I decided that I was spending too much time writing instructions in random Google docs, text files, and GitHub repos. I wanted to have a single location that I could use as a way to publicly document all the things that I tinker with:

  • Kubernetes
  • Docker (who doesnt?)
  • Splunk
  • Raspberry Pi
  • Random tech integrations (this list could go on forever)

With all of that, I felt that it was time to put it all into a blog. As I travel through life building these random things, I intend to document them here and link to any files, repos, or websites that I have created myself or used along the way.

Enjoy!

Optimizing Deep Learning Models: A Step-by-Step Guide

Deep Learning Model Optimization Techniques: A Step-by-Step Guide Introduction Deep learning models are increasingly complex and computationally expensive, making optimization techniques crucial for deployment in real-world applications. In this article, we’ll explore the most effective methods for optimizing deep learning models, including quantization, knowledge distillation, and pruning. Prerequisites Basic understanding of deep learning concepts and architectures Familiarity with popular deep learning frameworks (e.g., TensorFlow, PyTorch) Experience with Python programming Quantization Quantization is a technique that reduces the precision of a model’s weights and activations, resulting in significant reductions in memory usage and computational requirements. ...

February 5, 2025 · 4 min · Scott

Scalable Serverless AI/ML Pipelines: A Step-by-Step Guide

Building Scalable Serverless AI/ML Pipelines As the demand for artificial intelligence (AI) and machine learning (ML) applications continues to grow, the need for scalable and efficient pipelines has never been more pressing. In this article, we will explore the benefits and challenges of building scalable serverless AI/ML pipelines and provide a step-by-step guide on how to implement them. Introduction Serverless architecture is a key enabler for scalable AI/ML pipelines, allowing data engineers to focus on building and deploying applications without managing infrastructure. By leveraging serverless computing services like AWS Lambda, Google Cloud Functions, and Azure Functions, we can create scalable and cost-effective pipelines that can handle large volumes of data. ...

January 31, 2025 · 4 min · Scott

Scaling Mobile App Development with React Native: A Comprehensive Guide

Scaling Mobile App Development with React Native: A Comprehensive Guide Introduction As the world becomes increasingly mobile-dependent, businesses are racing to deliver high-quality, seamless, and secure mobile applications to users. React Native has emerged as a versatile and cost-effective framework for building cross-platform mobile apps, allowing developers to create apps for both iOS and Android using a single codebase. In this guide, we will delve into the world of building secure and scalable mobile apps with React Native. ...

January 29, 2025 · 4 min · Scott

Container Networking Fundamentals: A Step-by-Step Guide

Container Networking Fundamentals: A Step-by-Step Guide ==================================================================== Containerization has revolutionized the way we deploy and manage applications, but effective container networking is crucial for communication and coordination between containers. In this guide, we’ll explore the fundamentals of container networking, including network namespaces, Linux bridges, and container communication. Prerequisites Before diving into container networking, you should have a basic understanding of: Containerization (e.g., Docker, Kubernetes) Networking concepts (e.g., IP addresses, ports) Linux operating system (kernel 4.x or higher) Step 1: Understanding Network Namespaces Network namespaces are a Linux kernel feature that allows multiple isolated network stacks to coexist on a single host. Each namespace has its own routing tables, network interfaces, and firewall rules. ...

January 24, 2025 · 3 min · Scott

Building Trustworthy Recommendation Systems with Responsible AI

Implementing Responsible AI in Recommendation Systems: A Step-by-Step Guide Introduction Recommendation systems are ubiquitous in modern applications, influencing everything from our social media feeds to our online shopping experiences. However, these systems can perpetuate biases and lack transparency, leading to unintended consequences. In this article, we’ll explore the importance of responsible AI in recommendation systems and provide a step-by-step guide on implementing strategies for mitigating bias and ensuring transparency. Prerequisites Basic understanding of recommendation systems and their applications Familiarity with machine learning concepts and Python programming language Access to a dataset for experimentation (e.g., MovieLens, Book-Crossing) Identifying and Understanding Bias in Recommendation Systems Bias in recommendation systems refers to the unfair or discriminatory treatment of certain groups or individuals. There are several types of bias that can occur in recommendation systems, including: ...

January 22, 2025 · 4 min · Scott