DataTalks.Club - the place to talk about data!
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DataOps, Observability, and The Cure for Data Team Blues - Christopher Bergh
53:47
53:47
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0:00 hi everyone Welcome to our event this event is brought to you by data dos club which is a community of people who love 0:06 data and we have weekly events and today one is one of such events and I guess we 0:12 are also a community of people who like to wake up early if you're from the states right Christopher or maybe not so 0:19 much because…
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Working as a Core Developer in the Scikit-Learn Universe - Guillaume Lemaître
52:30
52:30
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In this podcast episode, we talked with Guillaume Lemaître about navigating scikit-learn and imbalanced-learn.🔗 CONNECT WITH Guillaume LemaîtreLinkedIn - https://www.linkedin.com/in/guillaume-lemaitre-b9404939/ Twitter - https://x.com/glemaitre58Github - https://github.com/glemaitreWebsite - https://glemaitre.github.io/🔗 CONNECT WITH DataTalksClubJ…
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Building a Domestic Risk Assessment Tool - Sabina Firtala
49:35
49:35
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49:35
Links: LinkedIn:https://www.linkedin.com/company/frontline100/ Ba Linh Le's LinkedIn: https://www.linkedin.com/in/ba-linh-le-/ Sabrina's LinkedIn: https://www.linkedin.com/in/sabina-firtala/ Twitter: https://x.com/frontline_100?mx=2 Website: https://www.frontline100.com/ Free LLM course: https://github.com/DataTalksClub/llm-zoomcampJoin DataTalks.C…
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We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.You can access all the podcast episodes here - https://datatalks.club/podcast.html📚Check our free online coursesML Engineering course - http://mlzoomcamp.comData Engineering course - https:…
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Community Building and Teaching in AI & Tech - Erum Afzal
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50:01
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We talked about: Erum's Background Omdena Academy and Erum’s Role There Omdena’s Community and Projects Course Development and Structure at Omdena Academy Student and Instructor Engagement Engagement and Motivation The Role of Teaching in Community Building The Importance of Communities for Career Building Advice for Aspiring Instructors and Freela…
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Working in Open Source - Probabl.ai and sklearn - Vincent Warmerdam
52:02
52:02
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52:02
We talked about: Vincent’s Background SciKit Learn’s History and Company Formation Maintaining and Transitioning Open Source Projects Teaching and Learning Through Open Source Role of Developer Relations and Content Creation Teaching Through Calm Code and The Importance of Content Creation Current Projects and Future Plans for Calm Code Data Proces…
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AI for Ecology, Biodiversity, and Conservation - Tanya Berger-Wolf
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51:47
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Links: Biodiversity and Artificial Intelligence pdf: https://www.gpai.ai/projects/responsible-ai/environment/biodiversity-and-AI-opportunities-recommendations-for-action.pdf Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club…
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Knowledge Graphs and LLMs Across Academia and Industry - Anahita Pakiman
53:14
53:14
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We talked about: Anahita's Background Mechanical Engineering and Applied Mechanics Finite Element Analysis vs. Machine Learning Optimization and Semantic Reporting Application of Knowledge Graphs in Research Graphs vs Tabular Data Computational graphs Graph Data Science and Graph Machine Learning Combining Knowledge Graphs and Large Language Models…
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Inclusive Data Leadership Coaching - Tereza Iofciu
48:16
48:16
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48:16
We talked about: Tereza’s background Switching from an Individual Contributor to Lead Python Pizza and the pizza management metaphor Learning to figure things out on your own and how to receive feedback Tereza as a leadership coach Podcasts Tereza’s coaching framework (selling yourself vs bragging) The importance of retrospectives The importance of…
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Building Production Search Systems - Daniel Svonava
58:25
58:25
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Links: VectorHub: https://superlinked.com/vectorhub/?utm_source=community&utm_medium=podcast&utm_campaign=datatalks Daniel's LinkedIn: https://www.linkedin.com/in/svonava/ Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/e…
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Building Machine Learning Products - Reem Mahmoud
56:48
56:48
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We talked about: Reem’s background Context-aware sensing and transfer learning Shifting focus from PhD to industry Reem’s experience with startups and dealing with prejudices towards PhDs AI interviewing solution How candidates react to getting interviewed by an AI avatar End-to-end overview of a machine learning project The pitfalls of using LLMs …
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Make an Impact Through Volunteering Open Source Work - Sara EL-ATEIF
55:56
55:56
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We talked about: Sara’s background On being a Google PhD fellow Sara’s volunteer work Finding AI volunteer work Sara’s Fruit Punch challenge How to take part in AI challenges AI Wonder Girls Hackathons Things people often miss in AI projects and hackathons Getting creative Fostering your social media Tips on applying for volunteer projects Why it’s…
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Accelerating The Job Hunt for The Perfect Job in Tech - Sarah Mestiri
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53:04
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53:04
