Lingaro Data Science and AI Conferences
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Timeline
Generative AI: A Breakthrough or Overrated?
Lingaro Senior AI Engineer Konrad Łyda, Lingaro AI Competency Manager Marcin Borucki, and No Fluff Jobs IT Project Manager and Team Lead Maciej Staniewski-Łobacz explore our hopes amidst the hype.
Leveraging Cloud-Based Deep Learning Models for Enhanced Computer Vision Application
Where is the exciting frontier of computer vision heading? What is the impact of infusing it with cloud-based deep learning models and large language models (LLMs)?
Discover how these models are shaping the future of AI, escalating scalability, slashing infrastructure costs, and hitting the bull's eye with precision.
Short-Term Load Forecasting: A Case Study of the Spanish Electricity Market
Ever wondered about the dynamism behind electricity demand forecasting? How different forecasting models are when executed, like naive seasonal, regularized linear regression, gradient boosting, ARIMA (autoregressive integrated moving average) and Facebook Prophet? Dive into this session as we put these to the test, specifically for the Spanish grid.
When BI Meets AI - Opportunities, Possibilities, and Challenges (presentation in Polish)
How To Unlock the Potential of Multimodal Generative AI for the Enterprise: An In-Depth Exploration of Techniques, Frameworks, and Use Cases
In this presentation, we’ll explore:
- The vast potential of multimodal generative AI models capable of handling multiple modalities, such as text, speech, image, and video.
- Challenges and opportunities for enterprises, covering state-of-the-art methods and frameworks like AutoGen, Composable Diffusion (CoDi), and NExT-GPT.
- Valuable insights into real business use cases, including content creation and knowledge management.
Meta-Trends in Ethical and Legal AI and Their Practical Applications
With AI regulations in the forge across the globe, the race is on. Recent developments in generative AI have heightened concerns about the socio-technical implications of AI systems — a crucial consideration for applied AI.
In this presentation, we’ll discuss:
- The business perspective on legal and ethical AI.
- The balance of costs and benefits in responsible AI development and explainable AI.
- Microsoft's Responsible AI vs. impending European Union regulations.
This holistic review coupled with case studies provides insights into the workings and applications of responsible AI in large organizations.
Generative AI and Large Language Models: Where Is The Money?
This presentation dives into the latest advancements in large language models (LLMs) and generative AI, and their impact on commercial practices. Continuing the exploration of generative AI models in business applications that began at DSS ML ’23, this presentation provides an update on recent changes in the field.
Key focus areas include:
- Successful implementation strategies for enterprises.
- Effective business models utilizing LLMs and generative AI.
- Updated industry hype curve and industries likely to be profoundly affected by generative AI and LLMs.
AI Delivery Framework: Navigate Projects With Quality and Purpose
In this engaging talk, we will:
- Discuss challenges faced in establishing AI solution delivery frameworks.
- Explore the complexity of machine learning (ML) software, which goes beyond data management to incorporate business logic.
- The importance of interdisciplinary approaches and dynamic data governance for data science practices.
- Modifying tasks of the CRISP-DM to bridge gaps in ML operationalization using our proposed AI Delivery Framework.
- Showcase the AI Delivery Framework as a tool for AI project management and technical leadership.
Using BERT Attention for Data Augmentation: An Overview of Mixing Augmentation Methods for NLP
As natural language processing (NLP) encounters ever-increasing complexity, applying computer vision-inspired (CV) strategies has also become more recognized. How can the attention mechanisms of BERT be harnessed to expand the horizons of data augmentation in NLP?
Key insights from this presentation include:
- Understanding data augmentation for NLP through mixed training samples.
- Application of CV techniques to NLP, insights into BERT’s role, novel applications in data augmentation, and visual examples of augmented outcomes.
- Empirical evaluations of the proposed techniques' effectiveness.
Code Development Best Practices for Data Science Projects
How can embracing structured coding standards foster an environment of innovation without chaos, and set the stage for data science initiatives to really excel?
In this presentation, Konrad will share:
- Structured coding standards and best practices in data science that reinforce innovation and efficiency.
- Overlooked aspects of the software creation process and practical solutions that address issues such as code inconsistency and poor readability.
- How strategies like linting, rigorous testing, and continuous integration foster team cohesion.
- Success stories highlighting the transformational impact of standardized coding practices on team productivity.
