunity for clarification, discussion, and direct interaction with the resource speakers.
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After completing the workshop sessions, please take the Post-Test to assess your understanding of the topics covered and measure your learning progress.
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Please complete the Training/Workshop Evaluation Form to share your feedback on the sessions, materials, and overall learning experience. Your responses will help improve future training activities.
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To receive your Certificate of Completion, complete the Pre-Test, Post-Test, and Training/Workshop Evaluation Form.
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Before you go: a short survey (optional)
Across these sessions you saw what a working AI system actually needs: GPUs to train on, datasets and model versions that someone has documented and kept, a registry that can say which version is running and what produced it, and somewhere to serve the model once it leaves the notebook. Each session covered a different piece of that stack.
We are studying whether a national AI-as-a-Service platform could bring these together for researchers, developers and students in the Philippines. As proposed, it would offer a searchable repository of documented datasets and models, with hosted inference and controlled fine-tuning planned. It is a proposal, not yet an available service: capacity, support and pricing are not decided.
If you would like to take part, the survey is at study.dimer.dev or aiaas-market-study-st8x.vercel.app.
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