AI Architect Guide · Innovation & Leadership

📐 ML Algorithms — Basics to Advanced

BASIC Supervised Learning

Linear Regression

Predicts continuous output using a line: y = mx + b. Loss = MSE.

Logistic Regression

Binary classification via sigmoid: σ(z) = 1/(1+e⁻ᶻ).

Decision Trees

Splits data by feature thresholds. Prone to overfit without pruning.

ADVANCED Ensemble Methods

XGBoost

Gradient boosting with regularization (L1/L2), tree pruning, column subsampling.

LightGBM

Histogram-based splits, leaf-wise growth — faster than XGBoost on large data.

Random Forest

Bagging of decision trees; reduces variance via averaging.

ADVANCED Neural Networks

CNN

Convolutional layers extract spatial features. Used in image/time-series tasks.

RNN/LSTM

Sequential memory via gates: input, forget, output. Handles time-series.

Transformer

Attention-based parallelism. Backbone of modern LLMs.

💻 ML Code — Simple & Memorable

🔹 XGBoost Classifier

pythonfrom xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train model
model = XGBClassifier(n_estimators=100, max_depth=5,
                        learning_rate=0.1, use_label_encoder=False)
model.fit(X_train, y_train)

# Evaluate
preds = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, preds):.2%}")

🔹 Neural Net with PyTorch

pythonimport torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(in_dim, 128),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(128, out_dim)
        )
    def forward(self, x):
        return self.net(x)

model = SimpleNet(20, 2)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
loss_fn = nn.CrossEntropyLoss()

📊 Feature Engineering & EDA

BASIC EDA Steps

Shape / dtypes / .describe()
Missing value analysis: df.isnull().sum()
Distribution plots: histplot, boxplot
Correlation heatmap (seaborn)
Outlier detection: IQR / Z-score

🔹 Feature Engineering Pipeline

pythonfrom sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer

num_pipe = Pipeline([
    ('impute', SimpleImputer(strategy='median')),
    ('scale',  StandardScaler())
])
cat_pipe = Pipeline([
    ('impute', SimpleImputer(strategy='most_frequent')),
    ('encode', OneHotEncoder(handle_unknown='ignore'))
])
preprocessor = ColumnTransformer([
    ('num', num_pipe, num_cols),
    ('cat', cat_pipe, cat_cols)
])

🔬 Developer-Level AIML Interview Questions

DEV Bias-Variance Tradeoff

High bias → underfitting (model too simple). High variance → overfitting (model memorizes). Solution: regularization (L1/L2), dropout, more data, cross-validation.

DEV Imbalanced Classes

Techniques: SMOTE oversampling, class_weight='balanced', use PR-AUC/F1 instead of accuracy, threshold tuning, ensemble methods.

DEV Gradient Descent Variants

BGD: full batch, stable but slow. SGD: noisy but fast. Mini-batch: best of both. Adam: adaptive LR — default choice for deep learning.

DEV Hyperparameter Tuning

GridSearchCV (exhaustive), RandomizedSearchCV (fast), Optuna (Bayesian optimization), Ray Tune (distributed). Always use k-fold CV to evaluate.

🎬 Real-Time Scenarios

Scenario 1: Fraud Detection System

You have 1M transactions, 0.1% are fraud. Your XGBoost model has 99% accuracy but catches only 20% of fraud cases. What do you do?

▶ Show Answer

The accuracy is misleading (imbalanced classes). Actions: (1) Switch metric to Recall/F1/AUC-PR. (2) Apply SMOTE or adjust scale_pos_weight in XGBoost. (3) Lower the decision threshold from 0.5 to 0.2. (4) Use cost-sensitive learning. (5) Try ensemble with isolation forest for anomaly detection layer.

Scenario 2: Model Degradation in Production

Your credit scoring model was 92% accurate at launch. After 6 months accuracy dropped to 78%. No code changed. Why?

▶ Show Answer

Data drift / concept drift. The distribution of input features has changed (economic conditions, user behavior shifts). Fix: (1) Monitor feature distributions with KL divergence. (2) Set up automated retraining triggers. (3) Use MLflow to version models. (4) Implement shadow deployment to test new model before swap. (5) Add feature drift alerts via Evidently AI.

🧠 LLM Fundamentals

BASIC Transformer Architecture

Self-Attention

Computes relevance of each token to every other token via Q, K, V matrices.

