DataTales.AI

About DataTales.AI

🎙️ Berlin Founders Hub Podcast 🌍 Welcome to the go-to podcast for Berlin’s most ambitious founders and creators and those building from anywhere! Hosted by the Berlin Founders Hub, we dive deep into interesting conversations with entrepreneurs, investors, and innovators inside and outside Berlin. 🚀 Real stories, honest challenges, and lessons from the front lines of startups. Whether you're launching your first venture or scaling your next big idea, this podcast is your backstage pass to the minds shaping the future. 📍Brought to you by Berlin’s dynamic founder community – where cowor

Publisher

Serop Baghdadlian

Category

business

Language

en

Explicit

No

First episode

26 Jul 2024

Latest episode

14 Mar 2025

Latest episodes

19 episodes in the feed.

1. 

   14 Mar 2025

   #19 Paul Iusztin: The Truth About LLMOps and Building Real AI Systems

   Summary This conversation explores the rapid evolution of AI, the complexities of AI roles, the importance of MLOps in deployment, and the challenges faced in implementing AI projects. The speakers share their personal journeys in AI engineering, discuss the balance between custom models and APIs, and emphasize the need for effective data retrieval methods in AI applications. In this conversation, the speakers delve into the complexities of AI, particularly in the context of MLOps and generative AI. They discuss the challenges of ambiguity in AI queries, the evolution of best practices in MLOps, and the importance of evaluation in AI models. The conversation also touches on the transition to content creation, the European AI landscape, and predictions for the future of AI, including advancements in robotics and genomics. Chapters 00:00 Coming Up 03:07 The Journey into AI Engineering 05:55 The Complexity of AI Roles 08:56 Custom Models vs. APIs in AI 11:52 The Role of MLOps in AI Deployment 15:00 Challenges in AI Project Implementation 18:11 Building Production-Ready AI Systems 20:49 Integrating Semantic Search in AI Applications 28:19 Navigating Ambiguity in AI Queries 30:37 The Evolution of MLOps in the Age of Gen AI 32:39 The Challenges of Evaluation in AI Models 35:41 Evaluating Non-Deterministic AI Systems 38:58 Transitioning to Full-Time Content Creation 41:35 The European AI Landscape and Data Security 44:14 Staying Updated in a Rapidly Evolving Field 48:15 Predictions for the Future of AI Takeaways AI is evolving rapidly, making it challenging to keep up. Europe has a strong advantage in data security for AI. The journey into AI can be overwhelming due to its complexity. Custom models may be necessary for specific tasks to reduce costs. MLOps is crucial for deploying AI systems effectively. Many AI projects fail due to unrealistic expectations and lack of resources. Building production-ready AI systems requires careful planning and organization. Semantic search can enhance data retrieval in AI applications. Understanding user intent is key to effective AI solutions. Collaboration and communication are essential in AI project success. Navigating ambiguity in AI queries is challenging but essential. MLOps principles are being overlooked in the rush of Gen AI. Evaluation of AI models is crucial and often neglected. Non-deterministic AI systems require careful evaluation. Transitioning to content creation can lead to a disconnect from industry practices. The European AI landscape is rich with talent and innovation. Data security is a competitive advantage for European companies. Staying updated in AI requires a strategic approach to information consumption. Robotics and genomics are poised to be the next big advancements in AI. Trusting oneself to learn and adapt is crucial in the evolving AI landscape. Contact linkedin.com/in/serop-baghdadlian linkedin.com/in/pauliusztin (http://linkedin.com/in/pauliusztin) #DataTales #DataScience #AIEngineering #MLOps #GenerativeAI

2. 

   28 Jan 2025

   #18 Ceyhun Derinbogaz: Building a 1M User AI SaaS [Success Story]

