Armin Hadzalic


2011 - Present

Software Developer

Development of Industrial Copilot (GenAI)

Development of  STEP 7 language editors (GUI)

Development of automated tests (including test infrastructure) and verification of software quality in industrial applications

.Net Application Development
Data Science & Machine Learning

2007 - 2011

Software Developer
Development of High-Speed-Placement machine generation and
Assembly Lines for Automotive Industry

.Net Application Development

1999 - 2007

Software Developer
Development of Surface Finishing Machines
for Automotive and Medical Industry

SimoDrive, Sinumerik

About Me

Hi! My name is Armin Hadzalic ( pronounced as h a d͡ʒ a l i t͡ʃ ). I am carrying over 15 years of experience in Hardware & Software Testing Industry. Currently I am working in Erlangen / Germany as a Software Developer on one of the biggest Automation Tools.

In my free time I’m a passionate chessplayer and I like to read books (specially the scientific literature). I enjoy keeping up with the technology development, studying programming languages and new technologies like the Internet of Things or Machine learning and Generative AI. Well, that free time is not often. I am coaching three times a week a B- and C-Junior football team in Nuremberg.

My slogan:
Ask | Learn | Share


2013 - Present

Wirtschaftsinformatik - FernUni Hagen


Certified Software-Tester (ISTQB)


Qualitätsmanagementbeauftragter (QMB)


Ausbilderprüfung (IHK)


Staatlich geprüfter Elektrotechniker / Automatisierungstechnik


Fachhochulreife - Berufskolleg Wuppertal


SPS-Techniker nach ZVEI


Energieelektroniker Anlagentechnik


.Net 95%
Software Development 95%
GenerativeAI 90%
Machine Learning 90%
Software Testing 90%
Presentation 90%
PLC 90%
DesignPatterns 90%
Windows Presentation Foundation 90%
CleanCode 85%
WordPress 85%
Inkscape 80%
Html & CSS 75%
Big Data 65%
Wirtschaftsinformatik 65%
Docker / Kubernetes 50%


* Learn Docker fundamentals
* Understand how it can compliment Machine Learning
* Train machine learning models during the Docker
* Serialize your models within the Image for easy retrieval
* Perform batch inference using Docker containers
* Understand online inference with a Real World example
* Implement a REST API using Docker and Flask RESTful

* Setup TensorFlow and Colab Runtime
* Load the Quora Insincere Questions Dataset
* TensorFlow Hub for Natural Language Processing
* Define Function to Build and Compile Models
* Train Various Text Classification Models
* Compare Accuracy and Loss Curves
* Fine-tune Model from TF Hub
* Train Bigger Models and Visualize Metrics with TensorBoard

* Sentiment analysis
* Data pre-processing
* Calculating word frequencies
* Vocabulary creation
* Supervised learning

* Error analysis
* Naive Bayes inference
* Log likelihood
* Laplacian smoothing
* Bayes rule

* Dimensionality reduction
* Principal component analysis
* Cosine similarity
* Euclidean distance
* Vector space models

* Gradient descent
* K nearest neighbors
* Document search
* Machine translation
* Frobenius norm

Create and save Tensorflow models as servable objects Integrate custom functions into servables Serve TF servables using conforming to REST

DevOps methodologies
Version control systems
Continuous integration and deployment tools
Jenkins, TeamCity, Maven
Software and automation testing frameworks
Configuration management tools Puppet, Chef, Ansible, Saltstack
Containerization with Docker
Continuous monitoring with Nagios, Grafana, ELK, Stack
Cloud on DevOps
Deploying Kubernetes clusters

Neural Networks and Deep Learning
Sequence Models
Structuring Machine Learning Projects
Convolutional Neural Networks
Improving Deep Neural Networks:
Hyperparameter tuning, Regularization and Optimization

Introduction to machine learning, datamining, and statistical pattern recognition.
(i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning)
(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
Case studies and applications:
apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam),
computer vision, medical informatics, audio, database mining

Part 1: Data Preprocessing
Part 2: Regression
Part 3: Classification
Part 4: Clustering
Part 5: Association Rule Learning
Part 6: Reinforcement Learning
Part 7: Natural Language Processing
Part 8: Deep Learning
Part 9: Dimensionality Reduction
Part 10: Model Selection & Boosting


An important step in understanding machine learning processes and evaluations is the interpretation of various metrics. It can be very time-consuming to read individual results numerically, particularly if the model has been training for many epochs.
In this article I want to show how Tensorboard enables one to, epoch over epoch, visually track your model’s cost (loss) and accuracy (acc) across both your training data and your validation (val) data.

What does a NLP pipeline look like that can determine the similarity of words from a text corpus and at the same time illustrate it clearly? And, is it really possible to find relations within a text corpus without having explicitly programmed it? I was a little sceptical, to be honest.
Check out how German Parliament speeches can be visualized.