ABOUT THE AUTHOR

Katarzyna Hewelt

Data Scientist

Katarzyna is a Data Scientist at CodiLime with extensive machine learning and NLP skills and a strong passion for large language models and AI. She contributes to the tech community by sharing her knowledge through programming courses, articles on our blog, and by organizing TEDx Warsaw Women. Her background in various data roles gives her valuable insights into business needs. Katarzyna is skilled in data analytics, data engineering, and machine learning, making her an asset in her field. In her current role at CodiLime, she focuses on developing machine learning models, providing analytical support to clients, and creating innovative AI assistant solutions, including chatbots.

Katarzyna Hewelt

CONNECT WITH KATARZYNA HEWELT

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Recent posts by Katarzyna:

Thumbnail of an article about Hosting LLMs on AWS
DATA

Hosting LLMs on AWS

In this article, I will guide you step-by-step through the process of preparing the resources needed to successfully run LLMs on AWS.
Katarzyna Hewelt

Katarzyna Hewelt

Thumbnail of an article about The crucial role of machine learning metadata and its influence on content embeddings
OBSERVABILITY

The crucial role of machine learning metadata and its influence on content embeddings

Explore the realm of content embeddings, a crucial concept in machine learning that transforms complex data into a format that machines can efficiently process. Through improving data quality, providing contextual relevance, and enabling feature engineering, metadata is shown to be a crucial element in the development of advanced AI systems.
Katarzyna Hewelt

Katarzyna Hewelt

Thumbnail of an article about Data cleaning techniques: Strategies for reliable data analysis
DATA

Data cleaning techniques: Strategies for reliable data analysis

Data cleaning is a critical component of data analysis, essential for ensuring the accuracy and reliability of the results. This process involves refining data by fixing or removing errors and inconsistencies, a key step for maintaining the integrity of data-driven insights. However, this task presents various challenges. Analysts often face issues like missing values, which can lead to biased outcomes, outliers that may distort the analysis, and irregularities in data formats. If these issues are not properly addressed, they can significantly diminish the effectiveness of data analysis. Therefore, data cleaning is not merely a preliminary step; it is an integral part of producing trustworthy and actionable data insights.
Katarzyna Hewelt

Katarzyna Hewelt

Thumbnail of an article about [interview] Beyond bits and bytes — insights from data science
DATA

[interview] Beyond bits and bytes — insights from data science

While navigating this data-rich environment, the question arises: How can organizations harness the potential within this vast ocean of information to stay competitive and meet the dynamic needs of modern customers?
Multiple authors

Multiple authors

Thumbnail of an article about Understanding big data infrastructure: essentials and challenges
DATA

Understanding big data infrastructure: essentials and challenges

In today's data-driven world, organizations are gathering vast amounts of information at an unprecedented rate. This phenomenon has led to the birth and rapid evolution of "big data", a term that describes data sets so large and complex that they cannot be processed using traditional data management tools
Katarzyna Hewelt

Katarzyna Hewelt

Thumbnail of an article about Private Slack Chatbot: an integration of corporate resources and large language models (LLMs)
DATA

Private Slack Chatbot: an integration of corporate resources and large language models (LLMs)

Ever since the debut of ChatGPT, large language models have taken the limelight. Their breadth of knowledge is nothing short of remarkable. From drafting emails on our behalf to assisting in code development, these models showcase extensive general knowledge. Yet, a notable limitation is their unfamiliarity with specific details related to our companies, such as sensitive documents, company policies, and the like. The idea of interacting with our personal documents through such models is undoubtedly intriguing.
Katarzyna Hewelt

Katarzyna Hewelt

Thumbnail of an article about Detecting patterns, uncovering insights: the crucial role of data monitoring
DATA

Detecting patterns, uncovering insights: the crucial role of data monitoring

In our digital era, data is a crucial resource for a wide range of industries. The ability to manage, interpret, and derive insights from the overwhelming flood of information is essential. This is where the role of data monitoring becomes significant - a process that oversees and reviews data to ensure its quality, assess system performance, and guarantee data security. Data monitoring is a well-structured method that provides a comprehensive understanding of the state and flow of data throughout its life cycle.
Katarzyna Hewelt

Katarzyna Hewelt

Thumbnail of an article about Data wrangling — what it is and why it is important
DATA

Data wrangling — what it is and why it is important

As data continues to grow in both size and complexity, it is becoming increasingly difficult for organizations to extract valuable insights from it. This is where data wrangling comes in. Data wrangling, which is also known as data munging or data cleaning, is the process of gathering, cleaning, transforming, and preparing unprocessed data into a format that is more easily understood and analyzed. Data wrangling enables organizations to leverage the full potential of their data. In this article, we delve into data wrangling, exploring what it is, why it is important and the key tasks involved in the process.
Katarzyna Hewelt

Katarzyna Hewelt

Thumbnail of an article about Data Science vs. Data Analytics — main differences overview
DATA

Data Science vs. Data Analytics — main differences overview

We live in a world where data is ubiquitous. Websites track all their users’ every click. Your phone carries a map of where you are and where you’ve been. Smart homes record information about their occupants and sales sites collect data about your buying habits. More and more people want to look for usable information in the data, to draw practical conclusions. This interest in data has developed rapidly and widely with the consequence that companies are looking for professionals with data-driven skills to deal with specific data problems.
Katarzyna Hewelt

Katarzyna Hewelt

Thumbnail of an article about Sentiment Analysis. What is it and how to use it?
DATA

Sentiment Analysis. What is it and how to use it?

During face-to-face conversations or by reading text - we, people, can determine a speaker's intention or mood, whether they feel happy about something or not. We can also identify the polarity of the context - is the expression positive or negative or maybe even neutral? It is relatively easy for most people, but do computers understand emotions? Is it important for machines to understand humans’ intentions? With the technological progression and advancement of machine learning techniques - our machines are getting closer and closer to answering these questions.
Katarzyna Hewelt

Katarzyna Hewelt

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