   Summary In this conversation, Serop Baghdadlian and Ceyhun (Jay) discuss the challenges and rewards of entrepreneurship, particularly in the AI sector. They explore the journey of TextCortex, its innovative solutions for knowledge management, and the importance of compliance in AI development. The discussion also touches on the future of workflow automation and the integration of AI features into existing systems. They also discuss the evolution of AI and automation, focusing on its applications in various industries, including legal and tax sectors. They explore the challenges of trust and accuracy in AI solutions, the impact of AI on software development, and the competitive landscape of AI startups. Jay shares his entrepreneurial journey, highlighting the importance of understanding industry-specific problems to create valuable solutions. The discussion also touches on the future of user interfaces and the potential for a universal API that could streamline interactions with technology. Chapters 00:00 Coming Up 00:44 Introduction to TextCortex and AI's Potential 03:01 The Evolution of TextCortex and Its Growth 06:00 Setting Up TextCortex for Enterprises 08:57 Integrations and Automation with TextCortex 12:08 Compliance and Challenges in AI Development 14:49 Future of AI and Workflow Automation 17:49 The Role of AI in Software Development 20:45 Navigating Competition in the AI Space 23:47 Predictions for the Future of AI Interfaces Takeaways TextCortex AI is a knowledge answer engine that simplifies data access. The evolution of TextCortex has been driven by organic growth and user feedback. Setting up TextCortex for enterprises involves understanding their systems and data sources. Integrations with platforms like make.com enhance automation capabilities. Compliance and security are significant challenges in AI development, especially in Europe. The future of AI includes more automation and workflow solutions for businesses. AI tools are becoming essential for software development, reducing the need for traditional coding. Competition in the AI space is intense, with many companies offering similar solutions. The interface of software applications is expected to evolve towards minimalism and voice interaction. AI needs to be equipped with the necessary tools to operate effectively in various environments. Contacts linkedin.com/in/ceyhunderinbogaz (http://linkedin.com/in/ceyhunderinbogaz) linkedin.com/in/serop-baghdadlian (http://linkedin.com/in/serop-baghdadlian) #DataTales #DataScience #WorkflowAutomation #AIEntrepreneurship #TechStartups

3. 

   18 Jan 2025

   #17 Maria Vechtomova: How to Correctly Navigate the AI Space

   Summary In this conversation, Serop Baghdadlian and Maria discuss the evolution of MLOps and AI technologies, and the importance of fundamentals in AI engineering. They explore the complexities of LLM Ops compared to traditional MLOps, the shift in evaluation standards for AI models, and the necessity of a data scientist's mindset when approaching AI projects. In this conversation, Serop Baghdadlian and Maria discuss the complexities and unpredictability of machine learning models, the evolution of MLOps, and the importance of focusing on fundamentals. They explore the challenges of reproducibility in machine learning environments, the journey of creating effective courses, and the significance of teaching and sharing knowledge in the tech community. Maria shares her experience in writing a book on MLOps with Databricks and emphasizes the need for simplicity in solutions. Chapters 00:00 Coming Up 05:10 Navigating Complexity in AI Systems 09:50 Evaluating AI Models: The Shift in Standards 15:03 The Role of Human Oversight in AI 19:49 Building Reliable AI Systems 24:54 Teaching and Sharing Knowledge in AI 29:58 The Future of AI and Continuous Learning Takeaways Fundamentals in AI and MLOps are crucial and don't change rapidly. Complex systems can lead to unreliability and financial loss. MLOps focuses on principles rather than just tools. Human oversight is essential in evaluating AI outputs. Simplicity should be prioritized in building AI systems. Teaching and sharing knowledge is vital for community growth. Continuous learning is necessary due to the fast-paced nature of AI. Evaluation standards for AI models have shifted towards gut feelings. Collaboration and mentorship are important in the AI field. Curiosity drives learning and understanding in AI. Contacts linkedin.com/in/maria-vechtomova (http://linkedin.com/in/maria-vechtomova) linkedin.com/in/serop-baghdadlian #DataTales #DataScience #MLOps #AIEngineering #TechPodcast

4. 

