Human-Centered AI for Disordered Speech Recognition - Katarzyna Foremniak

DataTalks.Club

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This Is Woman's Work with Nicole Kalil

1
How To Pitch Yourself (And Get A Yes) | 300 27:52

12 days ago27:52

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27:52

We made it— 300 episodes of This Is Woman’s Work ! And we’re marking this milestone by giving you something that could seriously change the game in your business or career: the skill of pitching yourself effectively. Whether you’re dreaming of being a podcast guest, landing a speaking gig, signing a client, or just asking for what you want with confidence—you’re already pitching yourself, every day. But are you doing it well? In this milestone episode, Nicole breaks down exactly how to pitch yourself to be a podcast guest … and actually hear “yes.” With hundreds of pitches landing in her inbox each month, she shares what makes a guest stand out (or get deleted), the biggest mistakes people make, and why podcast guesting is still one of the most powerful ways to grow your reach, authority, and influence. In This Episode, We Cover: ✅ Why we all need to pitch ourselves—and how to do it without feeling gross ✅ The step-by-step process for landing guest spots on podcasts (and more) ✅ A breakdown of the 3 podcast levels: Practice, Peer, and A-List—and how to approach each ✅ The must-haves of a successful podcast pitch (including real examples) ✅ How to craft a pitch that gets read, gets remembered, and gets results Whether you’re new to pitching or want to level up your game, this episode gives you the exact strategy Nicole and her team use to land guest spots on dozens of podcasts every year. Because your voice deserves to be heard. And the world needs what only you can bring. 🎁 Get the FREE Podcast Pitch Checklist + Additional Information on your Practice Group, Peer Group, and A-List Group Strategies: https://nicolekalil.com/podcast 📥 Download The Podcast Pitch Checklist Here Related Podcast Episodes: Shameless and Strategic: How to Brag About Yourself with Tiffany Houser | 298 How To Write & Publish A Book with Michelle Savage | 279 How To Land Your TED Talk and Skyrocket Your Personal Brand with Ashley Stahl | 250 Share the Love: If you found this episode insightful, please share it with a friend, tag us on social media, and leave a review on your favorite podcast platform! 🔗 Subscribe & Review: Apple Podcasts | Spotify | Amazon Music…

about a year ago 48:01

MP3•Laman utama episod

We talked about:

00:00 DataTalks.Club intro

08:06 Background and career journey of Katarzyna

09:06 Transition from linguistics to computational linguistics

11:38 Merging linguistics and computer science

15:25 Understanding phonetics and morpho-syntax

17:28 Exploring morpho-syntax and its relation to grammar

20:33 Connection between phonetics and speech disorders

24:41 Improvement of voice recognition systems

27:31 Overview of speech recognition technology

30:24 Challenges of ASR systems with atypical speech

30:53 Strategies for improving recognition of disordered speech

37:07 Data augmentation for training models

40:17 Transfer learning in speech recognition

42:18 Challenges of collecting data for various speech disorders

44:31 Stammering and its connection to fluency issues

45:16 Polish consonant combinations and pronunciation challenges

46:17 Use of Amazon Transcribe for generating podcast transcripts

47:28 Role of language models in speech recognition

49:19 Contextual understanding in speech recognition

51:27 How voice recognition systems analyze utterances

54:05 Personalization of ASR models for individuals

56:25 Language disorders and their impact on communication

58:00 Applications of speech recognition technology

1:00:34 Challenges of personalized and universal models

1:01:23 Voice recognition in automotive applications

1:03:27 Humorous voice recognition failures in cars

1:04:13 Closing remarks and reflections on the discussion

About the speaker:

Katarzyna is a computational linguist with over 10 years of experience in NLP and speech recognition. She has developed language models for automotive brands like Audi and Porsche and specializes in phonetics, morpho-syntax, and sentiment analysis.

Kasia also teaches at the University of Warsaw and is passionate about human-centered AI and multilingual NLP.