Positional Encoding

Sine/cosine waves injected to give tokens position awareness.

Feed-Forward

Two linear layers with non-linearity applied per token independently.

ADVANCED LLM Variants

Frontier closed model

Multimodal, 128k context, RLHF + RLAIF trained.

BERT

Encoder-only. Masked LM pretraining. Best for NLU tasks.

Open-weight model

Open-weight. Group Query Attention, RoPE embeddings, SwiGLU.

Long-context model

1M token context, mixture-of-experts, native multimodal.

ADVANCED Fine-Tuning Techniques

Full Fine-Tuning

All weights updated. Expensive, needs lots of GPU RAM.

LoRA

Low-Rank Adaptation: adds small trainable matrices A, B. ΔW = BA.

QLoRA

LoRA on 4-bit quantized model. Fits 70B on single A100.

RLHF

Reward model from human preferences → PPO policy update.

🛠️ Prompt Engineering

Techniques

Zero-shot No examples, direct instruction
Few-shot 2-5 input→output examples
CoT "Let's think step by step"
ReAct Reason + Act in alternation
Tree of Thought Explore multiple paths
Self-Consistency Sample N paths, majority vote

🔹 LLM API with structured output

pythonfrom openai import OpenAI
import json

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    response_format={"type": "json_object"},
    messages=[
        {"role": "system",
         "content": "Extract entities. Return JSON."},
        {"role": "user",
         "content": "Apple Inc. was founded by Steve Jobs in 1976."}
    ]
)
result = json.loads(response.choices[0].message.content)
print(result)

🔗 LLM Orchestration / Gen AI Frameworks

🔹 Orchestration chain (Python)

pythonfrom langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(model="gpt-4o-mini")

prompt = ChatPromptTemplate.from_template(
    "Summarize this in 3 bullets:\n{text}"
)

# LCEL pipe syntax
chain = prompt | llm | StrOutputParser()

result = chain.invoke({"text": "Your document here..."})
print(result)

🔹 LangGraph Agent Loop

pythonfrom langgraph.graph import StateGraph, END
from typing import TypedDict, List

class AgentState(TypedDict):
    messages: List[str]
    next_action: str

def call_llm(state):
    # Call LLM, return updated state
    return {"next_action": "tool" if needs_tool else "end"}

def call_tool(state):
    # Execute tool, append result
    return state

graph = StateGraph(AgentState)
graph.add_node("llm", call_llm)
graph.add_node("tool", call_tool)
graph.add_conditional_edges("llm", lambda s: s["next_action"])
app = graph.compile()

🎬 Gen AI Scenarios

Scenario: LLM Hallucinating in Customer Support Bot

Your LLM-powered support bot is confidently giving wrong answers about product pricing and policies. It's in production. How do you fix this?

▶ Show Answer

1. Implement RAG: pull real-time pricing/policy from your DB/Confluence before generating answer. 2. Add a system prompt: "Only answer from the provided context. Say 'I don't know' if not in context." 3. Add guardrails with NeMo Guardrails or LlamaGuard for output validation. 4. Implement confidence scoring — route low-confidence answers to human agents. 5. Log all responses, implement human-in-the-loop for high-stakes decisions. 6. Fine-tune on verified QA pairs for domain alignment.

🔄 MLOps Lifecycle

— MLOps Pipeline —

Data Ingest

→

Feature Store
(Feast/Tecton)

→

Model Training
(managed ML platform)

→

Experiment Tracking
(MLflow/W&B)

Model Registry

→

CI/CD
(GitHub Actions)

→

Serving
(FastAPI/Triton)

→

Monitoring
(Evidently/Grafana)

📦 MLflow — Model Versioning

pythonimport mlflow
import mlflow.sklearn

mlflow.set_experiment("fraud-detection-v2")

with mlflow.start_run():
    # Log params
    mlflow.log_param("n_estimators", 100)
    mlflow.log_param("max_depth", 5)

    # Train
    model.fit(X_train, y_train)

    # Log metrics
    mlflow.log_metric("f1", f1_score(y_test, preds))
    mlflow.log_metric("auc", roc_auc_score(y_test, proba))

    # Log model
    mlflow.sklearn.log_model(model, "model")
    run_id = mlflow.active_run().info.run_id

python# Register model to registry
result = mlflow.register_model(
    f"runs:/{run_id}/model",
    "FraudDetectionModel"
)