   14 Jan 2025

   #16 Eduardo Ordax: How AI is changing the Tech industry

   Summary In this engaging conversation, Serop Baghdadlian and Eduardo Ordax discuss Eduardo's journey in the tech industry, particularly focusing on AI and data. They explore the importance of humor in content creation on platforms like LinkedIn, the challenges businesses face when adopting AI, and the differences in AI adoption between the US and Europe. Eduardo shares insights on the necessity of a solid data strategy for successful AI implementation and the potential risks and regulations surrounding AI technology. In this conversation, Eduardo and Serop discuss the cultural differences in AI adoption between Europe and the U.S., the challenges of funding for startups, and the reality of the AI hype. They explore the top use cases for AI in business, the future of AI integration, and the importance of educating the workforce on how to effectively use AI technologies. Chapters 00:00 Coming Up 00:31 Introduction and Background 08:48 Navigating AI Adoption in Corporations 14:35 Transforming Business Models with AI 21:02 The Balance of Innovation and Regulation 24:47 Cultural Differences in Startup Ecosystems 27:05 The AI Landscape: Hype vs. Reality 30:22 Valuable Use Cases of AI in Business 35:41 The Evolution of AI in Various Industries 40:22 The Future of AI: Integration and Transformation 46:30 Educating the Workforce on AI Usage Takeaways Eduardo has a strong LinkedIn presence with 50K followers. Content creation should balance humor and seriousness. AI adoption varies significantly between the US and Europe. A solid data strategy is crucial for AI success. Businesses must adapt to AI or risk falling behind. Humor in content can lead to better engagement. AI is transforming traditional industries and business models. Regulation of AI technology is necessary but should not stifle innovation. The future of media consumption may change dramatically with AI. AI can enhance productivity but requires careful implementation. Cultural differences impact the pace of AI adoption. Funding for startups is more challenging in Europe. There is a significant hype around AI, but it's not a bubble. Top use cases for AI include software development and data querying. AI will be embedded in every product and service in the future. Workforce education on AI is crucial for future success. Companies need to learn how to effectively use AI tools. AI can significantly boost productivity in various sectors. Understanding how to prompt AI is essential for non-developers. The evolution of AI technologies is ongoing and rapid. Contacts: linkedin.com/in/serop-baghdadlian (http://linkedin.com/in/serop-baghdadlian)linkedin.com/in/eordax #DataTales #DataScience #AIRevolution #BusinessStrategy #StartupChallenges

5. 

   21 Dec 2024

   #15 Ben Feifke: The Ultimate Guide to Building a Data CONSULTANCY

   Summary In this conversation, Ben shares his journey from working in data science to becoming an entrepreneur and consultant. He discusses the importance of content creation, finding clients, and the challenges of pricing services. Ben emphasizes the value of analytics over data science in consulting and shares insights on automating his consultancy. He also reflects on personal experiences and the role of content in building trust and authority in the industry. In this conversation, Ben and Serop discuss the intricacies of transitioning from freelancing to agency work, emphasizing the importance of client relationships, offboarding strategies, and legal considerations. They share personal experiences regarding the risks of freelancing, the journey of entrepreneurship, and reflections on career choices. The dialogue highlights the challenges and rewards of building a business, navigating contracts, and the emotional aspects of making significant career decisions. Chapters 00:00 Coming Up00:32 Introduction to Ben's Journey in Data Science 02:53 Building a Consultancy: The Transition to Freelancing 06:02 Finding Clients: The Role of Upwork and Networking 08:49 Narrowing Focus: The Shift from Data Science to Analytics 12:07 The Value of Analytics: Meeting Client Needs 14:50 Pricing Strategies: Navigating Client Budgets 18:07 MLOps and Infrastructure: A New Service Offering 20:50 Content Creation: Building Trust and Authority 23:44 Beyond Content: Discovering Personalities 24:43 Navigating Client Relationships and Maintenance 27:21 Transitioning from Employment to Entrepreneurship 30:32 Understanding Contracts and Legalities 33:47 The Journey of Freelancing and Agency Building 37:27 Finding Focus in a Diverse Skill Set 40:13 The Leap into Entrepreneurship 44:12 Reflections on Career Transitions Takeaways Customers want results, not attempts. Freelancing can lead to long-term client relationships. Analytics is a valuable service for small businesses. Finding clients often starts with platforms like Upwork. Narrowing focus can lead to better service offerings. Pricing strategies are crucial for client acquisition. MLOps is a growing field with high demand. Content creation builds trust and authority. Diversification of services can be beneficial but focus is key. Networking and community engagement can lead to opportunities. Meeting people beyond their content reveals deeper personalities. Handling maintenance for clients requires clear communication and documentation. Transitioning from employment to entrepreneurship can be daunting yet rewarding. Understanding contracts is crucial for freelancers to protect themselves. Building an agency involves navigating various client relationships and expectations. Finding focus in a diverse skill set is a common challenge for entrepreneurs. The leap into entrepreneurship often comes from unexpected circumstances. Reflections on career transitions can provide valuable insights for others. Freelancing offers flexibility but also requires careful management of client expectations. The journey of entrepreneurship is filled with ups and downs, requiring resilience. Contacts: linkedin.com/in/serop-baghdadlian (http://linkedin.com/in/serop-baghdadlian)linkedin.com/in/benjamin-feifke #DataTales #DataScience #EntrepreneurshipJourney #FreelancingTips #DataConsultancy

6. 