Join our slack: https://datatalks.club/slack.html

182 episod

#Tech #DataTalks.Club

Human-Centered AI for Disordered Speech Recognition - Katarzyna Foremniak

DataTalks.Club

29 subscribers

published about a year ago

Kongsi

MP3•Laman utama episod

We talked about:

00:00 DataTalks.Club intro

08:06 Background and career journey of Katarzyna

09:06 Transition from linguistics to computational linguistics

11:38 Merging linguistics and computer science

15:25 Understanding phonetics and morpho-syntax

17:28 Exploring morpho-syntax and its relation to grammar

20:33 Connection between phonetics and speech disorders

24:41 Improvement of voice recognition systems

27:31 Overview of speech recognition technology

30:24 Challenges of ASR systems with atypical speech

30:53 Strategies for improving recognition of disordered speech

37:07 Data augmentation for training models

40:17 Transfer learning in speech recognition

42:18 Challenges of collecting data for various speech disorders

44:31 Stammering and its connection to fluency issues

45:16 Polish consonant combinations and pronunciation challenges

46:17 Use of Amazon Transcribe for generating podcast transcripts

47:28 Role of language models in speech recognition

49:19 Contextual understanding in speech recognition

51:27 How voice recognition systems analyze utterances

54:05 Personalization of ASR models for individuals

56:25 Language disorders and their impact on communication

58:00 Applications of speech recognition technology

1:00:34 Challenges of personalized and universal models

1:01:23 Voice recognition in automotive applications

1:03:27 Humorous voice recognition failures in cars

1:04:13 Closing remarks and reflections on the discussion

About the speaker:

Kasia also teaches at the University of Warsaw and is passionate about human-centered AI and multilingual NLP.

Join our slack: https://datatalks.club/slack.html

182 episod

#Tech #DataTalks.Club

All episodes

DataTalks.Club

1
From Supply Chain Management to Digital Warehousing and FinOps - Eddy Zulkifly 52:08

23 days ago52:08

52:08

In this podcast episode, we talked with Eddy Zulkifly about From Supply Chain Management to Digital Warehousing and FinOps About the Speaker: Eddy Zulkifly is a Staff Data Engineer at Kinaxis, building robust data platforms across Google Cloud, Azure, and AWS. With a decade of experience in data, he actively shares his expertise as a Mentor on ADPList and Teaching Assistant at Uplimit. Previously, he was a Senior Data Engineer at Home Depot, specializing in e-commerce and supply chain analytics. Currently pursuing a Master’s in Analytics at the Georgia Institute of Technology, Eddy is also passionate about open-source data projects and enjoys watching/exploring the analytics behind the Fantasy Premier League. In this episode, we dive into the world of data engineering and FinOps with Eddy Zulkifly, Staff Data Engineer at Kinaxis. Eddy shares his unconventional career journey—from optimizing physical warehouses with Excel to building digital data platforms in the cloud. 🕒 TIMECODES 0:00 Eddy’s career journey: From supply chain to data engineering 8:18 Tools & learning: Excel, Docker, and transitioning to data engineering 21:57 Physical vs. digital warehousing: Analogies and key differences 31:40 Introduction to FinOps: Cloud cost optimization and vendor negotiations 40:18 Resources for FinOps: Certifications and the FinOps Foundation 45:12 Standardizing cloud cost reporting across AWS/GCP/Azure 50:04 Eddy’s master’s degree and closing thoughts 🔗 CONNECT WITH EDDY Twitter - https://x.com/eddarief Linkedin - https://www.linkedin.com/in/eddyzulkifly/ Github: https://github.com/eyzyly/eyzyly ADPList: https://adplist.org/mentors/eddy-zulkifly 🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ Check other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/…

DataTalks.Club

1
Data Intensive AI - Bartosz Mikulski 54:54

5 weeks ago54:54

54:54

In this podcast episode, we talked with Bartosz Mikulski about Data Intensive AI. About the Speaker: Bartosz is an AI and data engineer. He specializes in moving AI projects from the good-enough-for-a-demo phase to production by building a testing infrastructure and fixing the issues detected by tests. On top of that, he teaches programmers and non-programmers how to use AI. He contributed one chapter to the book 97 Things Every Data Engineer Should Know, and he was a speaker at several conferences, including Data Natives, Berlin Buzzwords, and Global AI Developer Days. In this episode, we discuss Bartosz’s career journey, the importance of testing in data pipelines, and how AI tools like ChatGPT and Cursor are transforming development workflows. From prompt engineering to building Chrome extensions with AI, we dive into practical use cases, tools, and insights for anyone working in data-intensive AI projects. Whether you’re a data engineer, AI enthusiast, or just curious about the future of AI in tech, this episode offers valuable takeaways and real-world experiences. 0:00 Introduction to Bartosz and his background 4:00 Bartosz’s career journey from Java development to AI engineering 9:05 The importance of testing in data engineering 11:19 How to create tests for data pipelines 13:14 Tools and approaches for testing data pipelines 17:10 Choosing Spark for data engineering projects 19:05 The connection between data engineering and AI tools 21:39 Use cases of AI in data engineering and MLOps 25:13 Prompt engineering techniques and best practices 31:45 Prompt compression and caching in AI models 33:35 Thoughts on DeepSeek and open-source AI models 35:54 Using AI for lead classification and LinkedIn automation 41:04 Building Chrome extensions with AI integration 43:51 Comparing Cursor and GitHub Copilot for coding 47:11 Using ChatGPT and Perplexity for AI-assisted tasks 52:09 Hosting static websites and using AI for development 54:27 How blogging helps attract clients and share knowledge 58:15 Using AI to assist with writing and content creation 🔗 CONNECT WITH Bartosz LinkedIn: https://www.linkedin.com/in/mikulskibartosz/ Github: https://github.com/mikulskibartosz Website: https://mikulskibartosz.name/blog/ 🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ Check other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/…

