Machine Learning Courses & AI Engineering

Executive Summary: Transitioning from Data Science to AI Engineering

Training a machine learning model in a Jupyter Notebook is only 10% of the effort required to build production AI systems. Modern enterprise applications require robust AI Engineering: point-in-time correct feature stores, parameter-efficient fine-tuning (PEFT), continuous MLOps pipelines, model quantization, and low-latency inference serving engines operating under strict SLAs.

Machine Learning Courses AI Engineering is the authoritative 203-page handbook for machine learning engineers, AI system architects, data scientists, and MLOps specialists. Spanning 8 comprehensive modules, this guide covers the entire modern AI stack: from supervised gradient boosting (XGBoost) and PyTorch deep learning to Transformer self-attention math, LoRA/QLoRA LLM fine-tuning, Feast feature stores, MLflow tracking, and vLLM inference serving.

Deep Dive: Core AI Engineering Subsystems

The handbook provides functional PyTorch, HuggingFace PEFT, and XGBoost source code blueprints across five primary AI domains:

1. Transformer Multi-Head Self-Attention Mechanics

Understanding the mathematical engine driving modern Large Language Models (LLMs):

• Scaled Dot-Product Attention: Calculating $\text{Attention}(Q,K,V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$ from scratch in PyTorch.

2. Parameter-Efficient LLM Fine-Tuning (LoRA & QLoRA)

Fine-tuning 70B parameter models on consumer GPU hardware:

• Low-Rank Adaptation (LoRA): Injecting trainable rank decomposition matrices $A$ and $B$ into frozen LLM weight matrices while reducing VRAM usage by 80%.

3. Production MLOps & High-Throughput Inference Serving

Deploying AI models to production with zero downtime:

• vLLM & PagedAttention: Optimizing KV cache memory management to achieve 14x higher throughput compared to standard HuggingFace pipelines.

Field Engineering: PyTorch Multi-Head Self-Attention Implementation

Chapter 4 of the handbook provides practical PyTorch source code for constructing a Transformer Multi-Head Attention layer from scratch:

import torch
import torch.nn as nn
import math

class MultiHeadSelfAttention(nn.Module):
    def __init__(self, embed_dim=512, num_heads=8):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads

        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.out_proj = nn.Linear(embed_dim, embed_dim)

    def forward(self, x):
        batch_size, seq_len, _ = x.shape
        
        # Project & reshape to [batch_size, num_heads, seq_len, head_dim]
        Q = self.q_proj(x).view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        K = self.k_proj(x).view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        V = self.v_proj(x).view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)

        # Scaled Dot-Product Attention
        scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.head_dim)
        attn_weights = torch.softmax(scores, dim=-1)
        context = torch.matmul(attn_weights, V)

        # Concatenate heads & project back
        context = context.transpose(1, 2).contiguous().view(batch_size, seq_len, self.embed_dim)
        return self.out_proj(context)

mha = MultiHeadSelfAttention()
dummy_input = torch.randn(2, 16, 512) # [Batch: 2, SeqLen: 16, Dim: 512]
output = mha(dummy_input)
print(f"[+] Multi-Head Attention Output Shape: {output.shape}")

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, TaskType

model_id = "meta-llama/Llama-2-7b-hf"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto")

# Configure Low-Rank Adaptation (LoRA)
peft_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=16,                  # Rank Matrix Size
    lora_alpha=32,         # Scaling Factor
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none"
)

lora_model = get_peft_model(model, peft_config)
lora_model.print_trainable_parameters()
# Output: trainable params: 4,194,304 || all params: 6,742,609,920 || trainable%: 0.062%

Complete Table of Contents & Module Syllabus

• Module 01 Machine Learning Foundations: Supervised, Unsupervised & Math

  Pages 1–25
  Linear regression, logistic regression, decision trees, K-Means clustering, PCA, and loss functions.
• Module 02 Gradient Boosting & Ensemble Engineering (XGBoost, LightGBM)

  Pages 26–50
  Gradient boosted decision trees (GBDT), regularization, hyperparameter optimization with Optuna, and feature importance.
• Module 03 Deep Learning Mechanics: Neural Networks & Backpropagation

  Pages 51–75
  Multi-Layer Perceptrons (MLPs), automatic differentiation, PyTorch autograd, activation functions (ReLU, GELU), and AdamW optimizers.
• Module 04 Transformer Architecture & Attention Mechanics (QKV)

  Pages 76–100
  Query/Key/Value math, scaled dot-product attention, positional encodings (RoPE), and building Transformers in PyTorch.
• Module 05 LLM Fine-Tuning & Quantization: LoRA, QLoRA & PEFT

  Pages 101–125
  Parameter-Efficient Fine-Tuning (PEFT), LoRA rank matrices, 4-bit NF4 quantization, and SFT/RLHF instruction tuning.
• Module 06 MLOps Infrastructure: Feature Stores, MLflow & Kubeflow

  Pages 126–150
  Point-in-time feature engineering with Feast, MLflow experiment tracking, model registries, and Kubeflow pipeline DAGs.
• Module 07 High-Throughput Model Serving: Triton, vLLM & Quantization

  Pages 151–175
  vLLM PagedAttention KV cache management, Triton Inference Server setup, TensorRT-LLM, and FP16/INT8 latency benchmarks.
• Module 08 Production AI Observability: Data Drift, Concept Drift & Monitoring

  Pages 176–203
  Detecting data drift (KS-test, PSI), monitoring model latency, Evidently AI observability, and automated retraining triggers.

Who Should Read This Handbook?

This handbook is designed for modern AI software engineers and data science leads:

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