Data Science Blog | Nikolay Manchev

Nikolay Manchev is the Principal Data Scientist for EMEA at Domino Data Lab. In this role, Nikolay helps clients from a wide range of industries tackle challenging machine learning use-cases and successfully integrate predictive analytics in their domain specific workflows. He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King's College London. His area of expertise is Machine Learning and Data Science, and his research interests are in neural networks and computational neurobiology.

How data science can fail faster to leap ahead

One of the biggest challenges in data science today is finding the right tool to get the job done. The rapid change in best-in-class options makes...

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Speeding up Machine Learning with parallel C/C++ code execution via Spark

The C programming language was introduced over 50 years ago and it has consistently occupied the most used programming languages list ever since....

Machine Learning PySpark Parallel Processing

Semi-uniform strategies for solving K-armed bandits

In a previous blog post we introduced the K-armed bandit problem - a simple example of allocation of a limited set of resources over time and under...

Machine Learning Python Reinforcement Learning

Polars - A lightning fast DataFrames library

We have previously talked about the challenges that the latest SOTA models present in terms of computational complexity. We've also talked about...

Data Science Python Pandas Parallel Processing

Increasing model velocity for complex models by leveraging hybrid pipelines, parallelization and GPU acceleration

Data science is facing an overwhelming demand for CPU cycles as scientists try to work with datasets that are growing in complexity faster than...

Machine Learning Models Tuning Hyperparameters GPU

Reinforcement Learning: The K-armed bandit problem

In a previous blog post we talked about the foundations of reinforcement learning. We covered classical and operant conditioning, rewards, states,...

Code Python Reinforcement Learning

Reinforcement Learning Introduction: Foundations and Applications

Introduction When we think about learning, we are often tempted to focus on formal education that takes place during childhood and adolescence. We...

Machine Learning RL

Getting Started with Ray

In this blog post we give a quick introduction to Ray. We talk about the architecture and execution model, and present some of Ray's core paradigms...

Code Engineering High-Performance Computing Ray Python

Spark, Dask, and Ray: Choosing the Right Framework

Apache Spark, Dask, and Ray are three of the most popular frameworks for distributed computing. In this blog post we look at their history, intended...

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Explaining black-box models using attribute importance, PDPs, and LIME

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights...

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Building a Named Entity Recognition model using a BiLSTM-CRF network

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated...

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Accelerating model velocity through Snowflake Java UDF integration

Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data...

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Fitting Support Vector Machines via Quadratic Programming

In this blog post we take a deep dive into the internals of Support Vector Machines. We derive a Linear SVM classifier, explain its advantages, and...

Data Science Code Data Science Featured Machine Learning SVM Python

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. We present the...

Data Science Code Homepage Featured Main Machine Learning Practical Techniques

Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

In this article, we'll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot...

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On-Demand Spark clusters with GPU acceleration

Apache Spark has become the de facto standard for processing large amounts of stationary and streaming data in a distributed fashion. The addition...

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