Getting Started With GPU Scheduling for Training Clusters: A Developer Walkthrough

This is a practical, up-to-date guide to Getting Started — what it is, why it matters in 2026, and how to apply it in real projects. It is written for developers and founders who want clear answers and proven best practices, not filler.

Whether you're just starting out or leveling up, treat this as a working reference you can return to. Every section is built to be skimmed, applied, and shared.

Model monitoring and drift detection

Once a model is live, monitoring is what tells you whether it is still doing its job, and it spans operational metrics like latency and error rate as well as ML-specific signals. Data drift describes a change in the distribution of incoming features relative to training data, while concept drift describes a change in the relationship between features and the target, and either can quietly erode accuracy without any code changing. Because ground-truth labels often arrive late or never, teams rely on proxy signals such as prediction distribution shifts, embedding drift, and input validation to catch problems early. Tools like Evidently, Arize, WhyLabs, Fiddler, and NannyML specialize in this, computing statistical distance measures such as population stability index or Kolmogorov-Smirnov and alerting when they cross a threshold.

Evaluating LLM applications

Evaluation for LLM systems replaces the single accuracy score of classic ML with a portfolio of checks, because outputs are free-form text judged on correctness, relevance, safety, and style. Practical eval combines deterministic assertions (does the JSON parse, does it contain the required field) with reference-based metrics and, increasingly, LLM-as-judge scoring where a strong model grades responses against a rubric. Retrieval-augmented systems get their own metrics such as context precision, recall, and faithfulness, popularized by frameworks like RAGAS. The discipline is to maintain a curated, versioned evaluation set, run it in CI on every prompt or model change, and treat regressions as blocking, using tools such as OpenAI Evals, Braintrust, LangSmith, DeepEval, or Promptfoo.

What is MLOps?

MLOps is the set of practices, tooling, and culture for reliably taking machine learning models from experimentation into production and keeping them healthy over time. It borrows heavily from DevOps but adds concerns that traditional software does not have, most notably that the behavior of an ML system depends on data as much as on code. Where a web service is deterministic given its inputs, a model can silently degrade as the world shifts underneath it, so MLOps extends CI/CD with data versioning, model registries, continuous monitoring, and retraining loops. The goal is to make model deployment repeatable, auditable, and boring rather than a heroic one-off effort.

CI/CD for machine learning

CI/CD for ML extends the familiar build-test-deploy pipeline to cover data and models, which introduces stages that software pipelines do not have. Beyond running unit tests on code, an ML pipeline validates incoming data schemas and quality, triggers training when new data or code arrives, evaluates the resulting model against a holdout set and the current production model, and only promotes it if it clears the bar. Continuous training, where retraining is automated on a schedule or triggered by drift alerts, is the ML-specific addition that keeps models fresh. Orchestrators such as Kubeflow Pipelines, Metaflow, Airflow, Dagster, and ZenML define these workflows as code, while DVC and Git-based data versioning make each run reproducible from data to model.

Model serving with vLLM and TGI

Model serving is the runtime layer that turns a trained model into a low-latency, high-throughput API, and for open-weight LLMs the dominant engines are vLLM and Hugging Face Text Generation Inference. vLLM introduced PagedAttention, which manages the attention key-value cache in non-contiguous pages so that GPU memory is used efficiently and many requests can be batched together, while TGI offers a production-hardened server with tensor parallelism, quantization, and streaming. Both rely on continuous (in-flight) batching, where new requests join a running batch instead of waiting for a fixed window, which is the single biggest lever for GPU utilization. Alternatives and complements include NVIDIA Triton with its TensorRT-LLM backend, SGLang, and managed endpoints, but vLLM has become the common default for self-hosting.

How LLMOps differs from classic MLOps

LLMOps is the specialization of MLOps for applications built on large language models, and it shifts the center of gravity from training your own models to orchestrating, prompting, and evaluating foundation models you often did not train. Classic MLOps assumes you own the training pipeline and can retrain to fix drift; with hosted LLMs you instead manage prompts, retrieval pipelines, tool definitions, and provider selection. Evaluation becomes harder because outputs are open-ended and non-deterministic, pushing teams toward LLM-as-judge scoring and human review rather than a single accuracy number. New operational primitives appear too, such as token-cost budgeting, prompt versioning, semantic caching, and guardrails against prompt injection and unsafe output.

Getting Started: Key Facts and Data

According to recent industry research and the official documentation linked below:

• As of 2025, NVIDIA GPUs (via CUDA) remain the dominant hardware for training and inference, though AMD (ROCm), Google TPUs, AWS Trainium/Inferentia, and other accelerators have grown as alternatives.
• The rise of large language models drove the coining of the term LLMOps around 2022-2023, reflecting new operational concerns like prompt versioning, token-cost management, and non-deterministic output evaluation.
• MLOps emerged as a discipline around 2018-2019, adapting DevOps practices to the distinct challenges of data and model lifecycle management, and by 2025 it is a standard function on most mature ML teams.

Quick-Reference Summary

A map of what this guide covers:

Topic	What you'll learn
Model monitoring and drift detection	Once a model is live, monitoring is what tells you whether it is still doing its job, and it spans operational metrics
Evaluating LLM applications	Evaluation for LLM systems replaces the single accuracy score of classic ML with a portfolio of checks
What is MLOps?	MLOps is the set of practices, tooling, and culture for reliably taking machine learning models from experimentation
CI/CD for machine learning	CI/CD for ML extends the familiar build-test-deploy pipeline to cover data and models
Model serving with vLLM and TGI	Model serving is the runtime layer that turns a trained model into a low-latency
How LLMOps differs from classic MLOps	LLMOps is the specialization of MLOps for applications built on large language models

How to Get Started with Getting Started

A simple path that works:

1. Learn the fundamentals of Getting Started from primary sources, not just tutorials.
2. Build one small, real project end to end.
3. Get feedback, refactor, and add tests.
4. Ship it publicly and document what you learned.
5. Repeat with a slightly harder project each time.

Build It with a World-Class Full Stack Developer

Sandeep Kumar Chaudhary is a full stack world-class developer. If you want to turn this into a real, production-ready product, get in touch — message directly on WhatsApp at +9779802348957 for a fast, no-pressure consult.

You can also explore the projects already shipped to thousands of users, or start a conversation here.

Final Thoughts

Treat data and models as versioned, testable artifacts, not one-off scripts, or reproducibility and rollback will be impossible when something breaks in production. The developers and teams who win in 2026 pair strong fundamentals with consistent shipping. Start small, stay curious, build in public, and revisit this guide as your skills grow.

Sources and Further Reading

• MLflow Documentation
• Kubeflow Documentation
• vLLM Documentation
• Feast (Feature Store) Documentation
• MLOps (Wikipedia)
• Kubernetes Documentation