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Data science involves the application of scientific methodologies, algorithms, and tools to extract knowledge and insights from data. This interdisciplinary field combines statistics, machine learning, and domain expertise to analyze large datasets and make data-driven decisions. Data science plays a crucial role in solving complex problems, predicting trends, and supporting evidence-based strategies.

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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

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DATA
OPERATIONS

Unlocking the power of MLOps: revolutionizing machine learning deployment

Artificial intelligence (AI) has rapidly gained in popularity, revolutionizing numerous industries. However, despite the rapid advancements and widespread adoption of AI technologies, a significant challenge persists.
Tomasz Janaszka

Tomasz Janaszka

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DATA

How to use convolutional networks for time series classification?

In this blog post, we would like to address the topic of time series classification. This continues our consideration of the practical use of time series in the domain of networks and cloud infrastructure. In this article we focus on a use case related to distinguishing resource consumption characteristics (e.g. CPU, memory) for different workloads in a computing cluster.
Multiple authors

Multiple authors

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DATA

Self-hosted LLM: insights and practical deployment strategies

This comprehensive guide brings LLMs closer, offering a deep dive into their fundamentals and practical insights into deploying your own LLM.
Łukasz Rogalski

Łukasz Rogalski

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NETWORKS
DATA

Long-term time series forecasting: an insight into the methods

In this article, we describe the use case and reasoning for long-term traffic forecasting, next we focus on the methods we used, and discuss alternative approaches and challenges to overcome.
Mariusz Budziński

Mariusz Budziński

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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

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DATA

What is a large language model? LLM explained

Large language models are powerful algorithms taking the tech world by storm. Read the article to learn what they can be used for and how they work.
Tomasz Jędrośka

Tomasz Jędrośka

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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

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DATA

Introduction to time series for machine learning

Data is becoming an integral part of everyday life. Even if we are not fully aware of it, we deal with it all the time and it affects our lives in significant ways. This blog post elaborates on a particular type of data: time series. If you want to know more and discover what problems you can solve with time series analysis, you've come to the right place. Have you ever wondered how much data is created (including newly generated, captured, copied, or consumed data) each day? Currently it is 0.33 zettabytes every day!
Mariusz Budziński

Mariusz Budziński

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NETWORKS
DATA

AI and Machine Learning for Networks: natural language processing and reinforcement learning

This is the third part of the series, where we focus on the next two classes of ML methods: natural language processing and reinforcement learning. Also, we outline the major challenges of applying various ideas for ML techniques to network problems. This part also summarizes all three parts of the blog post. The first part can be found here, and the second part can be found here. Natural language processing is a part of AI which allows computer programs to understand statements and words written in human language.
Tomasz Janaszka

Tomasz Janaszka

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