DataTalks.Club

1
MLOps in Corporations and Startups - Nemanja Radojkovic 58:03

6 weeks ago58:03

58:03

In this podcast episode, we talked with Nemanja Radojkovic about MLOps in Corporations and Startups. About the Speaker: Nemanja Radojkovic is Senior Machine Learning Engineer at Euroclear. In this event,we’re diving into the world of MLOps, comparing life in startups versus big corporations. Joining us again is Nemanja, a seasoned machine learning engineer with experience spanning Fortune 500 companies and agile startups. We explore the challenges of scaling MLOps on a shoestring budget, the trade-offs between corporate stability and startup agility, and practical advice for engineers deciding between these two career paths. Whether you’re navigating legacy frameworks or experimenting with cutting-edge tools. 1:00 MLOps in corporations versus startups 6:03 The agility and pace of startups 7:54 MLOps on a shoestring budget 12:54 Cloud solutions for startups 15:06 Challenges of cloud complexity versus on-premise 19:19 Selecting tools and avoiding vendor lock-in 22:22 Choosing between a startup and a corporation 27:30 Flexibility and risks in startups 29:37 Bureaucracy and processes in corporations 33:17 The role of frameworks in corporations 34:32 Advantages of large teams in corporations 40:01 Challenges of technical debt in startups 43:12 Career advice for junior data scientists 44:10 Tools and frameworks for MLOps projects 49:00 Balancing new and old technologies in skill development 55:43 Data engineering challenges and reliability in LLMs 57:09 On-premise vs. cloud solutions in data-sensitive industries 59:29 Alternatives like Dask for distributed systems 🔗 CONNECT WITH NEMANJA LinkedIn - / radojkovic Github - https://github.com/baskervilski 🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/... Check other upcoming events - https://lu.ma/dtc-events LinkedIn - / datatalks-club Twitter - / datatalksclub Website - https://datatalks.club/…

DataTalks.Club

1
Trends in Data Engineering – Adrian Brudaru 56:59

7 weeks ago56:59

56:59

In this podcast episode, we talked with Adrian Brudaru about the past, present and future of data engineering. About the speaker: Adrian Brudaru studied economics in Romania but soon got bored with how creative the industry was, and chose to go instead for the more factual side. He ended up in Berlin at the age of 25 and started a role as a business analyst. At the age of 30, he had enough of startups and decided to join a corporation, but quickly found out that it did not provide the challenge he wanted. As going back to startups was not a desirable option either, he decided to postpone his decision by taking freelance work and has never looked back since. Five years later, he co-founded a company in the data space to try new things. This company is also looking to release open source tools to help democratize data engineering. 0:00 Introduction to DataTalks.Club 1:05 Discussing trends in data engineering with Adrian 2:03 Adrian's background and journey into data engineering 5:04 Growth and updates on Adrian's company, DLT Hub 9:05 Challenges and specialization in data engineering today 13:00 Opportunities for data engineers entering the field 15:00 The "Modern Data Stack" and its evolution 17:25 Emerging trends: AI integration and Iceberg technology 27:40 DuckDB and the emergence of portable, cost-effective data stacks 32:14 The rise and impact of dbt in data engineering 34:08 Alternatives to dbt: SQLMesh and others 35:25 Workflow orchestration tools: Airflow, Dagster, Prefect, and GitHub Actions 37:20 Audience questions: Career focus in data roles and AI engineering overlaps 39:00 The role of semantics in data and AI workflows 41:11 Focusing on learning concepts over tools when entering the field 45:15 Transitioning from backend to data engineering: challenges and opportunities 47:48 Current state of the data engineering job market in Europe and beyond 49:05 Introduction to Apache Iceberg, Delta, and Hudi file formats 50:40 Suitability of these formats for batch and streaming workloads 52:29 Tools for streaming: Kafka, SQS, and related trends 58:07 Building AI agents and enabling intelligent data applications 59:09 Closing discussion on the place of tools like DBT in the ecosystem 🔗 CONNECT WITH ADRIAN BRUDARU Linkedin - / data-team Website - https://adrian.brudaru.com/ 🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/... Check other upcoming events - https://lu.ma/dtc-events LinkedIn - /datatalks-club Twitter - /datatalksclub Website - https://datatalks.club/…