How To Use BERT (Bidirectional Encoder Representations from Transformers) Attention for Data Augmentation: An Overview of Mixing Augmentation Methods for NLP
The poster provides a comprehensive analysis of different augmentation methods for natural language processing (NLP). It covers various topics:
- Augmentation for NLP
- Mixing for Computer Vision (CV) and NLP
- Use of BERT attention
- Mixing in BERT and Attention Mix, primed with an empirical evaluation on the SST (Stanford Sentiment Treebank) dataset.
My Model Is in Production: What's Next?
This presentation focuses on managing machine learning models in production and assessing new models after training. Key concepts include data drift, concept drift, and lack of generalization, which are demonstrated through a practical project use case.
This use case showcases the integration of Databricks with MLflow, introducing MLflow’s features such as model registry, models versioning, experiments, and deployments.
Image Generative Models for Business Applications: Hype or Reality?
Lingaro's NLP and generative AI experts, delve into the practical application of image generative models in business.
In this engaging talk, they’ll unpack:
- Two business applications of generative models: concept generation for ads and PowerPoint slide generation based on varied format inputs.
- Explanation on what made these innovative projects successful.
Why Modern Data Augmentation Is Unintuitive: An Overview of Mixing Augmentation Methods
Lingaro expert delivers a detailed technical presentation on modern data augmentation techniques, shedding light on:
- Notation, methods covered, and the universal mixing equation.
- Canonical method: Mixup.
- Canonical method: CutMix.
- Empirical evaluation and method similarities.
- Application to other tasks and modalities.
Why Modern Data Augmentation Is Unintuitive: An Overview of Mixing Augmentation Methods
Lingaro expert delivers a detailed technical presentation on modern data augmentation techniques, shedding light on:
- Notation, methods covered, and the universal mixing equation.
- Canonical method: Mixup.
- Canonical method: CutMix.
- Empirical evaluation and method similarities. Application to other tasks and modalities.
“How Many Models?”: Building a Foundation For Many-Model ML in Production
Take a closer look at a real-life case study on developing an efficient framework to put many-model ML solutions into action, utilizing the best practices in MLOps.
Building a Foundation for Many-Models ML in Production: An Implementation Using Azure Machine Learning
Join our expert to get valuable insights on how to leverage MLOps and effectively overcome these hurdles. Experience the journey of designing and building a pioneering framework that fuses MLOps best practices with Microsoft's Azure Machine Learning.
This session will help you understand how a versatile, user-friendly, and production-ready base for many-models machine learning can simplify the complex web of machine learning production.
From EEG Signals and Study of Particles to Sales Forecasting: Why You Need To Know Spectral Methods and Their Applications
AI, You Are 57 Years Old, But Are You Mature?
Is AI really maturing at 57? How can this question be addressed from both a global standpoint and an organizational perspective?
This thought-provoking discussion exposes the gap between AI's scientific achievements and practical application.
Automatic Image Generation for the Application of Generation of Advertisements Adopted to Defined Customer Segments
Journey alongside our Lingaro expert as he unveils the newest approaches in automated image generation, highlighting influential breakthroughs and discussing the implications on security and democratization.
Automatic Image Generation for the Application of Generation of Advertisements Adopted to Defined Customer Segments
This session presents an overview of:
- Automatic image generation for a defined group of clients.
The Fluence project, automatic image generation, innovative framework, and generation examples.
- Fine-tuning of generative model, in embedding space, and with unfrozen weights.
Why Modern Data Augmentation Is Unintuitive: An Overview of Mixing Augmentation Methods
Lingaro expert delivers a detailed technical presentation on modern data augmentation techniques, shedding light on:
- Notation, methods covered, and the universal mixing equation.
- Canonical method: Mixup.
- Canonical method: CutMix.
- Empirical evaluation and method similarities.
- Application to other tasks and modalities.
Social Benefits of Using ML and AI: How ML Could Help People With Disabilities?
This session provides an overview of:
- Practical examples of the voice assistance.
- Experiment on open-source library Merlin.
- Experiment on FastSpeech 2 implementation.
- Experiment on Tacotron 2 implementation.
- MUSHRA (Multiple Stimuli with Hidden Reference and Anchor) and comparing the results of the experiments.
AI Engineering: A New Direction in Development in Big Data, Cloud, and Data Science
AI engineering is a new development trend for software developers, cloud engineers, big data scientists, and data scientists.
Marek Psiuk, CTO of No Fluff Jobs, Błażej Chodarcewicz, director of Lingaro’s Data Science & AI Center of Excellence, and Tomasz Rostkowski, Lingaro AI engineering leader, discuss this and the AI engineer position it creates.