   13 Dec 2024

   #14 Miguel Otero Pedrido: Deep Dive into AI Agents and how they are Transforming Software Design

   Summary In this engaging conversation, Serop Baghdadlian and Miguel Otero Pedrido explore the fascinating world of AI, focusing on the development of AI agents, their applications, and the overwhelming pace of technological advancements. Miguel shares insights from his YouTube channel, The Neural Maze, and discusses his work in recommender systems and the synergies with GenAI. They delve into the challenges of building a celebrity look-alike app and the importance of reflection patterns in AI agents, providing a comprehensive overview of the current landscape in AI technology. In this conversation, Serop Baghdadlian and Miguel Otero Pedrido delve into the evolving landscape of AI, particularly focusing on large language models (LLMs) and their evaluation, the use of tools in AI systems, and the significance of planning and multi-agent systems. They discuss the challenges of production environments, the impact of ChatGPT, and the shift back to specialized models. The conversation highlights the importance of human oversight in AI processes and the need for engineers to maintain control over AI systems to prevent potential failures. Chapters 00:44 Introduction to The Neural Maze 03:41 The Journey into AI Agents 06:44 Synergies Between GenAI and Recommender Systems 09:42 Building a Celebrity Look-Alike App 12:50 Navigating the Overwhelming AI Landscape 15:36 Understanding Agents and Their Components 18:47 Exploring Agentic Patterns 21:43 Reflection Pattern in AI Agents 25:12 Evaluating LLMs: The Role of Judges 28:18 Tool Use Patterns in AI 31:39 Planning and React Techniques in AI Agents 34:36 Multi-Agent Systems: Specialization vs. Complexity 38:42 The Shift Back to Specialized Models 41:24 The Impact of ChatGPT and GenAI 43:05 Challenges in Production Environments 47:35 Navigating the AI Engineering Landscape Takeaways The Neural Maze aims to simplify the overwhelming world of AI. AI agents are gaining popularity and interest among audiences. There are significant synergies between GenAI and recommender systems. Building applications like celebrity look-alike apps can be challenging yet rewarding. The tech stack for AI agents and recommender systems is often similar. Reflection patterns can enhance the output quality of AI-generated content. Understanding the components of agents is crucial for effective implementation. The rapid development of AI technologies can be overwhelming for professionals. Using tools effectively is key to the success of AI agents. The definition of AI and agents remains a complex and evolving topic. The evaluation of LLMs often involves using other LLMs as judges. Tool use patterns in AI can simplify complex tasks. Planning techniques like React help agents decide on actions. Multi-agent systems can be more effective than single intelligent agents. Specialized models are making a comeback in AI development. ChatGPT has significantly impacted public awareness of AI. Production environments pose unique challenges for AI systems. Human oversight is crucial in AI decision-making processes. AI engineering roles are evolving rapidly in the industry. Maintaining control over AI systems is essential to prevent failures. Contacts: linkedin.com/in/migueloteropedrido (http://linkedin.com/in/migueloteropedrido) linkedin.com/in/serop-baghdadlian #DataTales #DataScience #LLMApplications #HumanInAI #FutureOfTechnology

7. 

   6 Dec 2024

   #13 Jeremy Arancio: Building a Freelance Career in Machine Learning: A 3-year journey into AI and Independence