DataTalks.Club

1
Competitive Machine Leaning And Teaching – Alexander Guschin 53:27

10 weeks ago53:27

53:27

In this podcast episode, we talked with Alexander Guschin about launching a career off Kaggle. About the Speaker: Alexander Guschin is a Machine Learning Engineer with 10+ years of experience, a Kaggle Grandmaster ranked 5th globally, and a teacher to 100K+ students. He leads DS and SE teams and contributes to open-source ML tools. 0:00 Starting with Machine Learning: Challenges and Early Steps 13:05 Community and Learning Through Kaggle Sessions 17:10 Broadening Skills Through Kaggle Participation 18:54 Early Competitions and Lessons Learned 21:10 Transitioning to Simpler Solutions Over Time 23:51 Benefits of Kaggle for Starting a Career in Machine Learning 29:08 Teamwork vs. Solo Participation in Competitions 31:14 Schoolchildren in AI Competitions 42:33 Transition to Industry and MLOps 50:13 Encouraging teamwork in student projects 50:48 Designing competitive machine learning tasks 52:22 Leaderboard types for tracking performance 53:44 Managing small-scale university classes 54:17 Experience with Coursera and online teaching 59:40 Convincing managers about Kaggle's value 61:38 Secrets of Kaggle competition success 63:11 Generative AI's impact on competitive ML 65:13 Evolution of automated ML solutions 66:22 Reflecting on competitive data science experience 🔗 CONNECT WITH ALEXANDER GUSCHINLinkedin - https://www.linkedin.com/in/1aguschin/Website - https://www.aguschin.com/ 🔗 CONNECT WITH DataTalksClub Join DataTalks.Club: ⁠⁠⁠⁠https://datatalks.club/slack.html⁠⁠⁠⁠ Our events: ⁠⁠⁠⁠https://datatalks.club/events.html⁠⁠⁠⁠ Datalike Substack - ⁠⁠⁠⁠https://datalike.substack.com/⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠ / datatalks-club ⁠…

DataTalks.Club

1
Redefining AI Infrastructure: Open-Source, Chips, and the Future Beyond Kubernetes – Andrey Cheptsov 56:55

12 weeks ago56:55

56:55

In this podcast episode, we talked with Andrey Cheptsov about The future of AI infrastructure. About the Speaker: Andrey Cheptsov is the founder and CEO of dstack, an open-source alternative to Kubernetes and Slurm, built to simplify the orchestration of AI infrastructure. Before dstack, Andrey worked at JetBrains for over a decade helping different teams make the best developer tools. During the event, the guest, Andrey Cheptsov, founder and CEO of dstack, discussed the complexities of AI infrastructure. We explore topics like the challenges of using Kubernetes for AI workloads, the need to rethink container orchestration, and the future of hybrid and cloud-only infrastructures. Andrey also shares insights into the role of on-premise and bare-metal solutions, edge computing, and federated learning. 00:00 Andrey's Career Journey: From JetBrains to DStack 5:00 The Motivation Behind DStack 7:00 Challenges in Machine Learning Infrastructure 10:00 Transitioning from Cloud to On-Prem Solutions 14:30 Reflections on OpenAI's Evolution 17:30 Open Source vs Proprietary Models: A Balanced Perspective 21:01 Monolithic vs. Decentralized AI businesses 22:05 The role of privacy and control in AI for industries like banking and healthcare 30:00 Challenges in training large AI models: GPUs and distributed systems 37:03 DeepSpeed's efficient training approach vs. brute force methods 39:00 Challenges for small and medium businesses: hosting and fine-tuning models 47:01 Managing Kubernetes challenges for AI teams 52:00 Hybrid vs. cloud-only infrastructure 56:03 On-premise vs. bare-metal solutions 58:05 Exploring edge computing and its challenges 🔗 CONNECT WITH ANDREY CHEPTSOV Twitter - / andrey_cheptsov Linkedin - / andrey-cheptsov GitHub - https://github.com/dstackai/dstack/ Website - https://dstack.ai/ 🔗 CONNECT WITH DataTalksClub Join DataTalks.Club: ⁠⁠⁠https://datatalks.club/slack.html⁠⁠⁠ Our events: ⁠⁠⁠https://datatalks.club/events.html⁠⁠⁠ Datalike Substack - ⁠⁠⁠https://datalike.substack.com/⁠⁠⁠ LinkedIn: ⁠⁠⁠ / datatalks-club ⁠…