# Transition to production
client = mlflow.MlflowClient()
client.transition_model_version_stage(
    name="FraudDetectionModel",
    version=result.version,
    stage="Production"
)

# Load in serving
model = mlflow.pyfunc.load_model(
    "models:/FraudDetectionModel/Production"
)

🐳 Docker + Kubernetes for ML

🔹 Dockerfile for ML API

dockerfileFROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

🔹 FastAPI Model Endpoint

pythonfrom fastapi import FastAPI
from pydantic import BaseModel
import mlflow

app = FastAPI()
model = mlflow.pyfunc.load_model("models:/FraudModel/Production")

class Transaction(BaseModel):
    amount: float
    merchant_category: str
    hour_of_day: int

@app.post("/predict")
def predict(tx: Transaction):
    df = pd.DataFrame([tx.dict()])
    proba = model.predict_proba(df)[0][1]
    return {"fraud_prob": proba, "flag": proba > 0.5}

🔹 K8s Deployment YAML

yamlapiVersion: apps/v1
kind: Deployment
metadata:
  name: fraud-model-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: fraud-api
  template:
    metadata:
      labels: {app: fraud-api}
    spec:
      containers:
      - name: api
        image: myrepo/fraud-api:1.2
        ports:
        - containerPort: 8000
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "500m"
---
apiVersion: v1
kind: Service
metadata:
  name: fraud-api-svc
spec:
  type: LoadBalancer
  ports:
  - port: 80
    targetPort: 8000

🔍 RAG Pipeline Architecture

— RAG Flow —

Documents
(PDF/Web/DB)

→

Chunking
(RecursiveTextSplitter)

→

Embedding
(text-embedding-3)

→

Vector DB
(managed vector DB)

User Query

→

Embed Query

→

Similarity Search
(Top-K chunks)

→

LLM + Context
→ Answer

💻 RAG Implementation

🔹 Ingestion Pipeline

pythonfrom langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_pinecone import PineconeVectorStore

# Load & split
loader = PyPDFLoader("policy.pdf")
docs = loader.load()

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000, chunk_overlap=200
)
chunks = splitter.split_documents(docs)

# Embed and store
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vectorstore = PineconeVectorStore.from_documents(
    chunks, embeddings, index_name="my-index"
)

🔹 RAG Query Chain

pythonfrom langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o", temperature=0)
retriever = vectorstore.as_retriever(
    search_type="mmr",  # max marginal relevance
    search_kwargs={"k": 5, "fetch_k": 20}
)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    return_source_documents=True
)

result = qa_chain.invoke("What is our refund policy?")
print(result["result"])
print([d.metadata["page"] for d in result["source_documents"]])

🤖 AI Agent Frameworks

ADVANCED Agent Architectures

ReAct Agent

Thought → Action → Observation loop. Uses tools and reasons over results.

Plan-and-Execute

Planner LLM creates steps → Executor LLM runs each step.

Multi-Agent

Supervisor routes to specialist agents (coder, researcher, analyst).

🔹 Tool-calling Agent

pythonfrom langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain.tools import tool

@tool
def get_stock_price(ticker: str) -> str:
    """Get current stock price for a ticker symbol."""
    return f"${ticker}: $182.50"  # mock

@tool
def calculate_pe_ratio(price: float, eps: float) -> float:
    """Calculate P/E ratio."""
    return price / eps

tools = [get_stock_price, calculate_pe_ratio]
agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
executor.invoke({"input": "Analyze AAPL stock with P/E ratio"})

🔒 AI Security & Compliance

Prompt Injection

Attacker embeds instructions in user input to override system prompt. Defense: Input sanitization, instruction hierarchy, NeMo Guardrails, separate system/user contexts.

Data Leakage

LLMs may memorize training data. Defense: PII detection before RAG ingestion (Presidio), output filtering, differential privacy in fine-tuning.

