ABOUT THE AUTHOR

Tomasz Janaszka

Solutions Architect

Tomasz Janaszka is a solutions architect with over 20 years of experience in the telco/IT industry. He is a doctor of technical sciences in the field of telecommunications and an experienced engineer who has worked for many years in research and operational projects. He has worked as a leader, as a solution architect and as a developer in projects covering networks design, capacity planning, traffic engineering, load balancing, resources optimization and focusing on software solutions supporting automation of business processes related to network management. Currently working in CodiLime’s R&D department, he explores AI/ML technology looking for practical applications in various networking aspects. Tomasz is the author and the co–author of several scientific publications and a speaker at conferences, webinars and technical workshops. Check out his ebook "AI and Machine Learning for Networks" : https://resources.codilime.com/ebook-ai-ml-for-networks

Tomasz Janaszka

CONNECT WITH TOMASZ JANASZKA

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Recent posts by Tomasz :

Thumbnail of an article about Network Automation Forum AutoCon 2 2024: insights and summary
NETWORKS

Network Automation Forum AutoCon 2 2024: insights and summary

As part of the event, CodiLime led the workshop "Utilize GenAI for network troubleshooting" showcasing how generative AI can revolutionize network operations. Additionally, at our dedicated stand, we demonstrated the Net-Chat Assistant, an AI-powered tool designed to simplify network troubleshooting and automation workflows. The conference provided a fantastic platform to share knowledge, connect with industry leaders, and explore the future of network automation.
Tomasz Janaszka

Tomasz Janaszka

Thumbnail of an article about Unlocking the power of MLOps: revolutionizing machine learning deployment
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

Thumbnail of an article about How to use convolutional networks for time series classification?
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

Thumbnail of an article about [interview] The role of machine learning in modern network architecture and operations
NETWORKS

[interview] The role of machine learning in modern network architecture and operations

While discussing modern network architecture and operations, machine learning (ML) integration promises enhanced efficiency, security, and adaptability. During our interview, Tomasz Janaszka, a seasoned Solution Architect, sheds light on the current state of ML in networking, its challenges, and its future direction.
Multiple authors

Multiple authors

Thumbnail of an article about AI and Machine Learning for Networks: natural language processing and reinforcement learning
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

Thumbnail of an article about AI and Machine Learning for Networks: classification, clustering and anomaly detection
NETWORKS
DATA

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

Thumbnail of an article about AI and Machine Learning for Networks: time series forecasting and regression
NETWORKS
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

Thumbnail of an article about AI and Machine Learning landscape for networks [infographic]
NETWORKS
DATA

AI and Machine Learning landscape for networks [infographic]

Aritifical intelligence (AI) and machine learning (ML) is a trending topic in all technological domains. It offers a rich set of methods for data processing that can be used to solve practical problems, including those occurring in networks. In this article we present a set of network issues for which the use of ML methods and different AI types can bring benefits and generally seems to make sense. Please note that this is a non-exhaustive list and is open to additions. For greater clarity, five main categories have been used.
Multiple authors

Multiple authors

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