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Data exploration covers collection, processing, storage, and analysis. Extracting valuable insights from the collected data informs decision-making processes and enhances overall business intelligence.

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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

on Oct 19, 2023
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[interview] Unlocking the future: A glimpse into the world of R&D at CodiLime

In the ever-evolving landscape of technology, research and development (R&D) plays a pivotal role in driving innovation, pushing the boundaries of knowledge, and shaping the future of industries. R&D is the heart of organizations that innovate, explore, and create, and it is the engine behind breakthroughs that change the way we live and work. In this interview, we’ll delve into the world of R&D with Paweł Parol, the leader of one of the R&D teams at CodiLime. Paweł Parol: Well, in short, R&D stands for research and development.
Multiple authors

Multiple authors

on Sep 29, 2023
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DATA

[interview] The modern art of data transformations — how data engineering unlocks information potential

By 2025, global data creation is said to exceed 180 zettabytes. In 2020, we had already consumed and created more than 64 zettabytes. These data carry information that can put any business ahead of its competition. But how to take advantage of data effectively and use it to improve products, services and meet modern customers' needs? In this interview, we'll discuss the importance of data engineering with Tomasz Jędrośka, Head of Data Engineering at CodiLime. Tomasz's role is to grow this relatively new division of the company and make sure it delivers competence synergies with our Networking, Cloud, and Security services and therefore extend further CodiLime’s competitive advantages.
Multiple authors

Multiple authors

on Aug 10, 2023
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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

on Jul 10, 2023
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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

on Jul 7, 2023
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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

on Jun 12, 2023
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AI and Machine Learning for Networks: classification, clustering and anomaly detection

This is the second article in the series AI/ML for networks. In this article we focus on the two classes of ML methods: classification and clustering. We also mention anomaly detection, which is an important topic in the context of network-related data processing where various classes of ML algorithms can be used. The first article of the series can be found here. In machine learning, classification is a supervised learning problem of identifying to which category an observation (or observations) belongs to (see Figure 1).
Tomasz Janaszka

Tomasz Janaszka

on Jun 5, 2023
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DATA

AI and Machine Learning for Networks: time series forecasting and regression

Artificial intelligence (AI) and machine learning (ML) are trending topics in all technological domains. They offer a rich set of methods for data processing that can be used to solve practical problems, including those occurring in networks. We have prepared a series of articles to give you a better look at the various methods you can use for solving specific network issues. In a series of three articles, we present classes of AI/ML methods and algorithms that should play a key role in networking, considering the network/related data types they work on as well as specific types of problem they can help to solve.
Tomasz Janaszka

Tomasz Janaszka

on May 29, 2023
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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

on May 8, 2023
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DATA

AI & machine learning for networks: example use cases

In today's digital age, the use of machine learning (ML) in networks has become increasingly prevalent. Modern businesses rely heavily on networks to maintain operations. However, it could be more and more challenging to manage network infrastructure effectively. One solution is to use machine learning (ML) algorithms to analyze network data and provide insights that can lead to more efficient network management. In this article, we will explore several ML use cases in network management including time series forecasting, capacity planning, intelligent alerting, and the use of external data to enable faster recovery of network components.
Karolina Rusinowicz

Karolina Rusinowicz

on May 2, 2023
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