Module 15: MLOps Training & Model Serving Engines (`MOD-MLOPS`)

Version: 2.0.0

Module Syllabus & Overview

Welcome to Module 15: MLOps Training & Model Serving Engines!

In Module 14 (MOD-AI), you established the physical hardware intuition and systems orchestration engines required to transform a standard Kubernetes cluster into an elite AI supercomputing platform. You can provision GPU nodes via the NVIDIA GPU Operator, configure Multi-Instance GPUs (MIG) and Time-Slicing to eliminate hardware stranding, wire lossless InfiniBand networks with GPUDirect RDMA, and monitor physical silicon telemetry via DCGM and Prometheus.

You have built the physical foundation of the AI supercomputer.

However, having a perfectly provisioned GPU supercomputer is entirely useless if your machine learning engineers cannot efficiently deploy their training jobs and inference engines onto it!

In legacy organizations, bridging the gap between data scientists (who write Python and PyTorch) and platform engineers (who write YAML and Terraform) results in an absolute operational breakdown known as The MLOps Divide.

Data scientists are not Kubernetes experts! If you ask a data scientist to manually author a 500-line Kubernetes StatefulSet manifest to establish an 8-node distributed PyTorch training ring with NCCL environment variables, they will struggle for weeks, encounter permanent crash loops, and give up!

Furthermore, when data scientists attempt to serve a massive Large Language Model (e.g., Llama 3 70B) in production, they frequently wrap the raw PyTorch model inside a basic Python Flask or FastAPI web server.

Serving a Large Language Model inside a basic Python Flask web server is an operational catastrophe!

A basic Flask web server processes incoming HTTP requests sequentially or creates basic thread pools. It completely lacks Dynamic Batching. If 10 users send chatbot requests simultaneously, Flask sends 10 separate, tiny calculation requests to the GPU. The GPU’s massive Tensor Cores sit 90% idle waiting for data, inference latency is terrible (taking 30 seconds per word), and the server achieves a microscopic throughput rate!

To make matters worse, raw PyTorch does not optimize memory during text generation. As a Large Language Model generates text word-by-word, it must store past attention keys and values in GPU High Bandwidth Memory (HBM)—a mechanism known as the KV Cache. In a naive implementation, the KV Cache suffers from severe memory fragmentation, wasting up to 60% of VRAM! When the 11th user connects, the GPU instantly suffers a fatal CUDA Out-Of-Memory (OOM) crash!

Finally, when traffic surges, legacy Kubernetes autoscalers (HorizontalPodAutoscaler) inspect host CPU utilization (cpu > 80%). However, in an AI serving engine, host CPU sits at 15% while the GPU VRAM and request queues are completely saturated! The HPA fails to scale the pods, the request queue overflows, and the entire AI service blacks out!

This module bridges your advanced Platform Engineering capabilities directly into the world of MLOps Training & Model Serving Engines. We maintain our Version 2.0 educational philosophy: building deep conceptual intuition, demystifying abstract MLOps mechanics with crystal-clear real-world analogies, and ensuring you develop elite, verifiable AI platform capabilities.

Capability Statement

“I can deploy distributed training operators (KubeRay/Kubeflow), provision high-throughput inference engines (vLLM/Triton), configure PagedAttention/KV cache memory allocation, and autoscale GPU workloads via KEDA.”

By the end of this module, you will establish the essential MLOps platform capabilities required to deploy declarative distributed training operators (Kubeflow PyTorchJob, KubeRay), architect high-throughput model serving engines (vLLM, TensorRT-LLM, Triton), configure advanced memory optimizations like Dynamic Batching and PagedAttention to eliminate VRAM fragmentation, optimize model artifact storage pulling (s3://model-registry), and autoscale GPU inference pods dynamically using KEDA (Kubernetes Event-driven Autoscaling) based on custom GPU metrics and request queue depths.

Essential Module Anchors

Why am I learning this? Machine learning engineers rely on Platform Engineers to provide automated, declarative deployment engines for their training and serving workloads. Mastering MLOps operators and inference engines ensures your enterprise achieves maximum GPU throughput, zero OOM crashes, and flawless autoscaling.
How will I use it? You will use these exact skills to deploy Kubeflow PyTorchJob manifests (cleanPodPolicy: Running), deploy KubeRay RayCluster Custom Resources, configure vLLM inference deployments (--max-model-len=4096), configure Triton model repositories (config.pbtxt), author KEDA ScaledObject manifests (DCGM_FI_DEV_GPU_UTIL > 80), and optimize S3 model pulling init containers.
Where does this fit into Platform Engineering? MLOps concludes Stage 6 (AI & MLOps). It utilizes your GPU infrastructure capabilities (MOD-AI) and Kubernetes orchestration (MOD-K8S) to provide a self-service, high-performance runtime layer for enterprise AI workloads.
What problem does it solve? It solves the critical enterprise challenges of manual distributed training configuration, terrible inference latency (Flask wrappers), severe VRAM fragmentation (unoptimized KV cache), slow model loading times (140GB weights over the network), and broken autoscaling (CPU-bound HPAs failing on GPU workloads).
Where will I use it later? You will use these skills directly in Module 16 (Surfacing MLOps Templates in an Internal Developer Platform), Module 17 (Testing Multi-Region Model Serving Resilience), and Module 18 (Building a Production-Grade AI Enterprise Platform Capstone).

Lesson Directory

This module consists of five progressive, highly instructional lessons:

Progression & Difficulty Scope

Beginner: 20%
Intermediate: 40%
Advanced: 40%

Note: In accordance with our Version 2.0 mastery learning progression model, this module establishes foundational MLOps operator intuition before advancing into complex vLLM memory mechanics, kernel-level PagedAttention paging tables, and advanced KEDA external metric scaling.

Real-world MLOps and Vector Database Implementation MLOps & Vector Databases - Lesson 1

Module 15: MLOps Training & Model Serving Engines (MOD-MLOPS)