DataTalks.Club

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 knowledge and business context Open developer positions at AUTODOC What experience Lera came to AUTODOC with How marketing skills helped Lera in her current role Lera’s resource recommendations Everything is possible Links: Post: https://www.linkedin.com/posts/leracaiman_elasticsearch-ecommerce-activity-7106615081588674560-5WQO Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Ioannis Mesionis, an expert in collaborative data science in business, discusses topics such as collaborating with business stakeholders, optimizing digital marketing, agile project management in data science, and finding time for hands-on work in data science.

Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Angela Ramirez, a data engineer with experience in fraud prevention, talks about her career journey, the usefulness of knowing ML as a data engineer, best practices for system design and data engineering, working with different types of databases including document and network-based databases, and selecting the appropriate database type to work with. She also discusses the importance of software engineering knowledge in data engineering, data quality check tooling, debugging failed jobs, and working with external data sources.

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 up specifications for a pipeline Be agile, create a POC, iterate ASAP, and build reusable templates Reaching out to Loïc for questions Links: Loiic LinkedIn: https://www.linkedin.com/in/loicmagnien/ Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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 knowing all the tools Maria's next project plans Is LLM Ops a thing? What Ahold Delhaize does Resource recommendations to learn more about MLOps The importance of data engineering knowledge for ML engineers Links: LinkedIn: https://www.linkedin.com/company/marvelous-mlops/ Website: https://marvelousmlops.substack.com/ Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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 added complexity of a causal model worth implementing? Utilizing LLMs in causal models (text as outcome) Text as treatment and style extraction The viability of A/B tests in causal models Graphical structures and nonparametric identification Aleksander's resource recommendations Links: The Book of Why: https://amzn.to/3OZpvBk Causal Inference and Discovery in Python: https://amzn.to/46Pperr Book's GitHub repo: https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python The Battle of Giants: Causality vs NLP (PyData Berlin 2023): https://www.youtube.com/watch?v=Bd1XtGZhnmw New Frontiers in Causal NLP (papers repo): https://bit.ly/3N0TFTL Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Topics include moving from Spain to Norway, organizing the day as a remote worker, company's expertise and data collection process, challenges of finding a remote job in Norway, finding inspiration and writing interesting topics, benefits and challenges of remote work as a data engineer.

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 Sandra's LLM content recommendations Finding Sandra online Links: LinkedIn: https://www.linkedin.com/in/sandrakublik/ Twitter: https://twitter.com/sandra_kublik Youtube: https://www.youtube.com/@sandra_kublik Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html