DataTalks.Club

1
Linguistics and Fairness - Tamara Atanasoska 53:11

14 weeks ago53:11

53:11

In this podcast episode, we talked with Tamara Atanasoska about building fair AI systems. About the Speaker:Tamara works on ML explainability, interpretability and fairness as Open Source Software Engineer at probable. She is a maintainer of fairlearn, contributor to scikit-learn and skops. Tamara has both computer science/ software engineering and a computational linguistics(NLP) background.During the event, the guest discussed their career journey from software engineering to open-source contributions, focusing on explainability in AI through Scikit-learn and Fairlearn. They explored fairness in AI, including challenges in credit loans, hiring, and decision-making, and emphasized the importance of tools, human judgment, and collaboration. The guest also shared their involvement with PyLadies and encouraged contributions to Fairlearn. 00:00 Introduction to the event and the community 01:51 Topic introduction: Linguistic fairness and socio-technical perspectives in AI 02:37 Guest introduction: Tamara’s background and career 03:18 Tamara’s career journey: Software engineering, music tech, and computational linguistics 09:53 Tamara’s background in language and computer science 14:52 Exploring fairness in AI and its impact on society 21:20 Fairness in AI models26:21 Automating fairness analysis in models 32:32 Balancing technical and domain expertise in decision-making 37:13 The role of humans in the loop for fairness 40:02 Joining Probable and working on open-source projects 46:20 Scopes library and its integration with Hugging Face 50:48 PyLadies and community involvement 55:41 The ethos of Scikit-learn and Fairlearn 🔗 CONNECT WITH TAMARA ATANASOSKA Linkedin - https://www.linkedin.com/in/tamaraatanasoska GitHub- https://github.com/TamaraAtanasoska 🔗 CONNECT WITH DataTalksClub Join DataTalks.Club: ⁠⁠https://datatalks.club/slack.html⁠⁠ Our events: ⁠⁠https://datatalks.club/events.html⁠⁠ Datalike Substack - ⁠⁠https://datalike.substack.com/⁠⁠ LinkedIn: ⁠⁠ / datatalks-club…

DataTalks.Club

1
Career choices, transitions and promotions in and out of tech - Agita Jaunzeme 55:21

15 weeks ago55:21

55:21

In this podcast episode, we talked with Agita Jaunzeme about Career choices, transitions and promotions in and out of tech. About the Speaker: Agita has designed a career spanning DevOps/DataOps engineering, management, community building, education, and facilitation. She has worked on projects across corporate, startup, open source, and non-governmental sectors. Following her passion, she founded an NGO focusing on the inclusion of expats and locals in Porto. Embodying the values of innovation, automation, and continuous learning, Agita provides practical insights on promotions, career pivots, and aligning work with passion and purpose. During this event, discussed their career journey, starting with their transition from art school to programming and later into DevOps, eventually taking on leadership roles. They explored the challenges of burnout and the importance of volunteering, founding an NGO to support inclusion, gender equality, and sustainability. The conversation also covered key topics like mentorship, the differences between data engineering and data science, and the dynamics of managing volunteers versus employees. Additionally, the guest shared insights on community management, developer relations, and the importance of product vision and team collaboration. 0:00 Introduction and Welcome 1:28 Guest Introduction: Agita’s Background and Career Highlights 3:05 Transition to Tech: From Art School to Programming 5:40 Exploring DevOps and Growing into Leadership Roles 7:24 Burnout, Volunteering, and Founding an NGO 11:00 Volunteering and Mentorship Initiatives 14:00 Discovering Programming Skills and Early Career Challenges 15:50 Automating Work Processes and Earning a Promotion 19:00 Transitioning from DevOps to Volunteering and Project Management 24:00 Managing Volunteers vs. Employees and Building Organizational Skills 31:07 Personality traits in engineering vs. data roles 33:14 Differences in focus between data engineers and data scientists 36:24 Transitioning from volunteering to corporate work 37:38 The role and responsibilities of a community manager 39:06 Community management vs. developer relations activities 41:01 Product vision and team collaboration 43:35 Starting an NGO and legal processes 46:13 NGO goals: inclusion, gender equality, and sustainability 49:02 Community meetups and activities 51:57 Living off-grid in a forest and sustainability 55:02 Unemployment party and brainstorming session 59:03 Unemployment party: the process and structure 🔗 CONNECT WITH AGITA JAUNZEME Linkedin - /agita 🔗 CONNECT WITH DataTalksClub Join DataTalks.Club: ⁠https://datatalks.club/slack.html⁠ Our events: ⁠https://datatalks.club/events.html⁠ Datalike Substack - ⁠https://datalike.substack.com/⁠ LinkedIn: ⁠ / datatalks-club…

