In this podcast episode, we talked with Eddy Zulkifly about From Supply Chain Management to Digital Warehousing and FinOpsAbout 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.🕒 TIMECODES0:00 Eddy’s career journey: From supply chain to data engineering8:18 Tools & learning: Excel, Docker, and transitioning to data engineering21:57 Physical vs. digital warehousing: Analogies and key differences31:40 Introduction to FinOps: Cloud cost optimization and vendor negotiations40:18 Resources for FinOps: Certifications and the FinOps Foundation45:12 Standardizing cloud cost reporting across AWS/GCP/Azure50:04 Eddy’s master’s degree and closing thoughts🔗 CONNECT WITH EDDYTwitter - https://x.com/eddariefLinkedin - https://www.linkedin.com/in/eddyzulkifly/Github: https://github.com/eyzyly/eyzylyADPList: https://adplist.org/mentors/eddy-zulkifly🔗 CONNECT WITH DataTalksClubJoin the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck 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/

Bartosz Mikulski, an AI and data engineer, delves into the fascinating realm of Data Intensive AI. He shares his journey from Java development to AI engineering, emphasizing the pivotal role of testing in data pipelines. The conversation explores practical strategies for testing and the integration of AI tools like ChatGPT and Cursor in development workflows. Bartosz provides insights into creating a Chrome extension for lead classification on LinkedIn, while discussing prompt engineering techniques and the impact of AI on coding practices. A must-listen for data enthusiasts!

Nemanja Radojkovic, a Senior Machine Learning Engineer at Euroclear, shares his expertise in MLOps across both startups and corporations. He discusses the balance between flexibility and stability in different work environments, highlighting the unique challenges of implementing MLOps on tight budgets. The conversation covers navigating cloud complexities, the importance of selecting the right tools, and practical career advice for aspiring data scientists. Nemanja emphasizes mentorship and the evolving landscape of technology, making compelling points for both paths.

Adrian Brudaru, Co-founder of DLT Hub, shares his journey from corporate life to entrepreneurship in the realm of data engineering. He highlights the evolution of the modern data stack and the rising significance of open-source solutions. The conversation focuses on emerging trends like AI integration and innovative technologies such as Iceberg and DuckDB. Adrian also discusses the increasing opportunities for newcomers in the field and emphasizes the importance of understanding business contexts while navigating the evolving data landscape.

Join Alexander Guschin, a Kaggle Grandmaster and seasoned Machine Learning Engineer, as he shares his journey from Moscow to teaching over 100K students. Discover how participating in Kaggle competitions can jumpstart a career in machine learning and the significance of teamwork versus solo efforts. Guschin emphasizes the value of community support, the evolution of teaching methods, and practical applications in education. He also explores the impact of generative AI and AutoML on competitive data science, offering insights on how to persuade management about Kaggle's benefits.

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 DStack5:00 The Motivation Behind DStack7:00 Challenges in Machine Learning Infrastructure10:00 Transitioning from Cloud to On-Prem Solutions14:30 Reflections on OpenAI's Evolution17:30 Open Source vs Proprietary Models: A Balanced Perspective21:01 Monolithic vs. Decentralized AI businesses22:05 The role of privacy and control in AI for industries like banking and healthcare30:00 Challenges in training large AI models: GPUs and distributed systems37:03 DeepSpeed's efficient training approach vs. brute force methods39:00 Challenges for small and medium businesses: hosting and fine-tuning models47:01 Managing Kubernetes challenges for AI teams52:00 Hybrid vs. cloud-only infrastructure56:03 On-premise vs. bare-metal solutions58:05 Exploring edge computing and its challenges🔗 CONNECT WITH ANDREY CHEPTSOVTwitter -  / andrey_cheptsov  Linkedin -  / andrey-cheptsov  GitHub - https://github.com/dstackai/dstack/Website - https://dstack.ai/🔗 CONNECT WITH DataTalksClubJoin DataTalks.Club:⁠⁠⁠https://datatalks.club/slack.html⁠⁠⁠Our events:⁠⁠⁠https://datatalks.club/events.html⁠⁠⁠Datalike Substack -⁠⁠⁠https://datalike.substack.com/⁠⁠⁠LinkedIn:⁠⁠⁠  / datatalks-club  ⁠

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 community01:51 Topic introduction: Linguistic fairness and socio-technical perspectives in AI02:37 Guest introduction: Tamara’s background and career03:18 Tamara’s career journey: Software engineering, music tech, and computational linguistics09:53 Tamara’s background in language and computer science14:52 Exploring fairness in AI and its impact on society21:20 Fairness in AI models26:21 Automating fairness analysis in models32:32 Balancing technical and domain expertise in decision-making37:13 The role of humans in the loop for fairness40:02 Joining Probable and working on open-source projects46:20 Scopes library and its integration with Hugging Face50:48 PyLadies and community involvement55:41 The ethos of Scikit-learn and Fairlearn🔗 CONNECT WITH TAMARA ATANASOSKALinkedin - https://www.linkedin.com/in/tamaraatanasoskaGitHub- https://github.com/TamaraAtanasoska🔗 CONNECT WITH DataTalksClubJoin DataTalks.Club:⁠⁠https://datatalks.club/slack.html⁠⁠Our events:⁠⁠https://datatalks.club/events.html⁠⁠Datalike 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.