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Daniel Kleine

Data and AI Expert

About Me

I'm a Industrial and Organizational Psychologist having several years of experience as a Data Scientist specialized in traditional Machine Learning as well as Deep Learning with competencies in MLOps. My interdisciplinary background enables me to integrate people, business, and technology effectively. Currently, I'm actively pursuing skill development in the field of Generative AI.

Within the field of Artificial Intelligence, I am particularly - but not exclusively - interested in following subjects:

  • Natural Language Processing and Large Language Models
  • Computer Vision
  • Reinforcement Learning
  • Interpretable Machine Learning and Explainable Artificial Intelligence

Projects

Named Entity Recognition with LLaMA 3.2

  • Using LLaMA 3.2 1B to explore the effectiveness of autoregressive (decoder-only) LLMs for NER tasks with an bidirectional attention approach.

Barrel Detection for Radio-Controlled Tank Toy

  • Using YOLOv8 to estimate the location and orientation of a toy tank's barrel with a smartphone camera for augmented reality applications.

LLM-powered Chatbot for arXiv Knowledge Retrieval

  • Using OpenAI API for a Question-Answering System utilizing arXiv.org scientific publications

Prediction of car bad buys

  • Data Science workflow from data preparation to model interpretation to avoid making a bad used car purchases

Fashion Shop Gender Prediction Model

  • Gender prediction model for an e-Commerce Fashion Shop based on customers' browsing behavior, enabling the shop owner to provide the best possible shopping experience for each customer

Tracking & Reporting

  • Data flow for tracking and reporting using historical weather data with Google Cloud Platform, dbt (data build tool), and Google Looker Studio

Detection of knee or elbow points

  • Detecting knees/elbows (data points showing the best balance inherent tradeoffs) in discrete data sets based on the mathematical definition of curvature for continuous functions

Certifications

AI Engineering

Using AI technologies on Azure, covering computer vision, natural language processing, conversational AI, and knowledge mining.

  • Implementing and deploying Computer Vision applications.
  • Designing and creating conversational AI solutions with NLP capabilities.
  • Extracting, organizing, and utilizing knowledge from data sources.
Generative AI

Understanding foundational Generative AI principles, large language models, computer vision, and building AI solutions

  • NLP fundamentals, transformer architectures, retrieval augmented generation (RAG), and building custom chatbots.
  • Computer vision fundamentals, GANs, transformer-based computer vision models, and working with diffusion models for image generation.
  • Practical GenAI applications, focusing on semantic search, vector databases, LangChain framework.
Cloud DevOps

Deploy, test, and monitor cloud applications on Azure

  • DevOps and Azure infrastructure basics, focusing on security best practices and using Terraform to deploy a web server.
  • Agile project management principles, execute Python-centric CI strategies in Azure, and build a CI/CD pipeline for a machine learning project.
  • Evaluate cloud-based application performance using Selenium and Postman, implement JMeter for performance testing, and establish a systemic monitoring process in Azure.
ML Engineering

Effective end-to-end management of the machine learning lifecycle at scale using Azure Machine Learning's MLOps capabilities

  • Azure ML platform and Azure ML SDK for comprehensive machine learning
  • Automating machine learning processes with HyperDrive and AutoML
  • Model deployment, testing, and endpoint consumption
Natural Language Processing

Machine learning and deep learning skills with the addition of natural language processing and speech recognition techniques

  • Text processing fundamentals including stemming and lemmatization
  • Advanced techniques like word embeddings, deep learning attention, and more
  • Voice user interface (VUI) techniques that turn speech into text and vice versa
Machine Learning DevOps (MLOps)

Optimized integration of Machine Learning models and deploy them in a production-level environment

  • Deploying Machine Learning Models in Production
  • Efficiency, effectiveness, and productivity in modern, real ML projects best practices around reproducible ML workflows
  • Use of an machine learning model in production
  • Full automation of MLOps processes required to evaluate and redeploy ML models
(Deep) Reinforcement Learning

Fundamentals of reinforcement learning that can be applied to implementations of many classic solution methods and modern approaches to applications such as video games and robotics

  • Applying deep learning architectures to reinforcement learning tasks
  • Evolutionary algorithms and policy gradient methods
  • Collaboration respectively cooperation on a complex task

Computer Vision

Computer vision capabilities for various applications such as image and video processing, autonomous vehicle navigation, medical diagnostics, and smartphone apps, etc.

  • Basics of Computer Vision and image processing
  • CNN and RNN networks
  • Locate objects and track them over time