DataTalks.Club

1
Career advice, learning, and featuring women in ML and AI - Isabella Bicalho 54:40

19 weeks ago54:40

54:40

In this podcast episode, we talked with Isabella Bicalho about Career advice, learning, and featuring women in ML and AI. About the Speaker: Isabella is a Machine Learning Engineer and Data Scientist with three years of hands-on AI development experience. She draws upon her early computational research expertise to develop ML solutions. While contributing to open-source projects, she runs a newsletter dedicated to showcasing women's accomplishments in data science. During this event, the guest discussed her transition into machine learning, her freelance work in AI, and the growing AI scene in France. She shared insights on freelancing versus full-time work, the value of open-source contributions, and developing both technical and soft skills. The conversation also covered career advice, mentorship, and her Substack series on women in data science, emphasizing leadership, motivation, and career opportunities in tech. 0:00 Introduction 1:23 Background of Isabella Bicalho 2:02 Transition to machine learning 4:03 Study and work experience 5:00 Living in France and language learning 6:03 Internship experience 8:45 Focus areas of Inria 9:37 AI development in France 10:37 Current freelance work 11:03 Freelancing in machine learning 13:31 Moving from research to freelancing 14:03 Freelance vs. full-time data science 17:00 Finding first freelance client 18:00 Involvement in open-source projects 20:17 Passion for open-source and teamwork 23:52 Starting new projects 25:03 Community project experience 26:02 Teaching and learning 29:04 Contributing to open-source projects 32:05 Open-source tools vs. projects 33:32 Importance of community-driven projects 34:03 Learning resources 36:07 Green space segmentation project 39:02 Developing technical and soft skills 40:31 Gaining insights from industry experts 41:15 Understanding data science roles 41:31 Project challenges and team dynamics 42:05 Turnover in open-source projects 43:05 Managing expectations in open-source work 44:50 Mentorship in projects 46:17 Role of AI tools in learning 47:59 Overcoming learning challenges 48:52 Discussion on substack 49:01 Interview series on women in data 50:15 Insights from women in data science 51:20 Impactful stories from substack 53:01 Leadership challenges in projects 54:19 Career advice and opportunities 56:07 Motivating others to step out of comfort zone 57:06 Contacting for substack story sharing 58:00 Closing remarks and connections 🔗 CONNECT WITH ISABELLA BICALHO Github: github https://github.com/bellabf LinkedIn: / isabella-frazeto 🔗 CONNECT WITH DataTalksClub Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html Datalike Substack - https://datalike.substack.com/ LinkedIn: / datatalks-club…

DataTalks.Club

1
AI in Industry: Trust, Return on Investment and Future - Maria Sukhareva 52:58

20 weeks ago52:58

52:58

Reflection on an Almost Two-Year Journey of Generative AI in Industry – Maria Sukhareva About the speaker: Maria Sukhareva is a principal key expert in Artificial Intelligence in Siemens with over 15 years of experience at the forefront of generative AI technologies. Known for her keen eye for technological innovation, Maria excels at transforming cutting-edge AI research into practical, value-driven tools that address real-world needs. Her approach is both hands-on and results-focused, with a commitment to creating scalable, long-term solutions that improve communication, streamline complex processes, and empower smarter decision-making. Maria's work reflects a balanced vision, where the power of innovation is met with ethical responsibility, ensuring that her AI projects deliver impactful and production-ready outcomes. We talked about: 00:00 DataTalks.Club intro 02:13 Career journey: From linguistics to AI 08:02 The Evolution of AI Expertise and its Future 13:10 AI vulnerabilities: Bypassing bot restrictions 17:00 Non-LLM classifiers as a more robust solution 22:56 Risks of chatbot deployment: Reputational and financial 27:13 The role of AI as a tool, not a replacement for human workers 31:41 The role of human translators in the age of AI 34:49 Evolution of English and its Germanic roots 38:44 Beowulf and Old English 39:43 Impact of the Norman occupation on English grammar 42:34 Identifying mushrooms with AI apps and safety precautions 45:08 Decoding ancient languages like Sumerian 49:43 The evolution of machine translation and multilingual models 53:01 Challenges with low-resource languages and inconsistent orthography 57:28 Transition from academia to industry in AI Join our Slack: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html…