   Summary In this conversation, Jeremy shares his journey from being a mechanical engineer to becoming a successful freelancer in data science and machine learning. He discusses the challenges and rewards of freelancing, the importance of building a personal brand, and the realities of working with clients. The conversation also delves into the hype surrounding LLMs and AI, exploring their practical applications and the potential pitfalls of relying too heavily on these technologies. Jeremy emphasizes the need for a solid understanding of the problem at hand and the importance of finding the right solutions, whether through AI or traditional methods. Chapters 00:41 Introduction to Freelancing and Machine Learning 03:45 The Journey to Digital Nomadism 06:41 Transitioning from Engineering to Entrepreneurship 09:50 Freelancing vs. Traditional Employment 12:39 Building a Network and Finding Clients 15:48 The Role of Content Creation in Freelancing 18:39 Pricing Strategies and Client Expectations 21:33 Case Studies and Their Impact on Business 24:43 Navigating AI Expectations in Projects 27:45 The Importance of Personal Branding 33:33 The Journey of Content Creation 36:50 Freelancing: The Ups and Downs 39:45 Navigating the Challenges of LLMs 42:29 Real-World Applications of LLMs 46:37 The Hype and Reality of AI Solutions 49:30 Client Expectations vs. Reality 52:37 Building Solutions Beyond AI Hype 56:36 The Future of Open Source in AI Takeaways Freelancing offers flexibility and the opportunity to travel. Networking is crucial for finding clients and opportunities. Self-learning and online resources can lead to a successful career in data science. Building a personal brand can help attract clients and projects. Freelancers should be prepared for the ups and downs of client work. LLMs and AI are powerful tools, but they come with challenges. It's important to understand the problem before jumping to solutions. Open source solutions can be a viable alternative to expensive APIs. Content creation helps clarify ideas and build an online presence. The hype around AI may fade, but the need for practical solutions will remain. Links: linkedin.com/in/jeremy-arancio linkedin.com/in/serop-baghdadlian #DataTales #FreelancingJourney #MachineLearning #AIPracticality #DataScience

8. 

   29 Nov 2024

   #12 Itamar Golan: How Hackers Target LLMs: The Ultimate Security Guide

   Summary In this conversation, Itamar Golan, CEO of Prompt Security, discusses the evolving landscape of AI and cybersecurity, focusing on the security challenges posed by large language models (LLMs). He explains various attack vectors, including prompt injection and denial of wallets, and emphasizes the importance of integrating AI securely. The discussion also touches on the role of hallucinations in LLMs, the need for content moderation, and best practices for safeguarding AI applications. Golan highlights the dynamic nature of AI security and the necessity for continuous awareness and adaptation to new threats. Chapters 00:21  Cultural Origins and Personal Backgrounds 01:11  The Evolution of AI in Cybersecurity 03:53  Understanding LLM Security Threats 06:33  Prompt Injection and Its Implications 09:06  The Role of AI in Security 11:46  Hallucinations in LLMs: A Feature or Bug? 14:23  Denial of Wallets Attack Explained 16:54  Best Practices for LLM Integration 19:20  Toxicity and Content Moderation in AI 22:00  The Future of AI Security Takeaways AI is creating new threats that need addressing. Prompt injection is a significant vulnerability in LLMs. Hallucinations in LLMs are a feature, not a bug. Denial of Wallets is a new attack vector. Security measures must evolve with AI technology. Content moderation is essential for AI applications. Awareness of AI security risks is improving. Integrating LLMs requires careful configuration. Toxicity in AI responses varies by context. The future of AI will involve AI itself in security. #DataTales#DataScience #AIsecurity #CyberSecurity #LLMSecurity #AIethics #TechTrends

9. 

   22 Nov 2024

   #11 Shaw Talebi: 18 Months as a Data Entrepreneur: Lessons, Challenges, and Growth

   In this conversation, Shaw shares his journey from academia to entrepreneurship, detailing his experiences in data science, corporate work, and the challenges of transitioning to freelance and product development. He discusses the importance of community, mentorship, and the iterative process of building products, emphasizing the need for continuous learning and adaptation. Shaw also explores his vision for a venture studio aimed at supporting aspiring entrepreneurs in the data space. Chapters 00:00 Navigating the Podcasting Landscape 01:48 From Academia to Entrepreneurship 04:36 The Shift from Freelancing to Product Development 07:23 Finding the Right Audience for Educational Offers 10:18 Trial and Error in Entrepreneurship 13:06 Building Products for Personal Needs 15:51 Leveraging AI for Efficiency 18:38 Creating Solutions for Content Generation 21:34 Opportunities in Data Engineering and AI 24:10 The Importance of Infrastructure in Data Science 28:32 Navigating the MLOps Landscape 29:35 Building a Community for Data Entrepreneurs 31:16 The Importance of Mentorship in Entrepreneurship 32:66 Innovative Approaches to Finding Mentors 35:43 The Vision for Data Entrepreneurs 38:39 Funding and Growth Strategies for Startups 42:15 Learning Through Product Development 45:45 The Journey from Employment to Entrepreneurship 49:29 The Power of Community and Networking Takeaways Shaw's journey began with a master's in physics and an unexpected PhD. He transitioned from corporate work at Toyota to pursue entrepreneurship. Shaw emphasizes the importance of community and networking in entrepreneurship. He learned valuable lessons from trial and error in product development. Shaw's current focus is on creating educational offers and bootcamps. He believes in solving his own problems as a way to validate product ideas. Shaw highlights the significance of mentorship and learning from successful individuals. He aims to build a venture studio to support aspiring entrepreneurs. Shaw's experiences reflect the common challenges faced by many in the entrepreneurial journey. He encourages continuous learning and adaptation in the fast-paced tech landscape. #datatales #datascience #aisolutions #entrepreneurship #shawtalebi