OWASP LLM Top 10

Prompt Injection
Insecure Output Handling
Training Data Poisoning
Model Denial of Service
Supply Chain Vulnerabilities

🐍 Python Essentials for Data Science

🔹 Pandas Power Patterns

pythonimport pandas as pd
import numpy as np

# Method chaining — clean & readable
result = (
    df
    .query("amount > 1000 and status == 'completed'")
    .assign(log_amount=lambda x: np.log1p(x["amount"]))
    .groupby("merchant_category")
    .agg(
        total=("amount", "sum"),
        avg=("amount", "mean"),
        count=("amount", "count")
    )
    .sort_values("total", ascending=False)
    .reset_index()
)

🔹 Decorators & Generators

pythonimport time, functools

# Timing decorator
def timeit(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        elapsed = time.perf_counter() - start
        print(f"{func.__name__}: {elapsed:.3f}s")
        return result
    return wrapper

# Memory-efficient generator
def chunk_dataframe(df, size=1000):
    for i in range(0, len(df), size):
        yield df.iloc[i:i+size]

# Async for parallel API calls
import asyncio, aiohttp
async def fetch_all(urls):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch(session, url) for url in urls]
        return await asyncio.gather(*tasks)

❓ Python Interview Q&A

DEV GIL — Global Interpreter Lock

Python's GIL allows only one thread to execute at a time. For CPU-bound tasks, use multiprocessing. For I/O-bound, use asyncio or threading. GIL doesn't affect multi-process ML training.

DEV Memory Optimization

Use generators instead of lists. Downcast dtypes: df.astype({'col': 'float32'}). Use __slots__ in classes. Dask for out-of-memory DataFrames. Polars for fast columnar ops.

DEV Context Managers

from contextlib import contextmanager
@contextmanager
def timer(label):
    t = time.time()
    yield
    print(f"{label}: {time.time()-t:.2f}s")

DEV List vs Generator Performance

List: O(n) memory, instantiated upfront. Generator: O(1) memory, lazy evaluation. For ML pipelines processing millions of records, generators are essential to avoid OOM errors.

☕ Java Core + Spring Boot

🔹 Spring Boot Microservice Skeleton

java// Entity
@Entity @Table(name="transactions")
public class Transaction {
    @Id @GeneratedValue
    private Long id;
    private BigDecimal amount;
    private String status;
    private LocalDateTime createdAt;
}

// Repository
public interface TransactionRepo
    extends JpaRepository<Transaction, Long> {
    List<Transaction> findByStatus(String status);
}

// Service
@Service @Transactional
public class TransactionService {
    @Autowired private TransactionRepo repo;
    public Transaction process(TransactionDto dto) {
        var tx = mapper.toEntity(dto);
        return repo.save(tx);
    }
}

🔹 REST Controller + Validation

java@RestController
@RequestMapping("/api/v1/transactions")
public class TransactionController {

    @Autowired private TransactionService svc;

    @PostMapping
    public ResponseEntity<Transaction>
    create(@Valid @RequestBody TransactionDto dto) {
        return ResponseEntity
            .status(HttpStatus.CREATED)
            .body(svc.process(dto));
    }

    @GetMapping("/{id}")
    public ResponseEntity<Transaction>
    get(@PathVariable Long id) {
        return svc.findById(id)
            .map(ResponseEntity::ok)
            .orElse(ResponseEntity.notFound().build());
    }
}

🌐 Microservices Patterns

ADVANCED Circuit Breaker

Resilience4j: CLOSED → OPEN (after N failures) → HALF_OPEN (probe). Prevents cascade failures between services.

@CircuitBreaker(name="fraud-svc",
    fallbackMethod="fallback")
public Boolean checkFraud(Long txId) {...}

ADVANCED Saga Pattern

Distributed transactions without 2PC. Choreography (events) or Orchestration (central coordinator). Each step publishes success/failure events.

ADVANCED Event Sourcing + CQRS

Store all state changes as immutable events. CQRS: separate read/write models. Enables replay, audit trail, temporal queries.

❓ Java Developer Questions

DEV Java 17+ Stream API

// Parallel stream processing
var stats = transactions.parallelStream()
    .filter(t -> t.getAmount().compareTo(
        BigDecimal.ZERO) > 0)
    .collect(Collectors.groupingBy(
        Transaction::getStatus,
        Collectors.summarizingDouble(
            t -> t.getAmount().doubleValue())
    ));

DEV Spring Security JWT

@Bean
public SecurityFilterChain filterChain(HttpSecurity http)
    throws Exception {
    return http
        .csrf(csrf -> csrf.disable())
        .sessionManagement(s -> s.sessionCreationPolicy(
            SessionCreationPolicy.STATELESS))
        .authorizeHttpRequests(auth -> auth
            .requestMatchers("/auth/**").permitAll()
            .anyRequest().authenticated())
        .addFilterBefore(jwtFilter, ...)
        .build();
}

📊 Data Engineering Stack

BASIC ETL vs ELT

ETL

Extract → Transform (on server) → Load. Traditional, good for sensitive data.