DataTalks.Club

1
Large Hadron Collider and Mentorship – Anastasia Karavdina 54:13

22 weeks ago54:13

54:13

We talked about: 00:00 DataTalks.Club intro 00:00 Large Hadron Collider and Mentorship 02:35 Career overview and transition from physics to data science 07:02 Working at the Large Hadron Collider 09:19 How particles collide and the role of detectors 11:03 Data analysis challenges in particle physics and data science similarities 13:32 Team structure at the Large Hadron Collider 20:05 Explaining the connection between particle physics and data science 23:21 Software engineering practices in particle physics 26:11 Challenges during interviews for data science roles 29:30 Mentoring and offering advice to job seekers 40:03 The STAR method and its value in interviews 50:32 Paid vs unpaid mentorship and finding the right fit About the speaker: Anastasia is a particle physicist turned data scientist, with experience in large-scale experiments like those at the Large Hadron Collider. She also worked at Blue Yonder, scaling AI-driven solutions for global supply chain giants, and at Kaufland e-commerce, focusing on NLP and search. Anastasia is a mentor for Ml/AI, dedicated to helping her mentees achieve their goals. She is passionate about growing the next generation of data science elite in Germany: from Data Analysts up to ML Engineers. Join our Slack: https://datatalks .club/slack.html…

DataTalks.Club

1
MLOps as a Team - Raphaël Hoogvliets 55:36

24 weeks ago55:36

55:36

We talked about: 00:00 DataTalks.Club intro 02:34 Career journey and transition into MLOps 08:41 Dutch agriculture and its challenges 10:36 The concept of "technical debt" in MLOps 13:37 Trade-offs in MLOps: moving fast vs. doing things right 14:05 Building teams and the role of coordination in MLOps 16:58 Key roles in an MLOps team: evangelists and tech translators 23:01 Role of the MLOps team in an organization 25:19 How MLOps teams assist product teams 27 :56 Standardizing practices in MLOps 32:46 Getting feedback and creating buy-in from data scientists 36:55 The importance of addressing pain points in MLOps 39:06 Best practices and tools for standardizing MLOps processes 42:31 Value of data versioning and reproducibility 44:22 When to start thinking about data versioning 45:10 Importance of data science experience for MLOps 46:06 Skill mix needed in MLOps teams 47:33 Building a diverse MLOps team 48:18 Best practices for implementing MLOps in new teams 49:52 Starting with CI/CD in MLOps 51:21 Key components for a complete MLOps setup 53:08 Role of package registries in MLOps 54:12 Using Docker vs. packages in MLOps 57:56 Examples of MLOps success and failure stories 1:00:54 What MLOps is in simple terms 1:01:58 The complexity of achieving easy deployment, monitoring, and maintenance Join our Slack: https://datatalks .club/slack.html…

DataTalks.Club

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Using Data to Create Liveable Cities - Rachel Lim 45:35

25 weeks ago45:35

45:35

We talked about: 00:00 DataTalks.Club intro 01:56 Using data to create livable cities 02:52 Rachel's career journey: from geography to urban data science 04:20 What does a transport scientist do? 05:34 Short-term and long-term transportation planning 06:14 Data sources for transportation planning in Singapore 08:38 Rachel's motivation for combining geography and data science 10:19 Urban design and its connection to geography 13:12 Defining a livable city 15:30 Livability of Singapore and urban planning 18:24 Role of data science in urban and transportation planning 20:31 Predicting travel patterns for future transportation needs 22:02 Data collection and processing in transportation systems 24:02 Use of real-time data for traffic management 27:06 Incorporating generative AI into data engineering 30:09 Data analysis for transportation policies 33:19 Technologies used in text-to-SQL projects 36:12 Handling large datasets and transportation data in Singapore 42:17 Generative AI applications beyond text-to-SQL 45:26 Publishing public data and maintaining privacy 45:52 Recommended datasets and projects for data engineering beginners 49:16 Recommended resources for learning urban data science About the speaker: Rachel is an urban data scientist dedicated to creating liveable cities through the innovative use of data. With a background in geography, and a masters in urban data science, she blends qualitative and quantitative analysis to tackle urban challenges. Her aim is to integrate data driven techniques with urban design to foster sustainable and equitable urban environments. Links: - https://datamall.lta.gov.sg/content/datamall/en/dynamic-data.html 00:00 DataTalks.Club intro 01:56 Using data to create livable cities 02:52 Rachel's career journey: from geography to urban data science 04:20 What does a transport scientist do? 05:34 Short-term and long-term transportation planning 06:14 Data sources for transportation planning in Singapore 08:38 Rachel's motivation for combining geography and data science 10:19 Urban design and its connection to geography 13:12 Defining a livable city 15:30 Livability of Singapore and urban planning 18:24 Role of data science in urban and transportation planning 20:31 Predicting travel patterns for future transportation needs 22:02 Data collection and processing in transportation systems 24:02 Use of real-time data for traffic management 27:06 Incorporating generative AI into data engineering 30:09 Data analysis for transportation policies 33:19 Technologies used in text-to-SQL projects 36:12 Handling large datasets and transportation data in Singapore 42:17 Generative AI applications beyond text-to-SQL 45:26 Publishing public data and maintaining privacy 45:52 Recommended datasets and projects for data engineering beginners 49:16 Recommended resources for learning urban data science Join our slack: https: //datatalks.club/slack.html…