10. 

    2 Nov 2024

    #10 Chinar Movsisyan: How to use implicit feedback analytics to optimize LLM apps

    Summary In this conversation, Chinar Movsisyan shares her journey in AI, particularly focusing on her work with Feedback Intelligence. She discusses the challenges of integrating AI in healthcare, the importance of closing the feedback loop in AI applications, and the shift towards using large language models (LLMs) in business. Chinar emphasizes the need for personalization in AI chatbots and the significance of implicit feedback over explicit feedback. She reflects on her motivations as a founder and the future of AI in solving real-world problems. Takeaways Feedback Intelligence aims to close the feedback loop in AI applications. Personalization in AI chatbots is crucial for user satisfaction. The shift towards LLMs is driven by the need for efficiency in businesses. Founders often face ups and downs in their journey, which is part of the process. AI should be used to solve real problems, not replace humans. The healthcare sector faces significant challenges in integrating AI due to regulations. Companies are increasingly adopting AI to improve operational efficiency. Building a startup requires passion and a willingness to solve problems. Chapters 00:00 Chinar Movsisyan's Journey in AI 04:32 Feedback Intelligence: The Problem and Solution 11:00 Closing the Feedback Loop in AI 15:16 Optimizing Information Retrieval in Organizations 19:20 The Importance of Implicit Feedback 22:55 The Evolution of Feedback Intelligence 26:34 The Shift Towards LLM Adoption 31:02 The Journey of a Founder 35:55 AI's Potential to Solve Real Problems

11. 

    2 Nov 2024

    #9 Daniel Engelhardt: Building a Company-Specific Retrieval Augmented Generation System (RAG)

    Summary In this conversation, Daniel Engelhardt talked about the development and implementation of a Gen.ai RAG system designed to assist employees in a company. He discusses the journey from ideation to product development, the technical architecture, the challenges faced, and the importance of prompt engineering. Daniel shares insights on evaluating language model responses, security measures taken to protect sensitive information, and strategies to mitigate hallucinations in AI outputs. The conversation also touches on the significance of data quality, measuring success through user engagement, and the cost considerations associated with deploying AI systems. Takeaways The Gen.ai RAG system aims to assist employees with company-specific tasks. Prompt engineering is crucial and surprisingly complex in AI development. Evaluating language model responses requires innovative approaches. Security measures are essential to protect sensitive company data. Mitigating hallucinations in AI outputs is a significant challenge. Data quality directly impacts the effectiveness of AI systems. User engagement is a key metric for measuring success. Cost management is important but secondary to time savings. Continuous feedback from users helps improve the AI system. The integration of AI can enhance productivity and employee satisfaction. Chapters 00:00 Introduction to Gen.ai and the Co-Pilot System 05:11 Development Journey: From Idea to Product 11:07 Technical Architecture and Scalability Challenges 17:04 Prompt Engineering: The Art of Asking Questions 22:28 Evaluating Language Model Responses 28:32 Security Measures in Internal Applications 33:54 Data Quality and Management Challenges 39:39 Measuring Success and User Engagement 44:59 Cost Considerations in AI Implementation --------------------🤗Connect With Us🤗----------------------- Connect with Serop Baghdadlian on LinkedIn: https://www.linkedin.com/in/serop-b-498332169/ Connect with Daniel Engelhardt on LinkedIn: https://www.linkedin.com/in/danielengelhardt-entwickler/ Keywords Gen.ai, AI applications, software development, prompt engineering, language models, data quality, user engagement, security measures, hallucinations, cost management

12. 