ELT

Extract → Load (raw) → Transform (in DWH). Modern cloud approach with cloud data warehouses.

BASIC Data Formats

Parquet Columnar, great compression, fast analytics
Avro Row-based, schema evolution, streaming
Delta Lake ACID on data lake, time travel
Iceberg Table format for massive tables

ADVANCED Medallion Architecture

Bronze

Raw ingested data. No transformation. Append-only.

Silver

Cleaned, validated, joined. Business rules applied.

Gold

Aggregated, business-ready. Serving layer for BI/ML.

⚡ Apache Spark + Airflow

🔹 PySpark ETL Job

pythonfrom pyspark.sql import SparkSession
from pyspark.sql import functions as F

spark = SparkSession.builder \
    .appName("FraudETL") \
    .config("spark.sql.adaptive.enabled", "true") \
    .getOrCreate()

# Read raw from S3
df = spark.read.parquet("s3://bucket/raw/transactions/")

# Transform
clean = (
    df
    .filter(F.col("amount") > 0)
    .withColumn("date", F.to_date("timestamp"))
    .withColumn("hour", F.hour("timestamp"))
    .dropDuplicates(["tx_id"])
)

# Aggregate (Gold layer)
daily = clean.groupBy("date", "merchant_id") \
    .agg(F.sum("amount").alias("daily_volume"),
         F.count("*").alias("tx_count"))

# Write to Delta Lake (Silver)
clean.write.format("delta") \
    .mode("merge") \
    .partitionBy("date") \
    .save("s3://bucket/silver/transactions/")

🔹 Airflow DAG

pythonfrom airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.providers.amazon.aws.operators.glue import GlueJobOperator
from datetime import datetime, timedelta

default_args = {
    'retries': 2,
    'retry_delay': timedelta(minutes=5),
    'email_on_failure': True
}

with DAG(
    "fraud_etl_pipeline",
    schedule="@daily",
    start_date=datetime(2024, 1, 1),
    default_args=default_args,
    catchup=False
) as dag:

    ingest = GlueJobOperator(
        task_id="ingest_raw", job_name="raw-ingest"
    )
    transform = PythonOperator(
        task_id="transform", python_callable=run_spark_job
    )
    validate = PythonOperator(
        task_id="data_quality", python_callable=run_great_expectations
    )

    ingest >> transform >> validate

🎬 Data Engineering Scenarios

Scenario: Late-Arriving Data Problem

Your daily Spark job calculates revenue per merchant. Occasionally, transactions arrive 2-3 days late. Your reports show wrong numbers. How to fix?

▶ Show Answer

1. Use event_time not processing_time for partitioning. 2. Implement watermarking in streaming: df.withWatermark("event_time", "3 days"). 3. For batch: use Delta Lake's MERGE into instead of overwrite — upsert late arrivals. 4. Reprocess partition when late data arrives using Airflow's backfill + catchup. 5. Add a "reporting cutoff" flag — freeze metrics for reporting after 5 days, note them as estimated before that.

λ Lambda Architecture Deep Dive

— Lambda Architecture (Nathan Marz) —

All Data Sources
(Events/Logs/DB)

BATCH LAYER
Spark / Hadoop

↓

Batch Views
(Recomputed daily)

SPEED LAYER
Kafka + Flink/Spark

↓

Real-time Views
(Last hours only)

SERVING LAYER
Merges Batch + Speed views

Query / Dashboard / API

⚡ Kappa vs Lambda

Lambda Architecture

Two paths: batch + streaming
Complex but handles reprocessing
Batch ensures correctness
Good for: fraud detection, reporting
Tools: Spark + Kafka + HBase

Kappa Architecture

Single streaming path only
Simpler, lower latency
Reprocess by replaying Kafka
Good for: real-time analytics
Tools: Kafka + Flink + Cassandra

🔄 Kafka + Flink Streaming

🔹 Kafka Producer (Python)

pythonfrom kafka import KafkaProducer
import json

producer = KafkaProducer(
    bootstrap_servers=['kafka:9092'],
    value_serializer=lambda v: json.dumps(v).encode()
)