DataTalks.Club

1
DataTalks.Club 4th Anniversary AMA Podcast – Alexey Grigorev and Johanna Bayer 53:41

26 weeks ago53:41

53:41

We talked about: 00:00 DataTalks.Club intro 00:00 DataTalks.Club anniversary "Ask Me Anything" event with Alexey Grigorev 02:29 The founding of DataTalks .Club 03:52 Alexey's transition from Java work to DataTalks.Club 04:58 Growth and success of DataTalks.Club courses 12:04 Motivation behind creating a free-to-learn community 24:03 Staying updated in data science through pet projects 26 :37 Hosting a second podcast and maintaining programming skills 28:56 Skepticism about LLMs and their relevance 31:53 Transitioning to DataTalks.Club and personal reflections 33:32 Memorable moments and the first event's success 36:19 Community building during the pandemic 38:31 AI's impact on data analysts and future roles 42:24 Discussion on AI in healthcare 44:37 Age and reflections on personal milestones 47:54 Building communities and personal connections 49:34 Future goals for the community and courses 51:18 Community involvement and engagement strategies 53:46 Ideas for competitions and hackathons 54:20 Inviting guests to the podcast 55:29 Course updates and future workshops 56:27 Podcast preparation and research process 58:30 Career opportunities in data science and transitioning fields 1:01 :10 Book recommendations and personal reading experiences About the speaker: Alexey Grigorev is the founder of DataTalks.Club. Join our slack: https://datatalks.club/slack.html…

DataTalks.Club

1
Human-Centered AI for Disordered Speech Recognition - Katarzyna Foremniak 48:01

29 weeks ago48:01

48:01

We talked about: 00:00 DataTalks.Club intro 08:06 Background and career journey of Katarzyna 09:06 Transition from linguistics to computational linguistics 11:38 Merging linguistics and computer science 15:25 Understanding phonetics and morpho-syntax 17:28 Exploring morpho-syntax and its relation to grammar 20:33 Connection between phonetics and speech disorders 24:41 Improvement of voice recognition systems 27:31 Overview of speech recognition technology 30:24 Challenges of ASR systems with atypical speech 30:53 Strategies for improving recognition of disordered speech 37:07 Data augmentation for training models 40:17 Transfer learning in speech recognition 42:18 Challenges of collecting data for various speech disorders 44:31 Stammering and its connection to fluency issues 45:16 Polish consonant combinations and pronunciation challenges 46:17 Use of Amazon Transcribe for generating podcast transcripts 47:28 Role of language models in speech recognition 49:19 Contextual understanding in speech recognition 51:27 How voice recognition systems analyze utterances 54:05 Personalization of ASR models for individuals 56:25 Language disorders and their impact on communication 58:00 Applications of speech recognition technology 1:00:34 Challenges of personalized and universal models 1:01:23 Voice recognition in automotive applications 1:03:27 Humorous voice recognition failures in cars 1:04:13 Closing remarks and reflections on the discussion About the speaker: Katarzyna is a computational linguist with over 10 years of experience in NLP and speech recognition. She has developed language models for automotive brands like Audi and Porsche and specializes in phonetics, morpho-syntax, and sentiment analysis. Kasia also teaches at the University of Warsaw and is passionate about human-centered AI and multilingual NLP. Join our slack: https://datatalks.club/slack.html…

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Human-Centered AI for Disordered Speech Recognition - Katarzyna Foremniak