PBH Applied Systems, LLC  ·  Oklahoma City, OK

Applied AI &
Machine Learning
Systems

Building high-performance artificial intelligence and machine learning systems designed for real-world deployment — from LLM optimization and quantized AI infrastructure to scalable ML pipeline engineering.

Get the Book Watch the Series Work Together
12 Video Series Episodes
1 Published Textbook
6+ Core AI Service Areas
UAT University Adopted
Patrick Hill
Founder · PBH Applied Systems, LLC
Program Controller Tinker AFB AI/ML Practitioner Published Author
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About

Turning Advanced AI Into Practical Systems

Artificial intelligence is powerful — but without proper engineering it remains an experiment. PBH Applied Systems focuses on the critical layer between research and deployment: designing AI systems that run efficiently, scale reliably, and produce consistent results in real operational environments.

Founded by Patrick Hill — a Program Controller at Tinker AFB and published author in applied machine learning — PBH Applied Systems brings a disciplined, engineering-first perspective to every AI and ML engagement. Every solution is built with reproducibility, performance efficiency, and long-term maintainability as non-negotiable requirements.

Patrick's textbook Applied Machine Learning: Concepts, Tools, and Case Studies has been adopted as required reading at the University of Advancing Technology, reflecting a commitment to rigorous, practical AI education grounded in real systems.

Core Services

AI & ML Engineering Capabilities

Each engagement is scoped to the specific engineering problem — from initial model selection through production deployment, evaluation, and long-term operational reliability.

01 — LLM Optimization
Large Language Model Optimization & Deployment
Deploying and optimizing modern open-source language models for practical applications. Reducing infrastructure requirements while maintaining model capability through quantization, adapter-based customization, and efficient inference pipeline design.
Model Quantization LoRA / QLoRA GPU Inference Benchmarking
02 — AI Infrastructure
Quantized AI Infrastructure
Building infrastructure that allows advanced models to run efficiently on available hardware. GGUF conversion workflows, GPU-accelerated inference using llama.cpp, VRAM profiling, and scalable distributed inference architectures.
GGUF Conversion llama.cpp VRAM Profiling Distributed Inference
03 — ML Pipelines
Machine Learning Pipeline Engineering
End-to-end machine learning systems from raw data to reliable predictive models. Data preprocessing, feature engineering, hyperparameter optimization, probabilistic calibration, and model interpretability — all designed for operational transparency.
Feature Engineering Hyperparameter Opt. Model Calibration Interpretability
04 — Evaluation
AI Evaluation & Model Reliability
Rigorous evaluation frameworks that measure model behavior under realistic conditions. Deterministic evaluation harnesses, structured output validation, prompt robustness testing, and golden-fixture systems for repeatable, trustworthy deployment.
Eval Harnesses Output Validation Prompt Robustness Golden Fixtures
05 — Synthetic Data
Synthetic Data & Privacy-Preserving AI
Systems that enable dataset generation while protecting sensitive information. Structured placeholder token systems, privacy-preserving training workflows, and closed-loop synthetic data generation and evaluation pipelines.
Synthetic Generation Data Privacy Token Systems Closed-Loop Eval
06 — Automation
AI Automation & Systems Integration
Python-based orchestration systems that integrate AI capabilities into operational workflows. Automated ML pipelines, GPU resource management, AI-driven content workflows, and end-to-end automation for AI and media infrastructure.
Python Orchestration GPU Management Workflow Automation Media Infrastructure
07 — Scalable Media
Scalable Media Production & AI Distribution Infrastructure
Automated media production and distribution systems that combine AI, professional media tools, and workflow automation into unified infrastructure. Integrating Adobe Premiere Pro, After Effects, Audition, and AI-assisted narration with Python-based automation services — moving organizations from isolated content creation to repeatable, scalable publishing systems across YouTube, LinkedIn, and other platforms.
Adobe Premiere / AE TTS / AI Narration Watch-Folder Daemons Metadata Pipelines Multi-Platform Distribution Workflow Orchestration
Applied Machine Learning: Concepts, Tools, and Case Studies — book cover
Published Textbook
Applied Machine Learning
Concepts, Tools, and Case Studies
Patrick Hill
Published Textbook

Applied Machine Learning:
Concepts, Tools, and Case Studies

A practitioner-focused textbook covering the full applied machine learning workflow — from foundational concepts and supervised learning through unsupervised methods, neural networks, and advanced deployment techniques. Every chapter is grounded in real datasets, working code, and the engineering discipline required to move models from experimentation into production.

Adopted as required reading at the University of Advancing Technology. Designed for practitioners, engineers, and students who need AI knowledge that translates directly to real systems.

Buy on Amazon Watch the Companion Series
Author
Patrick Hill
Adopted At
University of Advancing Technology
Companion
12-Video YouTube Series
YouTube Series

Applied Machine Learning — Video Series

A 12-video series mapping directly to the published textbook — each episode covers core concepts with working code, real datasets, and production-grade implementation details. Built with a fully AI-assisted production workflow as a live demonstration of disciplined AI tool use.

AML — 01
What Applied Machine Learning Actually Is
AML — 02
Data Is the Model
AML — 03
Supervised Learning Foundations
AML — 04
Classification in Practice
AML — 05
Regression & Prediction Systems
AML — 06
Ensemble Methods & Model Stacking
AML — 07
Unsupervised Learning
AML — 08
Neural Networks from the Perceptron Up
AML — 09
Deep Learning Architecture
AML — 10
Model Evaluation & Reliability
AML — 11
LLMs & Modern NLP
AML — 12
Deploying ML Systems in Production
Watch the full series on YouTube Each episode maps to the published textbook — companion reading enhances every video.
View on YouTube
Engineering Philosophy

Built on Practical Engineering

Every system PBH Applied Systems delivers is governed by the same engineering principles that make production AI trustworthy and maintainable at scale.

Reproducibility
Every pipeline, model, and evaluation is designed to produce consistent, verifiable results across runs and environments.
Performance Efficiency
Systems are optimized for the available hardware — maximizing capability while minimizing compute overhead and infrastructure cost.
Transparent Evaluation
Model behavior is measured rigorously before deployment. No system ships without validated evaluation against realistic conditions.
Scalable Architecture
Designs are built to grow — from prototype to production — without requiring architectural rewrites as requirements evolve.
Long-Term Maintainability
Code and pipelines are documented, modular, and built for the engineers who will operate them after initial delivery.
Work With PBH Applied Systems

Let's Build Something That Works

Whether optimizing large language models, engineering machine learning pipelines, or designing scalable AI infrastructure — PBH Applied Systems provides the expertise needed to move AI from experimentation into production.

Organizations seeking practical, high-performance AI solutions are invited to connect to discuss project requirements and collaboration opportunities.

Email patrick@pbhappliedsystems.com LinkedIn linkedin.com/in/patrickhill YouTube @pbhappliedsystems Book Applied Machine Learning — Amazon
Services Available For Engagement
  • Large Language Model Optimization & Deployment
  • Quantized AI Infrastructure Engineering
  • Machine Learning Pipeline Design
  • AI Evaluation & Reliability Frameworks
  • Synthetic Data & Privacy-Preserving AI
  • AI Automation & Systems Integration
  • Scalable Media Production Infrastructure
  • Technical AI Education & Curriculum