# Publish transaction event
producer.send('transactions', {
    'tx_id': 'T123',
    'amount': 450.00,
    'merchant': 'AMZN',
    'timestamp': '2024-01-15T10:30:00Z'
})
producer.flush()

🔹 Kafka Consumer with windowing

pythonfrom kafka import KafkaConsumer
from collections import defaultdict
from datetime import datetime, timedelta

consumer = KafkaConsumer(
    'transactions',
    bootstrap_servers=['kafka:9092'],
    auto_offset_reset='earliest',
    value_deserializer=lambda x: json.loads(x.decode())
)

# 5-min tumbling window aggregation
window = defaultdict(lambda: {'count': 0, 'total': 0})

for msg in consumer:
    tx = msg.value
    key = tx['merchant']
    window[key]['count'] += 1
    window[key]['total'] += tx['amount']
    # Alert if velocity exceeds threshold
    if window[key]['count'] > 100:
        alert(f"High velocity: {key}")

☁️ Public Cloud Architecture for AI

— Cloud AI/ML Reference Architecture —

Object-store data lake

→

Managed ETL
(jobs)

→

Managed ML
(Train)

→

Managed ML
Endpoint

Managed stream
(Streaming)

→

Serverless fn
(Trigger)

→

Managed LLM
(API)

→

API gateway
+ CDN

🔧 Cloud CLI — Entry Level Commands

Object storage commands

aws s3 cp data.csv s3://bucket/raw/

Upload a file to object storage

aws s3 sync ./local/ s3://bucket/data/

Sync a folder to object storage

aws s3 ls s3://bucket/ --recursive

List all objects

Managed ML CLI

aws sagemaker list-training-jobs

View all training jobs

aws sagemaker describe-endpoint --endpoint-name my-ep

Check endpoint status

aws sagemaker create-model --model-name fraud-v1 ...

💻 Managed LLM API — in Python

pythonimport boto3, json

bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')

# Claude 3 via Bedrock
response = bedrock.invoke_model(
    modelId='anthropic.claude-3-sonnet-20240229-v1:0',
    body=json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 1024,
        "messages": [{"role": "user",
                        "content": "Summarize this financial report..."}]
    })
)
result = json.loads(response['body'].read())
print(result['content'][0]['text'])

# SageMaker endpoint invocation
sm_runtime = boto3.client('sagemaker-runtime')
sm_response = sm_runtime.invoke_endpoint(
    EndpointName='fraud-detection-endpoint',
    ContentType='application/json',
    Body=json.dumps({"features": [450.0, 2, 1, 0]})
)
prediction = json.loads(sm_response['Body'].read())

🎬 Cloud Scenario

Scenario: Design a Real-Time Fraud Detection System on a Public Cloud

You need to score 10,000 transactions/second for fraud with <50ms latency. Model is an XGBoost + LLM explanation. Design the cloud architecture.

▶ Show Answer

1. Managed data streams: ingest 10k TPS. 2. Serverless function (or stream analytics): feature extraction in real-time. 3. Managed ML endpoint with multi-model server + auto-scaling (target tracking on InvocationsPerInstance). 4. ElastiCache Redis: cache merchant profiles, avoid DB round-trips. 5. For LLM explanations: async call to a managed LLM API (non-blocking) — use a message queue to decouple. 6. Key-value store: store fraud flags with TTL. 7. Cloud monitoring + tracing: monitor P99 latency. 8. Use the ML platform's inference recommender to optimize instance type.

💰 BFS / Fintech Domains

BASIC Key BFS Concepts

AML

Anti-Money Laundering. Detect structuring, layering, smurfing patterns in transaction graphs.

KYC

Know Your Customer. Identity verification, document validation, risk scoring at onboarding.

PSD2 / Open Banking

EU regulation mandating banks expose APIs. Enables TPP access to account data.

ADVANCED Fraud Detection Patterns

Card Not Present

Online fraud. Behavioral biometrics + device fingerprinting + velocity checks.

Account Takeover

Stolen credentials. Anomaly detection on login location, device, time.

Synthetic Identity

Fake identities using real SSNs. Graph analysis to detect identity clusters.

ADVANCED Credit Risk Models

PD Model

Probability of Default. Logistic regression with bureau + behavioral features.

LGD

Loss Given Default. Recovery rates by collateral type.

