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Task 2.1: Choose a Modeling Approach – MLA-C01 Study Notes

Overview

Selecting the right modeling approach involves matching business problems to algorithms, foundation models, or pre-built AI services based on data characteristics, complexity, interpretability needs, cost, and feasibility. Assess data availability/quality and problem type (classification, regression, clustering, forecasting, generation) first. Prefer managed AI services for common tasks; use SageMaker built-ins/JumpStart for custom needs; build custom only when necessary.

ML Stack Layers (bottom to top):

  • Infrastructure + frameworks (EC2, containers, TensorFlow/PyTorch)
  • ML services (SageMaker for build/train/deploy)
  • AI services (pre-trained APIs: Rekognition, Comprehend, Bedrock, etc.)
graph TD
    A[Business Problem + Data] --> B{Feasibility?}
    B -->|Simple/Common| C[AWS AI Services]
    B -->|Custom/Trainable| D[SageMaker Built-in / JumpStart / Bedrock FMs]
    B -->|Complex/Novel| E[Custom Algo + Frameworks]
    C --> F[Cost / Interpretability Check]
    D --> F
    E --> F
    F --> G[Deploy]

Core ML Algorithms & Use Cases

Know supervised vs unsupervised, and classic algorithms. Match to problem type and data.

Algorithm Type Best For Key Traits / Exam Notes
Linear Regression Supervised Continuous prediction (price, demand) Interpretable (weights); assumes linear relationships
Logistic / Multinomial Logistic Regression Supervised Binary / multi-class classification Probabilities via sigmoid; good baseline
Decision Trees / Random Forests Supervised Classification & regression; tabular High interpretability (rules); RF reduces overfitting via ensemble
SVM Supervised Classification (clear margins) Effective in high dimensions; kernel trick for non-linear
K-Nearest Neighbors (KNN) Supervised Classification / regression Instance-based; sensitive to scale & k
K-Means Unsupervised Clustering Needs k; distance-based; initialize carefully
PCA Unsupervised Dimensionality reduction Captures variance; preprocessing step
Latent Dirichlet Allocation (LDA) Unsupervised Topic modeling (text) Probabilistic topics
Word2Vec Unsupervised / Embedding Word embeddings / semantic similarity Captures context
Seq2Seq Supervised (seq) Translation, summarization Encoder-decoder; often with attention

Gradient Descent: Core optimizer (batch/mini-batch/stochastic). Minimizes loss by updating weights.

Exam Trap: Do not pick complex models (deep nets) when simple linear/logistic or trees suffice and interpretability is required.

Deep Learning Fundamentals

  • Artificial Neuron: Inputs × weights + bias → activation function → output.
  • Activation Functions: ReLU (common, avoids vanishing), Sigmoid/Tanh (output layers or gates), Softmax (multi-class).
  • Weights & Bias: Learned parameters; initialized carefully to aid convergence.
  • CNNs: Convolutional + pooling layers. Primary use = image classification / object detection. Hierarchical feature extraction.
  • RNNs: Maintain hidden state for sequences. Use cases = translation, sentiment, audio, time-series (order matters).

Vanishing Gradient Problem (long sequences): Gradients shrink → early layers stop learning.

  • Solution 1 – LSTM: Gates (input/forget/output) control memory. Multiple internal networks → powerful but slower/more compute to train.
  • Solution 2 – GRU: Simplified gates; fewer parameters → faster training, often comparable performance.

Exam Tip: LSTM when accuracy on long dependencies is critical and training time is available; GRU when speed/cost matters. Both are RNN variants.

Model Interpretability

Higher interpretability = easier to explain predictions, but often trades off with raw performance (complex models capture non-linearities better).

  • Intrinsic (inherently interpretable): Low-complexity models.
  • Linear/Logistic Regression (coefficients = importance)
  • Decision Trees (if-then rules)
  • Post-hoc (after training, often model-agnostic):
  • Local (single prediction) vs Global (overall behavior)
  • Examples: SHAP (Shapley values), feature importance, partial dependence
  • Works on neural nets and black-box models

SageMaker Clarify: Implements scalable SHAP for bias detection and prediction explanations. Use during selection/evaluation for compliance or trust needs.

Trade-off Decision: Regulated domains (finance/healthcare) → favor intrinsic or Clarify-augmented models. Pure performance → deep models + post-hoc.

AWS AI Services (Pre-built, API-driven)

Use these for rapid solutions to common problems—no training required (except Forecast/Personalize/Fraud Detector which accept your data). Fully managed, pay-per-use.