    22 Oct 2024

    #8 Siok Siok Tan: Building Sustainable AI

    Summary In this conversation, Serop Baghdadlian and Siok Siok Tan explore the evolving landscape of AI, addressing common misconceptions, the importance of the human element in AI development, and the ethical implications of AI technologies. They discuss the role of creativity in AI, the impact of social media, and the need for a more inclusive representation of global narratives. The conversation emphasizes the gap between knowledge and wisdom in the context of AI advancements and concludes with insights on building sustainable AI for humanity. Takeaways AI has become more accessible and less intimidating for the general public. The biggest misconception about AI is that it is magic and can do wonders without proper data. Human involvement is crucial in AI development to ensure ethical implications are considered. Creativity in AI should involve collaboration between humans and machines. AI can generate content, but it lacks the human touch that makes it relatable. Social media algorithms can create echo chambers and reinforce biases. There is a need for more diverse narratives in AI training data. The rapid advancement of AI technology outpaces society's ability to adapt. AI should be nurtured with wisdom rather than just managed as a static technology. Ethical considerations in AI development are essential for its impact on society. Chapters 00:00 Navigating the AI Landscape 01:56 Understanding AI Misconceptions 04:58 The Human Element in AI 07:54 AI's Role in Creativity 10:54 The Importance of Human-AI Collaboration 14:01 Cultural Perspectives on AI 16:56 The Impact of Social Media on AI 19:51 How does AI makes us more human 25:50 The Future of AI and Human Interaction 28:42 AI Bias Problem 32:21 Human Wisdom Trying to catch up to AI 36:55 AI in social media 43:37 Building sustainable AI 48:32 Final message to AI practitioners --------------------🤗Connect With Us🤗----------------------- Connect with Serop Baghdadlian on LinkedIn: https://www.linkedin.com/in/serop-b-498332169/ Connect with Siok Siok on LinkedIn: https://www.linkedin.com/in/sioksiok/

13. 

    15 Oct 2024

    #7 Monica Sirbu: Navigating Data Challenges in HR

    In this conversation, Monica shares her journey from HR to data analytics, discussing the growing importance of data in HR practices. She emphasizes the need for HR professionals to connect their data with business goals and the evolving role of data analytics in improving employee experiences, particularly during onboarding. The discussion also covers the challenges faced in HR data analysis, including data privacy concerns and the impact of AI on HR processes. Monica highlights the opportunities AI presents for automating repetitive tasks, allowing HR professionals to focus on strategic initiatives and employee engagement. keywords: HR, data analytics, career transition, employee experience, AI in HR, onboarding, data-driven decisions, business goals, employee engagement, HR technology. Make sure you join us on this journey and learn more about data science --------------------🤗Connect With Us🤗----------------------- Connect with Serop Baghdadlian on LinkedIn: https://www.linkedin.com/in/serop-b-498332169/ Connect with Monica Sirbu: on LinkedIn: https://www.linkedin.com/in/monica-sirbu-b2572914a/

14. 

    7 Sept 2024

    #6 Alexandra Harmanas: Building Intelligent Discount Generation System

    In this episode, we sit down with Alexandra Harmanas, Senior Data Scientist at Metro.Digital, to explore how AI and data science transform the retail space. We discussed a very cool project, how data science can be used for generating customer-oriented discounts for products that are about to expire to generate more sales and minimize loss. Alexandra talks about the challenges, and the ways to develop such a system so stay tuned in to gain valuable insights from the forefront of AI in retail. Make sure you join us on this journey and learn more about data science --------------------🤗Connect With Us🤗----------------------- Connect with Serop Baghdadlian on LinkedIn: https://www.linkedin.com/in/serop-b-498332169/ Connect with Alexandra Harmanas on LinkedIn: https://www.linkedin.com/in/alexandrapintican/

15. 

