Podd: DataTalks.Club

Linguistics and Fairness - Tamara Atanasoska

17 januari 2025 | 54 min

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. 0:00 Introduction to the event and the community 1:51 Topic introduction: Linguistic fairness and socio-technical perspectives in AI 2:37 Guest introduction: Tamara’s background and career 3:18 Tamara’s career journey: Software engineering, music tech, and computational linguistics 9:53 Tamara’s background in language and computer science 14:52 Exploring fairness in AI and its impact on society 21:20 Fairness in AI models 26:21 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

Career choices, transitions and promotions in and out of tech - Agita Jaunzeme

10 januari 2025 | 55 min

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

Career advice, learning, and featuring women in ML and AI - Isabella Bicalho

13 december 2024 | 55 min

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

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

6 december 2024 | 53 min

Large Hadron Collider and Mentorship – Anastasia Karavdina

22 november 2024 | 54 min

MLOps as a Team - Raphaël Hoogvliets

8 november 2024 | 56 min

Using Data to Create Liveable Cities - Rachel Lim

1 november 2024 | 46 min

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 4th Anniversary AMA Podcast – Alexey Grigorev and Johanna Bayer

26 oktober 2024 | 54 min

Human-Centered AI for Disordered Speech Recognition - Katarzyna Foremniak

10 oktober 2024 | 48 min

DataOps, Observability, and The Cure for Data Team Blues - Christopher Bergh

15 augusti 2024 | 54 min