Meet Our Experts
Paweł Kryszkiewicz
Head of DS & AI Practice, Manager,
DS & AI Practice Team
In his role as Head of Data Science and Artificial Intelligence Practice at Lingaro, Paweł Kryszkiewicz draws on his experience as an AI product manager, AI delivery leader, and R&D consulting director. Paweł directs the development of new tools and solutions for a range of sectors, including finance, CPG, telco, and healthcare. A travel enthusiast, he loves how his work combines business and pleasure.
Błażej Chodarcewicz
DS and AI Competency Senior Director
Norbert Fijałek
AI Engineering Associate Manager,
DS & AI Competency Center
Norbert has a passion for AI and MLOps. With more than a decade of experience in implementing new technologies, he has developed innovative products and guided teams in delivering successful AI solutions across multiple industries. He enjoys sharing his knowledge, frequently speaking at various conferences on AI and related topics. Norbert is a lifelong learner, always eager to apply the latest technology trends to real-world problems.
Krystian Jabłoński
AI Advisor/Senior Consultant,
DS & AI Practice Team
Krystian has spent over seven years honing his skills in the data and analytics field, with a focus on creating and developing generative AI services. He has demonstrated leadership in managing product and product teams, as well as AI R&D activities, and is appreciated for his ability to translate business challenges into practical AI solutions.
Dominik Lewy
Principal Data Scientist,
DS & AI Competency Center
Dominik brings over nine years of hands-on experience in machine learning, deep learning, data exploration, and business analysis, primarily in the FMCG industry. He's a technical leader who develops project roadmaps and sets goals. At Warsaw University of Technology, Dominik is a Ph.D. candidate studying neural networks for image processing and connects the commercial and academic worlds. His primary research interest lies in digital image processing, aiming to facilitate the adoption of deep learning algorithms in business contexts where training data is limited or non-existent.
Taylor van Valkenburg
Data Science Expert/Senior Consultant,
DS & AI Practice Team
Taylor is a seasoned data scientist with a keen interest in the intersection between emerging technologies and public policy. Leveraging best practices in ML engineering and MLOps, Taylor adeptly uses a variety of machine learning and AI technologies to develop effective solutions.
Magdalena Kurlanc
Senior Consultant,
BI Tech Lead
With more than 11 years of experience in business intelligence, Magdalena is a Senior Consultant and a recognized Tableau expert. She has organized the past three editions of the Lingaro Data Visualization Challenge and is driven by data analytics, UX, AI, and more recently, SynBio.
Marcin Borucki
AI Manager, DS & AI Competency Center
As an AI manager in Data Science & AI Competence Center at Lingaro, Marcin Borucki leads a team of 20 AI specialists, delivering AI-powered solutions for Fortune 500 companies. With a 13-year experience stretching across diverse industries such as professional services, aviation, and banking, Marcin has evolved from a data scientist role to a management path. Additionally, he has made his mark as a seasoned trainer, introducing data science and data analytics to a diverse audience.
Wiktor Madejski
AI and ML Engineer, Principal Consultant, DS & AI Competency Center
Wiktor specializes in machine learning architectures and cloud optimizations. With years of experience in forecasting, computer vision, and natural language processing, he guides teams towards successful machine learning solution implementations. Being a mathematician, computer scientist, and economist, Wiktor encourages a visionary perspective, aiming to chart the optimal path for AI transformation.
Tomasz Rostkowski
AI Engineering Manager,
DS & AI Competency Center
With over 20 years of IT experience, primarily as a Data and Analytics Solutions Architect, Tomasz has spent the last seven years in leadership roles. His extensive experience includes building technical delivery teams, defining service portfolios, and delivering high-quality innovative projects. He also offers strategic technical initiatives, technology management, and technical organization setup advice.
Konrad Łyda
Al and ML Senior Engineer,
DS & Al Competency Center
An AI and machine learning expert based in Eastern Poland, Konrad mainly uses Python to create machine learning solutions and thoughtfully implement them. Through clever automation, be it small scale on his computer or large scale through end-to-end machine learning pipelines, Konrad helps data scientists focus on their learning goals.
Anna Tumanova
Data Scientist,
DS & AI Competency Center
Karol Piniarski
ML/AI Engineer/Lead Consultant,
DS & AI Competency Center
Adrian Cypcar
AI Associate Manager,
DS & AI Competency Center