{"podcast":{"title":"Day Two DevOps","slug":"day-two-devops","podcast_index_feed_id":341814,"rss_url":"https://feeds.packetpushers.net/day2cloud/","website_url":"https://packetpushers.net/","image_url":"https://static.feedpress.com/logo/day2cloud-669fc5e024d4b.jpg","author":"Packet Pushers","episode_count":250,"summary":"Join hosts Ned Bellavance and Ethan Banks as they dive deep into the challenges of cloud operations from the perspective of seasoned practitioners. You'll hear from expert guests—technical leaders, trainers, and consultants with years of hands-on experience—discussing the nuances of modern cloud environments. From AWS to Azure, networking to security, automation to DevOps, each weekly episode equips you with the insights to confidently address tech and business challenges such as resilience, cost management, and performance. Whether you want to hone your skills today or prepare for what’s coming next, Day Two Cloud cuts through the vendor fog to guide you through a shifting IT landscape.","last_synced_at":null,"page_url":"https://stenobird.com/podcast/day-two-devops"},"episode":{"title":"D2DO291: From Politics to Machine Learning and AI Engineering","slug":"d2do291-from-politics-to-machine-learning-and-ai-engineering","published_at":"2026-01-07T16:34:02+00:00","page_url":"https://stenobird.com/podcast/day-two-devops/d2do291-from-politics-to-machine-learning-and-ai-engineering","show_page_url":"https://stenobird.com/podcast/day-two-devops","url":"https://packetpushers.net/podcasts/day-two-devops/d2do291-from-politics-to-machine-learning-and-ai-engineering/","audio_url":"https://feeds.packetpushers.net/link/20975/17248695/D2DO291.mp3","summary":"Learn the fundamental distinction between Machine Learning Engineering and AI Engineering through the lens of a Senior Applied Scientist at Twitch. This episode explores how to transition from using pre-trained APIs to building production-ready applications and managing data pipelines.","meta_description":"Explore the differences between AI Engineering and ML Engineering, with practical advice on data pipelines, Airflow, and building AI-driven products.","key_points":["Main idea: AI Engineering focuses on building products using pre-trained models and APIs, whereas ML Engineering involves training models from scratch","Practical takeaway: Use Jupyter Notebooks for early-stage prototyping and data exploration, but transition to Python scripts and orchestrators for production","Failure mode: Relying solely on certifications rather than building and deploying production-ready, user-facing applications","Technical insight: Data orchestration tools like Airflow are essential for managing complex DAGs and triggering training jobs in large-scale environments","Practical takeaway: Evaluate LLM outputs using rubrics, specific rules, or 'LLM-as-a-judge' patterns to handle subjective content"],"chapters":[{"start_ms":60000,"title":"Defining the Role of an Applied Scientist","summary":"An introduction to Marina Wyss's role at Twitch and the scope of her work across ML infrastructure and recommendation systems."},{"start_ms":250000,"title":"AI Engineering vs. Machine Learning Engineering","summary":"A breakdown of the distinction between using pre-trained models via APIs versus training custom models from scratch."},{"start_ms":625000,"title":"Career Paths and Skill Acquisition","summary":"Discussing the non-linear paths into data science and the importance of foundational statistics and domain knowledge."},{"start_ms":1000000,"title":"Demystifying Large Language Models","summary":"Understanding LLMs as probabilistic next-token predictors and how to approach model selection."},{"start_ms":1185000,"title":"Data Orchestration with Airflow","summary":"An overview of using Airflow for managing data pipelines, DAGs, and integrating with services like AWS SageMaker."},{"start_ms":1550000,"title":"Advice for Aspiring AI Engineers","summary":"Practical recommendations for learning, including essential reading and the value of self-directed projects."},{"start_ms":1740000,"title":"Evaluating Autonomous AI Agents","summary":"Strategies for evaluating the performance of LLMs in subjective tasks like email drafting."}],"topics":["AI Engineering","Machine Learning","Data Pipelines","Airflow","LLM Evaluation","Data Science Careers","AWS SageMaker","Applied Science"],"duration_seconds":2503,"processing_state":"processed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/day-two-devops/episodes/d2do291-from-politics-to-machine-learning-and-ai-engineering/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/day-two-devops/d2do291-from-politics-to-machine-learning-and-ai-engineering.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}