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Planning as the Core Challenge in Agentic AI: Solving it with Reinforcement Learning By Anthony Alcaraz

Key Principles for Delivering AI Outcomes in Large Enterprises By Walid Mehanna

Using Semantics to Find Trends in Global Quality Data By Rex VanHorn

How SMEs Can Navigate the Data & AI Landscape By Frank Stadler

Creating Value at Scale with AI in Pharmaceuticals By Tomislav Ilicic

Enhanced Large Language Models as Reasoning Engines By Anthony Alcaraz

From RAG to RAR By James Duez

Essential Soft Skills for Data Scientists By Sandro Saitta

Data Quality in Relation to Algorithmic Bias By Gareth Hagger-Johnson

Common-Sense Leadership By Claus Villumsen

Davos 2024: Rebuilding Trust, Reclaiming Relevance and the Catalytic Effect of AI By Zana Aston

The Imperative of AI Constitutionalism: Building an Ethical Framework for a Brave New World By Sahaj Vaidya

Challenges and Opportunities in Clinical Trial Registry Data By Philipp Diesinger

Why Generative AI Projects Fail By Colin Harman

Fortifying Digital Frontiers: Wise’s Innovative Approach to Fraud Protection By Iaroslav Polianskii and Luca Traverso

Industry Case Study: Merck By Walid Mehanna

Artificial Intelligence for Artificial Languages By Francesco Gadaleta

The 5 Use Cases for Enterprise LLMs By Colin Harman

Data Science City: Warsaw

Could Rust Be the Future of AI? By Francesco Gadaleta

Rise of the Data Generalist: Smaller Teams, Bigger Impact By Tarush Aggarwal

Ethical and Sustainable Data By Sarah-Jane Smyth & Jasmine Grimsley

AI at Continental By Filipa Castro

Time Series Forecasting with Deep Learning in PyTorch (LSTM-RNN) By Zain Baquar

Using Open Sourced LLMs in Language By Bartmoss St Clair

Transforming Freight Logistics with Advanced Machine Learning By Luís Moreira-Matias

A Journey from Data Analyst to Data Science Leader By Rebecca Vickery

Navigating the Future of Ads: Large Language Models and Their Impact on Programmatic StrategiesBy Javier Campos

Are Large Language Models the Ultimate Database? By Francesco Gadaleta

Measuring the Effect of Change on Physical Assets By Colin Parry

Leveraging Data for a Sustainable Competitive Advantage with GenAI By Michel de Ru

AI the World Can Trust By James Duez

Scalable Data Management in the Cloud By Jacques Conradie

With LLMs, Enterprise Data is Different By Colin Harman

The Path to Responsible AI By Julia Stoyanovich

The Biological Model By Pat McQuillan

The AI Revolution vs the Mobile Revolution By Rex Woodbury

The Dawn of Synthetic Data By Philipp M Diesinger

Data Science Platforms for Patient-Centred Drug Discovery By Benjamin Glicksberg

How Infusing Marketing with Data Science Can Lead to Better Marketing Decisions By Faisal Wasswa

Should You Industrialise your Data Science MVP? By Philipp M Diesinger

The Principles of an Effective Data-Driven Organisation By Pat McQuillan

Data Science City: London

ChatGPT By Francesco Gadaleta

What Is Causal Inference and Why Should Data Leaders and Data Scientists Pay Attention? By Graham Harrison

How I Became a Heroine in Tech – A Personal Reflection By Anna Litticks

Risk Mitigation and the Future of Large Language Models in the Enterprise Market By Damien Deighan

Start Up: Interview with Tanmaiyii Rao

Digital Twins: The First Fruit of the Metaverse, Powered by Generative AI By Aakash Shirodkar

Building a Feature Platform Guided by Your Ethos By Andreu Mora

How AI Is Driving the Eradication of Malaria By Arnon Houri-Yafin

Embracing Neurodiversity: My Journey with ADHD and How Generative AI Propelled My Career in Data Science By Elle Neal

Charting the Evolution of Talent in Data Science at the Event Horizon of AGI By Lin Wang

From Humans to Hybrids: Preparing Your Workforce to Thrive in the Future By Philipp M Diesinger

Rapid And Budget-Friendly LLM Training Using Lora: Francesco Gadaleta

Interactive Insights: When Generative AI Meets Analytics BY Martijn Bauters

LLMs, ELMs And The Semantic Layer By Tarush Aggarwal

Evolution of Data Platforms: Francesco Gadaleta

Start Up: Interview With Tamanna Haque – Getting Into Data Science

Data Platforms – How Can The Right Data Platforms Streamline Your Organisation? Tarush Aggarwal

Anna Litticks: Automisation Means Change

Amsterdam – Data Science City: 7 Things About Amsterdam You May Not Know

How To Scale Data Science Pilots To Industrialise Your Data: Philipp M Diesinger

Nicole Janeway Bills: Ten Books To Expand Your Worldview As A Data Professional

Should You Reassess Your Candidate Assessment In The Wake Of CHAT GPT3? By Damien Deighan

Heidi Hurst: A Guide To Satellite Imaging

Dr Anna Litticks: Things I Wish I knew Before Becoming a Data Scientist

Start Up – Ryan Kearns: How I Got Into Data Science

Detecting Financially – Vulnerable Customers At Scale: Rebecca Vickery

Data Science Can Be a Complex Place. Here We Try To Simplify It.

We Explore The Fascinating World of Reinforcement learning with One of The Experts in The Field: Francesco Gadaleta

Damien Deighan: Talks About The Current State Of Data Science Hiring

Tarush Aggarwal: Tells us How he Got Started in Data Science at Salesforce And Wework

Skanda Vivek: Senior Data Scientist From Onsolve Talks About How he Transitioned From Academia to The Data Science Industry

Data Science City: Our First City Spotlight Highlights a Metropolis Rapidly Emerging as a Dominant Force in Data Science And AI on a Global Scale – Frankfurt.

Tam Tran: Shares Insights on How to Fix Common Cross-Validation Pitfalls

Mikkel Dengsoe: A highly Respected and Experienced Data Leader, Sheds Light on The Complex Relationship Between Team Size And Data Complexity

Genevieve Hays: Tells Data Scientists – If You Want to be Valued Then You Need to Ask The Right Questions

Unlocking The Power of Neutral Networks in Machine Translation With Philipp Koehn

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