Darya Petrashka
Data Scientist at SLB
Szczytno, Poland
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AWS Community Builder, works as a Data Scientist at SLB. She is passionate about data and its usage for problem-solving. The area of interest includes classical ML and NLP, GenAI, as well as working with AWS services. An eternal student, she likes taking part in online schools, courses, and workshops.
Area of Expertise
Topics
Building a Bedrock agent for document processing
This session provides a step-by-step guide to building a Bedrock agent for document processing, showcasing how to automate tasks such as extracting key information, querying and altering documents, summarizing content, and organizing data. Attendees will learn how Bedrock agents work, explore potential use cases, and understand the concept of action groups. The session also dives into knowledge bases, covering their purpose, use cases, creation, querying, and integration with Bedrock agents. With a live demonstration and insights into integrating AWS services like Lambda, Textract, DynamoDB, and S3, this session equips attendees to build intelligent and efficient document workflows.
You don’t think about your Streamlit app optimization until you deploy it to AWS
Building Streamlit apps is easy for Data Scientists - but when it’s time to deploy them to the cloud, challenges like slow model loading, scalability, and security can become major hurdles. This talk bridges two perspectives: the Data Scientist who builds the app and the MLOps engineer who deploys it. We'll dive into optimizing model loading from Hugging Face Hub, implementing features like autoscaling and authentication, and securing your app against potential threats. By the end of this talk, you’ll be ready to design Streamlit apps that are functional and deployment-ready for the AWS.
Empower your Bedrock agent with GraphRAG
While traditional RAG excels in searching across unstructured documents, it often struggles to grasp intricate relationships between entities. GraphRAG overcomes this limitation by integrating knowledge graphs, enabling your solutions to understand and utilize these complex relationships.
In this session, we’ll explore how to supercharge Bedrock agents with GraphRAG, leveraging Amazon Neptune as the graph database alongside the Bedrock knowledge base. As part of the last re:Invent announcements, we'll see how this approach delivers precise, contextually rich answers.
The session centers on a use case in the tourism industry, but the techniques presented extend seamlessly across diverse domains such as document processing, healthcare, finance, media, and beyond.
Building with Generative AI on AWS using PartyRock, Amazon Bedrock, and Amazon Q
This workshop is designed for builders ready to learn about Generative AI on AWS. You'll learn to develop applications using PartyRock and Amazon Bedrock. You will focus on practical skills like prompt engineering and using different foundational models. We will also explore how to 'chat with your documents' through knowledge bases, retrieval augmented generation (RAG), embeddings, and agents. Also, you'll find out how to use Amazon Q Developer to help in coding and debugging.
In this workshop, you will complete the following 3 modules:
1. Build Generative AI Applications with PartyRock: Learn how to quickly build generative AI applications with no code.
2. Use Foundation Models in Amazon Bedrock: Learn how to use various foundation models to generate text and images using Amazon Bedrock.
3. Chat with your Documents: Learn how to use Amazon Bedrock to "Chat with your documents". We will explore how to build RAG applications highlighting knowledge bases, embeddings, and agents.
Building an AI Chat Assistant with Amazon Bedrock Agent
During the session, you will discover the potential of AWS's groundbreaking announcement from last year—the Bedrock agent. Focused on building a smart assistant, this session will provide insights into how the Bedrock agent leverages the reasoning capability of foundation models (FMs) to support workflow orchestration and automation.
Explore the capabilities of the Bedrock agent through a captivating example project, where you will witness its ability to monitor updates on messaging platforms like Telegram, seamlessly translate messages, summarize content, and extract actionable insights.
By the end of this 30-minute talk, every attendee will gain a deeper understanding of the possibilities that the Bedrock agent holds for optimizing productivity and facilitating smart assistance. Join me as we embark on this exploration of the Bedrock agent's potential.
NLP on AWS: SageMaker and high-level services
NLP (natural language processing) nowadays is a very popular component of machine learning. AWS proposes a huge variety of tools to solve various NLP tasks.
AWS SageMaker which enables data scientists to quickly and easily train and deploy machine learning models has built-in NLP algorithms. The BlazingText algorithm (text classification) and 2 topic modeling algorithms (LDA and Neural Topic Model) will be covered during the talk.
However, to solve a specific NLP task, it is not always necessary to write any code, AWS offers many high-level services: Amazon Translate can translate many languages, Amazon Transcribe performs speech-to-text tasks, Amazon Polly converts text into speech, and Amazon Lex builds chat-bots. There is also Amazon Textract that performs document text detection and analysis tasks. All the mentioned services can be used in different domains like retail, commerce, education, medicine, etc.
An example project of a voicing chatbot combining Lex, Polly, and Lambda will be provided.
Øredev 2025 Sessionize Event Upcoming
AWS User Group Kosice meetup #3 User group Sessionize Event Upcoming
AWS Community Day Baltic Sessionize Event
AWS Community pre:Invent Warmup Sessionize Event
AWS Community Day Italy 2025 Sessionize Event
AWS Community Day - Hungary 2024 Sessionize Event
AWS Community Day Nordics 2024 Sessionize Event
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