We talked about: Sarah’s background How Sarah became a coach and found her niche Sarah’s clients How Sarah helps her clients find the perfect job Finding a specialization Informational interviews Building a connection for mutual benefit The networking strategy Listing your projects in the CV The importance of doing research yourself and establishin…
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Machine Learning Engineering in Finance - Nemanja Radojkovic
53:10
53:10
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We talked about: Nemanja’s background When Nemanja first work as a data person Typical problems that ML Ops folks solve in the financial sector What Nemanja currently does as an ML Engineer The obstacle of implementing new things in financial sector companies Going through the hurdles of DevOps Working with an on-premises cluster “ML Ops on a Shoes…
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Stock Market Analysis with Python and Machine Learning - Ivan Brigida
55:31
55:31
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55:31
We talked about: Ivan’s background How Ivan became interested in investing Getting financial data to run simulations Open, High, Low, Close, Volume Risk management strategy Testing your trading strategies Sticking to your strategy Important metrics and remembering about trading fees Important features Deployment How DataTalks.Club courses helped Iv…
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Bayesian Modeling and Probabilistic Programming - Rob Zinkov
54:15
54:15
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54:15
We talked about: Rob’s background Going from software engineering to Bayesian modeling Frequentist vs Bayesian modeling approach About integrals Probabilistic programming and samplers MCMC and Hakaru Language vs library Encoding dependencies and relationships into a model Stan, HMC (Hamiltonian Monte Carlo) , and NUTS Sources for learning about Bay…
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Navigating Challenges and Innovations in Search Technologies - Atita Arora
57:00
57:00
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57:00
We talked about: Atita’s background How NLP relates to search Atita’s experience with Lucidworks and OpenSource Connections Atita’s experience with Qdrant and vector databases Utilizing vector search Major changes to search Atita has noticed throughout her career RAG (Retrieval-Augmented Generation) Building a chatbot out of transcripts with LLMs I…
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The Entrepreneurship Journey: From Freelancing to Starting a Company - Adrian Brudaru
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56:21
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56:21
We talked about: Adrian’s background The benefits of freelancing Having an agency vs freelancing What let Adrian switch over from freelancing The conception of DLT (Growth Full Stack) The investment required to start a company Growth through the provision of services Growth through teaching (product-market fit) Moving on to creating docs Adrian’s c…
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Become a Data Freelancer - Dimitri Visnadi
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We talked about: Dimitri’s background The first steps of transitioning into freelance Working with recruiters (contracting) Deciding on what to charge for your services Establishing your network Self-marketing Contracting vs freelancing Which channel is better for those starting out? Cutting out the middleman Where to look for clients and how to ve…
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AI for Digital Health - Maria Bruckert
50:24
50:24
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We talked about: Maria’s background Deciding to go into telecare (healthcare) Current difficulties in healthcare Getting into the healthcare industry as a lifestyle brand The importance of a plan B and being flexible What is SQIN and the importance of communication Going from lipstick to skin health analysis The importance of community and broadeni…
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Cracking the Code: Machine Learning Made Understandable - Christoph Molnar
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51:59
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51:59
We talked about: Christoph’s background Kaggle and other competitions How Christoph became interested in interpretable machine learning Interpretability vs Accuracy Christoph’s current competition engagement How Christoph chooses topics for books Why Christoph started the writing journey with a book Self-publishing vs via a publisher Christoph’s ot…
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The Unwritten Rules for Success in Machine Learning - Jack Blandin
50:26
50:26
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50:26
We talked about: Jack’s background Transitioning from IC to management Lesson not taught in traditional school The importance of people’s perception, trust, and respect How soft skills are relevant to machine learning How to put on a salesman hat in machine learning management The importance of visuals and building a POC as fast as possible 1st Rul…
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From a Research Scientist at Amazon to a Machine learning/AI Consultant - Verena Webber
54:55
54:55
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54:55
Links: Mini sound bath: https://www.youtube.com/watch?v=g-lDrcSqcrQ Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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From Marketing to Product Owner in Search - Lera Kaimashnіkova
55:14
55:14
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55:14
We talked about: Lera’s background Lera’s move from Ukraine to Germany The transition from Marketing to Product Ownership The importance of communication and one-on-ones The role of Product Owner Utilizing Scrum as a Product Owner Building teams and cross-functionality Lera’s experience learning about search The importance of having both technical …
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Collaborative Data Science in Business - Ioannis Mesionis
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55:50
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55:50
Links: LinkedIn: https://www.linkedin.com/in/ioannis-mesionis/ Github: https://github.com/ioannismesionis Website: https://ioannismesionis.github.io/ Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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Bridging Data Science and Healthcare - Eleni Stamatelou