Service Core Function Key Use Cases Notes / Differentiator
Amazon Lex Chatbots / Conversational Call-center bots, FAQs, Alexa-like NLU + dialog
Amazon Polly Text-to-Speech Audio generation, accessibility SSML (shout/whisper); lexicons for custom pronunciation of acronyms/terms
Amazon Transcribe Speech-to-Text (ASR) Call/meeting transcription, subtitles Reverse of Polly
Amazon Translate Neural Machine Translation Mass document/website translation Real-time or batch
Amazon Textract OCR + Document Understanding Forms, handwriting, tables extraction Feeds NLP pipelines
Amazon Comprehend NLP Sentiment, entities, key phrases, PII, topics; Comprehend Medical Ideal for social media crawlers + OpenSearch indexing
Amazon Rekognition Image/Video Analysis Object/scene detection, faces, celebs, custom labels Custom Labels + Ground Truth for your objects
Amazon Forecast Time-series Forecasting Demand, inventory, traffic Bring historical data; uses RNNs etc.
Amazon Personalize Recommendations “Customers also bought”, personalized rankings Real-time personalization
Amazon Fraud Detector Fraud Detection Payments, fake accounts Managed ML models
Amazon Bedrock Generative AI Text/image generation, chat, summarization Choose FMs from multiple providers; customize (fine-tune/RAG); fully managed

Comparisons:

  • Polly (TTS) ↔ Transcribe (STT)
  • Textract (extract structure) vs Comprehend (understand meaning/sentiment)
  • Rekognition (vision) vs custom CV in SageMaker
  • Bedrock (FMs + tools) vs training your own LLM (expensive)

Exam Trap: Polly mispronounces terms → use pronunciation lexicons, not a different service. Sentiment on social posts → Comprehend (not Translate or plain Textract).

SageMaker for Custom Modeling

Fully managed platform to build, train, tune, deploy. Uses Docker containers for code, dependencies, algorithms, and environments (not primarily for horizontal scaling—that is handled by the service). Pre-built AWS containers exist for popular frameworks and algorithms; you can bring custom images. SageMaker orchestrates lifecycle on managed infra.

Key Capabilities for Model Selection:

  • Built-in Algorithms: Linear Learner, XGBoost, Image Classification, Object Detection, Seq2Seq, BlazingText (Word2Vec), LDA, K-Means, PCA, DeepAR (forecasting), etc. Choose based on data type and problem.
  • SageMaker JumpStart: Pre-trained models, solution templates, and one-click examples (vision, NLP, tabular). Fastest path for many problems; includes foundation-model style assets.
  • Hyperparameter Tuning: Automatic multi-job search.
  • SageMaker Neo: Compile once, optimize for cloud or edge targets.
  • Ground Truth: Data labeling (integrates with Rekognition Custom Labels).
  • Augmented AI (A2I): Human review loops for low-confidence predictions (Rekognition/Textract/custom).
  • Cost selection: Prefer built-ins/JumpStart/Spot training over from-scratch; match instance types to algorithm (GPU for deep learning).

Bedrock vs SageMaker JumpStart: Bedrock = pure FM API + customization for genAI apps. JumpStart = broader pre-trained + classical ML templates inside SageMaker ecosystem.

Decision Framework & Skills Application

  1. Assess Data & Complexity: Enough labeled data? Tabular/image/text/time-series? Feasible with ML?
  2. Match Problem:
  3. Common (sentiment, OCR, translate, detect objects) → AI service
  4. Need customization / your data → SageMaker built-in, JumpStart, or Bedrock FM
  5. Full control / novel → Custom container + framework
  6. Constraints: Interpretability (Clarify/trees) | Cost (built-in > custom; serverless inference) | Latency/edge (Neo)
  7. Select & Validate: Compare metrics + business KPIs; check bias.

Exam Tips & Traps

  • Memorize pairings: CNN=images, RNN/LSTM/GRU=sequences, trees=interpretability, K-Means=clustering, PCA=reduction.
  • Vanishing gradient → LSTM (accurate) or GRU (faster). Never “just add layers”.
  • Interpretability question → intrinsic for simple, post-hoc/SHAP/Clarify for complex.
  • AI service scenario (chat, speech, fraud, recommend, forecast, genAI) → pick exact match; watch for lexicon (Polly), Custom Labels (Rekognition), medical (Comprehend Medical).
  • SageMaker containers = packaging environment/code, not the scaling mechanism.
  • Cost-aware: JumpStart/built-in/Bedrock usually cheaper & faster than training large models from scratch.
  • Always consider feasibility first—insufficient data → no ML or use pre-trained/transfer.
  • Trap: Choosing deep learning when logistic regression or XGBoost solves it with better explainability and lower cost.

Quick Win Pattern: Business need → AI service if exists → else JumpStart/built-in → else custom. Layer Clarify for trust. Total focus: match tool to problem constraints.