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AI got cheap enough that Duolingo’s most expensive plan may not survive it. I read the earnings call transcript and opened the app to see what is actually changing for learners.
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Explore Retrieval Augmented Generation using LangChain to build a question-answering system with private data, leveraging vector stores, embedding models, and LLMs.
Explore NASA's use of generative design and AI prompt engineering to enhance spaceflight missions, reducing costs and improving performance.
Discover five key lessons for building cohesive MLOps platforms, enhancing self-service capabilities, and maximizing return on investment in machine learning operations.
Gain insights into building an MLOps deployment platform using Kubernetes, integrating key technologies for serving and monitoring complex deep learning models.
Discover rapid ML model iteration and deployment techniques used at Dropbox, reducing development time from weeks to under an hour.
Explore risks and strategies for safely managing generative AI coding tools in organizations, supporting innovation while minimizing potential hazards.
Explore the journey from LLM playgrounds to enterprise-scale production pipelines, addressing challenges and leveraging MLOps infrastructure for cutting-edge models.
Discover practical tools and algorithmic strategies to automatically identify and address dataset issues, improving ML model performance across various data types.
Explore challenges in ML evaluation and monitoring, focusing on stable baselines in changing environments and their implications for traditional and emerging ML systems.
Explore on-device computing for real-time personalization, balancing privacy, latency, and scale while reducing cloud reliance and enhancing ML capabilities.
Discover strategies for integrating LLMs into enterprise infrastructure, overcoming challenges, and leveraging cloud computing for AI applications.
Learn to build robust guardrails for AI applications, ensuring consistent and accurate outputs from Large Language Models in production environments.
Explore unique data challenges in video games, focusing on model deployment and evolving ML stacks in League of Legends since 2009.
Explore real-world examples of failed GenAI implementations in large US enterprises, uncovering key lessons and unique challenges beyond common concerns.
Explore the effectiveness of in-context learning vs. labelled data for predictive tasks, comparing BERT-sized models to GPT-4 in text categorization and entity recognition.
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