    26 Aug 2024

    #5 Dr. Patrick Bormann: How to build trust in Data Science Solution

    In this episode, we sit down with Dr. Patrick Bormann, Senior Data Scientist at Metro.Digital, to explore how AI and data science are transforming the retail space. Dr. Bormann talks about the smart delisting project he is developing and how to build a data science MVP that works!! After that, we dive into the challenges and potential dangers of deploying GenAI applications and how to mitigate these dangers. Tune in to gain valuable insights from the forefront of AI in retail. Make sure you join us on this journey and learn more about data science --------------------🤗Connect With Us🤗----------------------- Connect with Serop Baghdadlian on LinkedIn: https://www.linkedin.com/in/serop-b-498332169/ Connect with Patrick Bormann on LinkedIn: https://www.linkedin.com/in/dr-patrick-bormann-81915a1b1/

16. 

    16 Aug 2024

    #4: Dr. Robert Kuebler: AI for Intelligent Delisting & Recommendation Systems

    In this episode, we sit down with Dr. Robert Kuebler, Senior Data Scientist at Metro.Digital, to explore how AI and data science are transforming the retail space. Dr. Kuebler shares two real-world use cases where he leveraged advanced AI and data science techniques to optimize product assortments and develop powerful recommendation systems for Metro AG, a leading wholesaler. We dive into the details of how these recommendation systems work, the metrics used to measure their success, and the best practices for validating machine learning models through rigorous testing. Tune in to gain valuable insights from the forefront of AI in retail. Make sure you join us on this journey and learn more about data science --------------------🤗Connect With Us🤗----------------------- Connect with Serop Baghdadlian on LinkedIn: https://www.linkedin.com/in/serop-b-498332169/ Connect with Robert Kuebler on LinkedIn: https://www.linkedin.com/in/robert-kuebler/

17. 

    9 Aug 2024

    #3 - Selma Illig: The different data teams structures

    Selma Illig is a data engineer at Metro.Digital. During her master's thesis, she interviewed many data companies to answer the question, what is the optimal way to structure data teams in a company? Which one is better, a centralized or decentralized data team? In this podcast, we discuss the advantages and disadvantages of each team topology and the challenges that come up from each. We also shed light on the amazing engineering work that is required to deploy customer churn predictions directly to the users. Make sure you join us on this journey and learn more about data science --------------------🤗Connect With Us🤗----------------------- Connect with Serop Baghdadlian on LinkedIn: https://www.linkedin.com/in/serop-b-498332169/ Connect with Selma Illig on LinkedIn: https://www.linkedin.com/in/selmaillig/

18. 

    1 Aug 2024

    #2 - Felix Germaine: Customer Churn Modeling

    Felix Germaine is a data scientist at Metro.Digital. With a strong background in statistics, he implemented interesting machine-learning systems for predicting customers who are about to churn. He explains what goes into developing such systems and what are the general challenges that come with working with such big data. We talked about how such systems work under the hood, what are the challenges that he faced, and how to overcome these challenges. Make sure you join us on this journey and learn more about data science --------------------🤗Connect With Us🤗----------------------- Connect with Serop Baghdadlian on LinkedIn: https://www.linkedin.com/in/serop-b-498332169/ Connect with Felix Germaine on LinkedIn: https://www.linkedin.com/in/felix-germaine-74407ab2/

19. 

    26 Jul 2024

    #1 - Dora Petrella: Leading Data Science and Analytics Domain

    In this episode, Dora Petrella talks about her journey from being a data scientist to leading the data science and analytics domain at Metro.digital. We discussed the transition, the challenges and rewards of leading a diverse and remote team, and the strategic vision required to drive data science initiatives in a major company. Most importantly, we talked about what makes data science projects successful and how to do proper expectation management, especially with all the hype around GenAI that is going on. Make sure you join us on this journey and learn from one of the top leaders in the data science industry. --------------------🤗Connect With Us🤗----------------------- Connect with Serop Baghdadlian on LinkedIn: https://www.linkedin.com/in/serop-b-498332169/ Connect with Dora Petrelle on LinkedIn: https://www.linkedin.com/in/dora-petrella-968371a6/ Book Consultation Services: https://serop-ba.github.io/

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

YouTube views

10,781

Score snapshot 15 Sept 2025

Spotify

5.0 / 5

12 ratings

Podcast Authority Score: 59 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

68

Social presence

0

YouTube

76

Engagement

51

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Guest appearances

Does not typically book guests

Based on episode analysis; this does not confirm that the show is currently accepting guests.