54:02
54:02
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54:02
Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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DataTalks.Club Anniversary Interview - Alexey Grigorev, Johanna Bayer
57:44
57:44
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Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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Data Engineering for Fraud Prevention - Angela Ramirez
54:14
54:14
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We talked about: Angela's background Angela's role at Sam's Club The usefulness of knowing ML as a data engineer Angela's career path Transitioning from data analyst to data engineer/system designer Best practices for system design and data engineering Working with document databases Working with network-based databases Detecting fraud with a netwo…
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From Data Manager to Data Architect - Loïc Magnien
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56:41
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We talked about: Loïc's background Data management Loïc's transition to data engineer Challenges in the transition to data engineering What is a data architect? The output of a data architect's work Establishing metrics and dimensions The importance of communication Setting up best practices for the team Staying relevant and tech-watching Setting u…
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Pragmatic and Standardized MLOps - Maria Vechtomova
53:43
53:43
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We talked about: Maria's background Marvelous MLOps Maria's definition of MLOps Alternate team setups without a central MLOps team Pragmatic vs non-pragmatic MLOps Must-have ML tools (categories) Maturity assessment What to start with in MLOps Standardized MLOps Convincing DevOps to implement Understanding what the tools are used for instead of kno…
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Democratizing Causality - Aleksander Molak
56:00
56:00
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56:00
We talked about: Aleksander's background Aleksander as a Causal Ambassador Using causality to make decisions Counterfactuals and and Judea Pearl Meta-learners vs classical ML models Average treatment effect Reducing causal bias, the super efficient estimator, and model uplifting Metrics for evaluating a causal model vs a traditional ML model Is the…
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Mastering Data Engineering as a Remote Worker - José María Sánchez Salas
46:30
46:30
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46:30
We talked about: José's background How José relocated to Norway and his schedule Tech companies in Norway and José role Challenges of working as a remote data engineer José's newsletter on how to make use of data The process of making data useful Where José gets inspiration for his newsletter Dealing with burnout When in Norway, do as the Norwegian…
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The Good, the Bad and the Ugly of GPT - Sandra Kublik
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50:53
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We talked about: Sandra's background Making a YouTube channel to break into the LLM space The business cases for LLMs LLMs as amplifiers The befits of keeping a human in the loop when using LLMs (AI limitations) Using LLMs as assistants Building an app that uses an LLM Prompt whisperers and how to improve your prompts Sandra's 7-day LLM experiment …
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LLMs for Everyone - Meryem Arik
55:28
55:28
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55:28
We talked about: Meryam's background The constant evolution of startups How Meryam became interested in LLMs What is an LLM (generative vs non-generative models)? Why LLMs are important Open source models vs API models What TitanML does How fine-tuning a model helps in LLM use cases Fine-tuning generative models How generative models change the lan…
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Investing in Open-Source Data Tools - Bela Wiertz
54:57
54:57
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54:57
We talked about: Bela's background Why startups even need investors Why open source is a viable go-to-market strategy Building a bottom-up community The investment thesis for the TKM Family Office and the blurriness of the funding round naming convention Angel investors vs VC Funds vs family offices Bela's investment criteria and GitHub stars as a …
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Why Machine Learning Design is Broken - Valerii Babushkin
51:20
51:20
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51:20
Links: Book: https://www.manning.com/books/machine-learning-system-design?utm_source=AGMLBookcamp&utm_medium=affiliate&utm_campaign=book_babushkin_machine_4_25_23&utm_content=twitter Discount: poddatatalks21 (35% off) Evidently: https://www.evidentlyai.com/ Article: https://medium.com/people-ai-engineering/design-documents-for-ml-models-bbcd30402ff…
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Interpretable AI and ML - Polina Mosolova
52:47
52:47
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We talked about: Polina's background How common it is for PhD students to build ML pipelines end-to-end Simultaneous PhD and industry experience Support from both the academic and industry sides How common the industrial PhD setup is and how to get into one Organizational trust theory How price relates to trust How trust relates to explainability T…
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From Scratch to Success: Building an MLOps Team and ML Platform - Simon Stiebellehner
53:33
53:33
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53:33
We talked about: Simon's background What MLOps is and what it isn't Skills needed to build an ML platform that serves 100s of models Ranking the importance of skills The point where you should think about building an ML platform The importance of processes in ML platforms Weighing your options with SaaS platforms The exploratory setup, experiment t…
