About Kernel Labs by Kuriko IWAI - ML Engineering & Research

Deep dives into LLM deployment, CI/CD for ML, and Reinforcement Learning through 6+ production-grade projects and system architectures.

Explore:

  • Masterclass: Build eight AI systems to master LLM techniques.
  • ML Research & Blogs
    • Theory: Technical blogs on LLMs, Generative AI, Deep Learning, and traditional ML.
    • Learning Scenario: Specialized research into Unsupervised, Reinforcement, Meta, and Online Learning.
    • MLOps: Best practices on CI/CD integration, ML Lineage, and system architectures.
    • LLM & NLP: Advanced LLM engineering techniques and neural architecture deep dives.
  • Labs: Experimentations on ML systems with walk-through tutorials and code snippets.
  • Solution: ML system and ETL pipeline engineering, AI audit services.

Hosted by Kuriko IWAI

Kuriko IWAI is the founder and lead engineer at Kernel Labs, a Singapore-based research entity focused on agentic AI infrastructure and ML optimization.

Kuriko IWAI, ML engineer

Tech Stacks

  • Programming Languages: Python, JavaScript, R, SQL, Java
  • AI/ML: PyTorch, TensorFlow, Keras, Scikit-learn, HuggingFace, LangChain, CrewAI
  • MLOps Tools: DVC, Prefect, Airflow, Spark, Git, Docker
  • Data Science: Pandas, NumPy, Matplotlib, Excel, Tableau, Power BI, Google Analytics
  • Cloud Platform: AWS (Lambda, SageMaker, Bedrock), Azure, Google Cloud Platform
  • Frontend Frameworks: Node.js, React.js

Certifications

  • AWS Certified ML Engineer (AWS): Production ML Implementation
  • AWS Certified ML Specialty (AWS): Model Training & Deployment
  • Data Structures and Algorithms with Python (Codecademy): Basic data structures and algorithms.

What I Offer

  • End-to-end ML System Development: I architect and deploy end-to-end ML systems for your downstream services.
  • Infrastructure Architecture: I design reliable ML systems to ensure your models perform consistently in production.
  • Data Pipeline Engineering: I design ETL pipelines to cleanse, structure, and optimize raw data for model training.
  • Reliability & Security Audit: I audit your AI pipeline using evaluation frameworks and quantify system faithfulness.
  • Technical IP & Content: I create technical contents to turn complex ML concepts into actionable insights.
Contact 

Background

I am a AWS-certified Machine Learning Engineer specializing in building end-to-end ML systems, agentic frameworks, and structural risk minimization.

My background bridges the gap between technical execution and product strategy. I transitioned from leading the digital division of a Series-A gaming startup to engineering a Python SDK for autonomous AI agents that currently maintains a top-25% global open-source ranking.

This trajectory was driven by a fundamental need for technical sovereignty; during my tenure launching applications across the Southeast Asian market, I identified a critical failure point in relying on high-level vendor proposals and surface-level standups without a deep mastery of the underlying "how" and "why."

To resolve this, I moved into software development, mastering the full stack across Python and Javascript to build products from the ground up.

This transition led to the engineering multiple ML systems and high-performance frameworks and participation in pre-seed startup accelerators, where I focused on scaling autonomous systems.

Aside from coding, I enjoy publishing technical blogs to structure my expertise in boarder machine learning.

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