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  

Agita Jaunzeme, a versatile professional with a background in DevOps/DataOps engineering, education, and community building, shares her unique career trajectory. She discusses her transition from art school to tech, tackling burnout, and the value of mentorship. Agita highlights her NGO aimed at promoting inclusivity and gender equality while exploring the differences between volunteer and employee management. The conversation delves into the significance of community in tech and the challenges faced by expatriates, combined with insights on fostering collaboration and innovative problem-solving in the industry.

In this engaging conversation, Isabella Bicalho, a Machine Learning Engineer and passionate advocate for women in data science, shares her journey from academia to freelancing in AI. She discusses the vibrant AI scene in France, her experiences with open-source contributions, and the significance of mentorship. Isabella reveals her insights on balancing technical and soft skills, the pros and cons of freelancing vs. full-time work, and highlights her mission to showcase women's achievements in the tech field through her newsletter.

Maria Sukhareva, a Principal key expert in AI at Siemens, shares her 15 years of experience in generative AI. She discusses the journey of AI in industry, highlighting vulnerabilities and the importance of human oversight in chatbot deployments. Sukhareva emphasizes AI as a tool to enhance human capabilities rather than replace them. She also reflects on the evolution of the English language and the challenges of decoding ancient languages, underscoring the need for caution in using AI for linguistic research.

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

Raphaël Hoogvliets, a leader in MLOps with a background in data science, discusses his transition from sustainable agriculture to tech. He highlights Dutch agricultural challenges and the importance of addressing technical debt in MLOps. The conversation emphasizes the delicate balance between speed and quality in team dynamics. Key roles like tech translators are explored, alongside best practices such as data versioning. Raphaël also shares success and failure stories, underscoring the complexity of deploying machine learning in real-world environments.

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

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

Katarzyna Foremniak, a seasoned computational linguist with over a decade of experience in NLP and speech recognition, shares her insights. She discusses the complexities of automatic speech recognition, particularly for disordered speech, and the challenges of articulation variability. With anecdotes about consonant clusters and amusing voice recognition mishaps in automotive systems, Kasia emphasizes the need for human-centered AI and personalized ASR models to enhance communication for diverse speech patterns.

Christopher Bergh, a DataOps and observability expert, dives into the evolution and challenges of DataOps, drawing insights from his experiences at NASA and MIT. He underlines the need for a cultural shift towards automation and effective methodologies to boost data team satisfaction. The discussion also delves into optimizing data systems, navigating Kubernetes, and the importance of focusing on code versioning over data versioning. Throughout, Bergh emphasizes how culture plays a pivotal role in fostering collaboration and retaining talent within data teams.