CECL

Current Expected Credit Loss. IFRS 9 / FASB standard for lifetime loss provisioning.

🔗 Graph-Based Fraud Detection

🔹 NetworkX Transaction Graph

pythonimport networkx as nx
import pandas as pd

# Build transaction graph
G = nx.DiGraph()

for _, row in transactions.iterrows():
    G.add_edge(
        row['sender'], row['receiver'],
        weight=row['amount'],
        timestamp=row['ts']
    )

# Detect high centrality nodes (money mules)
betweenness = nx.betweenness_centrality(G, weight='weight')
suspects = {n: s for n, s in betweenness.items() if s > 0.5}

# Detect cycles (round-tripping)
cycles = list(nx.simple_cycles(G))
print(f"Suspicious cycles: {len(cycles)}")

DEV Real-Time Velocity Check

import redis
r = redis.Redis()

def check_velocity(card_id, amount):
    key = f"vel:{card_id}:1h"
    pipe = r.pipeline()
    pipe.incrbyfloat(key, amount)
    pipe.expire(key, 3600)
    total, _ = pipe.execute()

    if float(total) > 10000:
        return {"flag": True,
                "reason": "velocity_breach",
                "total_1h": float(total)}
    return {"flag": False}

🏛️ Project: AI-Powered Fraud Detection Platform

— Production Architecture (public cloud) —

Mobile / Web App

→

API Gateway
+ WAF

→

Spring Boot
Microservices

→

Kafka
Event Bus

Managed stream
(Streams)

→

Serverless fn
(Feature Extract)

→

Managed ML
(ML Scoring)

→

Managed LLM
(Explanation)

Object-store data lake

↔

Lakehouse
Delta tables

↔

Cloud
DWH

↔

MLflow
Registry

Managed
Vector DB

↔

RAG
Pipeline

↔

Grafana
+ Evidently

↔

Kubeflow
Pipelines

🏗️ Project: LLM-Powered Customer Service Platform

— LLM Platform Architecture —

User Query

→

Intent
Classifier

→

Guardrails
(NeMo)

→

LangGraph
Agent

Tool: SQL DB

Tool: Policy RAG

Tool: Calculator

Tool: Escalate

Frontier LLM
(managed API)

→

Output Validator
(Hallucination Check)

→

Response
+ Audit Log

📐 Design Patterns for AI Systems

Human-in-the-Loop

Route low-confidence predictions (<0.7) to human review. Collect labels → continuous retraining. Feedback loop closes the model-data gap.

Shadow Deployment

Run new model in parallel with production. Compare outputs without affecting users. Graduate to A/B then full cutover. Zero risk rollout.

Canary Release

Route 5% traffic to new model → monitor metrics → gradually increase. Rollback instantly if KPIs degrade. Blue-green variant for atomic switch.

Score: 0 / 10

AI ArchitectComplete Reference

BASIC Supervised Learning

ADVANCED Ensemble Methods

ADVANCED Neural Networks

BASIC EDA Steps

DEV Bias-Variance Tradeoff

DEV Imbalanced Classes

DEV Gradient Descent Variants

DEV Hyperparameter Tuning

Scenario 1: Fraud Detection System

Scenario 2: Model Degradation in Production

BASIC Transformer Architecture

ADVANCED LLM Variants

ADVANCED Fine-Tuning Techniques

Techniques

Scenario: LLM Hallucinating in Customer Support Bot

ADVANCED Agent Architectures

Prompt Injection

Data Leakage

OWASP LLM Top 10

DEV GIL — Global Interpreter Lock

DEV Memory Optimization

DEV Context Managers

DEV List vs Generator Performance

ADVANCED Circuit Breaker

ADVANCED Saga Pattern

ADVANCED Event Sourcing + CQRS

DEV Java 17+ Stream API

DEV Spring Security JWT

BASIC ETL vs ELT

BASIC Data Formats

ADVANCED Medallion Architecture

Scenario: Late-Arriving Data Problem

Lambda Architecture

Kappa Architecture

Object storage commands

Managed ML CLI

Scenario: Design a Real-Time Fraud Detection System on a Public Cloud

BASIC Key BFS Concepts

ADVANCED Fraud Detection Patterns

ADVANCED Credit Risk Models

DEV Real-Time Velocity Check

Human-in-the-Loop

Shadow Deployment

Canary Release

AI Architect
Complete Reference