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From MLOps to DataOps - Santona Tuli
53:05
53:05
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53:05
We talked about: Santona's background Focusing on data workflows Upsolver vs DBT ML pipelines vs Data pipelines MLOps vs DataOps Tools used for data pipelines and ML pipelines The “modern data stack” and today's data ecosystem Staging the data and the concept of a “lakehouse” Transforming the data after staging What happens after the modeling phase…
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Data Developer Relations - Hugo Bowne-Anderson
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50:51
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We talked about: Hugo's background Why do tools and the companies that run them have wildly different names Hugo's other projects beside Metaflow Transitioning from educator to DevRel What is DevRel? DevRel vs Marketing How DevRel coordinates with developers How DevRel coordinates with marketers What skills a DevRel needs The challenges that come w…
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Lessons Learned from Freelancing and Working in a Start-up - Antonis Stellas
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We talked about; Antonis' background The pros and cons of working for a startup Useful skills for working at a startup and the Lean way to work How Antonis joined the DataTalks.Club community Suggestions for students joining the MLOps course Antonis contributing to Evidently AI How Antonis started freelancing Getting your first clients on Upwork Pr…
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Data Access Management - Bart Vandekerckhove
50:28
50:28
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We talked about: Bart's background What is data governance? Data dictionaries and data lineage Data access management How to learn about data governance What skills are needed to do data governance effectively When an organization needs to start thinking about data governance Good data access management processes Data masking and the importance of …
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Data Strategy: Key Principles and Best Practices - Boyan Angelov
55:49
55:49
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55:49
We talked about: Boyan's background What is data strategy? Due diligence and establishing a common goal Designing a data strategy Impact assessment, portfolio management, and DataOps Data products DataOps, Lean, and Agile Data Strategist vs Data Science Strategist The skills one needs to be a data strategist How does one become a data strategist? D…
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Practical Data Privacy - Katharine Jarmul
57:44
57:44
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57:44
We talked about: Katharine's background Katharine's ML privacy startup GDPR, CCPA, and the “opt-in as the default” approach What is data privacy? Finding Katharine's book – Practical Data Privacy The various definitions of data privacy and “user profiles” Privacy engineering and privacy-enhancing technologies Why data privacy is important What is d…
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Building Scalable and Reliable Machine Learning Systems - Arseny Kravchenko
50:59
50:59
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50:59
We talked about: Arseny's background Working on machine learning in startups What is Machine Learning System Design? Constraints and requirements Known unknowns vs unknown unknowns (Design stage) Writing a design document Technical problems vs product-oriented problems The solution part of the Design Document What motivated Arseny to write a book o…
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Building an Open-Source NLP Tool - Johannes Hötter
56:26
56:26
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56:26
We talked about: Johannes’s background Johannes’s Open Source Spotlight demos – Refinery and Bricks The difficulties of working with natural language processing (NLP) Incorporating ChatGPT into a process as a heuristic What is Bricks? The process of starting a startup – Kern Making the decision to go with open source Pros and cons of launching as o…
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Navigating Industrial Data Challenges - Rosona Eldred
53:22
53:22
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53:22
We talked about: Rosona’s background How mathematics knowledge helps in industry What is industrial data? Setting up an industrial process using blue paint Internet companies’ data vs industrial data Explaining industrial processes using packing peanuts Why productive industry needs data Measuring product qualities How data specialists use industri…
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Mastering Self-Learning in Machine Learning - Aaisha Muhammad
51:02
51:02
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51:02
We talked about: Aaisha’s background How homeschooling affects self-study Deciding on what to learn about Establishing whether a resource is good How Aaisha focuses on learning Deciding on what kind of project to build Find research materials Aaisha’s experience with the Data Talks Club ML Zoomcamp ML Zoomcamp projects Aaisha’s interest in bioinfor…
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The Secret Sauce of Data Science Management - Shir Meir Lador
48:42
48:42
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48:42
We talked about: Shir’s background Debrief culture The responsibilities of a group manager Defining the success of a DS manager The three pillars of data science management Managing up Managing down Managing across Managing data science teams vs business teams Scrum teams, brainstorming, and sprints The most important skills and strategies for DS a…
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SE4ML - Software Engineering for Machine Learning - Nadia Nahar
53:39
53:39
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53:39
We talked about: Nadia’s background Academic research in software engineering Design patterns Software engineering for ML systems Problems that people in industry have with software engineering and ML Communication issues and setting requirements Artifact research in open source products Product vs model Nadia’s open source product dataset Failure …
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