Modul 1 — Fundamental Predictive AI

Membahas konsep dasar dan penerapan Predictive AI dalam berbagai konteks bisnis dan teknologi, termasuk pendekatan pembelajaran AI, training model, neural network, serta fungsi utama AI seperti computer vision, NLP, speech recognition, dan pattern recognition.

Pokok Bahasan:

• Pendorong bisnis dan teknologi AI Prediktif
• Manfaat AI Prediktif
• Risiko dan tantangan umum penggunaan AI Prediktif
• Kategori masalah bisnis yang ditangani AI
• Jenis-jenis AI Prediktif
• Pendekatan pembelajaran AI Prediktif yang umum
• Pembelajaran AI Prediktif dan pelatihan model
• Proses training loop langkah demi langkah
• Supervised learning, unsupervised learning, dan continuous learning
• Heuristic learning, semi-supervised learning, dan reinforcement learning
• Desain fungsional AI Prediktif: computer vision dan pattern recognition
• Robotics dan Natural Language Processing (NLP)
• Speech recognition dan Natural Language Understanding (NLU)
• Pemahaman model AI dan neural network

Modul 2 — Microservice Technology Concepts

Membahas konsep dan teknologi dasar implementasi microservices, termasuk komunikasi layanan, pertukaran data, REST services, web services, serta konsep cloud computing dalam arsitektur layanan modern.

Pokok Bahasan:

• Perbandingan medium implementasi layanan
• Peran service roles dan service agents
• Message exchange patterns dan service activities
• Konsep dasar XML, XML Schema, JSON, dan JSON Schema
• HTTP methods, response codes, dan headers
• Konsep dasar REST service termasuk properti dan constraint
• REST services, contracts, resources, dan messaging
• Hypermedia dan late binding
• Konsep dasar WSDL dan SOAP
• WS-* technologies
• Web service contracts, messaging, dan registries
• Konsep cloud computing
• Vertical dan horizontal scaling
• Multitenancy, elasticity, dan resiliency
• On-demand usage, ubiquitous access, dan measured usage
• Public, private, dan hybrid clouds
• Model layanan IaaS, PaaS, dan SaaS

Modul 7 — Fundamental Predictive AI Engineering

Membahas teknik dasar rekayasa Predictive AI, termasuk preprocessing data, evaluasi model, optimasi performa, deployment, monitoring, serta arsitektur neural network untuk sistem AI prediktif.

Pokok Bahasan:

• Predictive AI model selection dan hyperparameter tuning
• Predictive AI model deployment, monitoring, dan maintenance
• Predictive AI bias detection dan mitigation
• Predictive AI model explainability dan interpretability
• Predictive AI model evaluation dan validation techniques
• Data preprocessing techniques, overfitting, dan regularization
• Performance optimization techniques for Predictive AI models
• Understanding predictive neural networks and models
• Neural network types, neurons, layers, links, dan weights
• Loss, hyperparameters, learning rate, bias, dan epoch
• Activation functions (Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax, Softplus)
• Neuron cell types (input, hidden, recurrent, convolution, pool, output, dll.)
• Common neural network architectures for Predictive AI systems

Modul 10 — Fundamental Generative AI Engineering

Membahas praktik dasar rekayasa Generative AI, termasuk pengolahan data, prompt engineering, evaluasi model, optimasi performa, serta arsitektur neural network untuk sistem AI generatif.

Pokok Bahasan:

• Representasi data dan encoding
• Latent space dan manipulasi latent space
• Prompt engineering
• Metrik evaluasi model generatif
• Pemilihan model Generative AI dan hyperparameter tuning
• Deployment, monitoring, dan maintenance model Generative AI
• Deteksi dan mitigasi bias pada Generative AI
• Explainability dan interpretability model Generative AI
• Teknik evaluasi dan validasi model Generative AI
• Teknik preprocessing data, overfitting, dan regularization
• Teknik optimasi performa model Generative AI
• Memahami neural network generatif dan model-modelnya
• Tipe neural network, neuron, layer, link, dan weight
• Loss, hyperparameter, learning rate, bias, dan epoch
• Fungsi aktivasi (Leaky ReLU, Tanh, ReLU, Softmax, Sigmoid, Softplus)
• Tipe sel neuron (input, hidden, recurrent, convolution, pool, output, dll.)
• Arsitektur neural network umum untuk sistem Generative AI

Modul 13 — Fundamental AI Architecture & Design

Membahas konsep dasar arsitektur sistem AI dan pendekatan desain solusi AI modern, termasuk mode operasional, komponen inti AI pipeline, data processing, inference, serta monitoring sistem AI.

Pokok Bahasan:

• Perbandingan AI product architecture dan custom AI architecture
• Ruang lingkup arsitektur AI (system dan solution)
• Mode operasional solusi AI (training dan production)
• Tipe arsitektur sistem AI (monolithic, modular, hybrid)
• Penyimpanan data solusi AI (internal, eksternal, hybrid)
• Komponen inti sistem AI
• Data ingestion untuk Predictive AI dan Generative AI
• Data preprocessing dalam sistem AI
• Feature engineering dalam sistem AI
• Inference engine dalam sistem AI
• Model repository dalam sistem AI
• Monitoring operasi (performance dan resource)
• Monitoring data (input dan output)
• Monitoring model (weight, gradient, activation distribution, bias, fairness)
• Monitoring tambahan (explainability, robustness, adversarial attack, kualitas data, dan labeling data)