Effortless Change Data Capture with Debezium and Kubernetes
Harness the power of Change Data Capture in Kubernetes using Debezium. Learn to track, stream, and deploy real-time database changes efficiently for data-driven applications.
Jupyter Extension for Executing Kubeflow Pipeline Seamlessly
Explore Elyra's integration with Kubeflow 2.0, enhancing ML workflow development and deployment on Kubernetes with visual editing and multi-language support.
Laboratory and Hands-on Activities for Dark Matter and Neutrino Detection
Explore hands-on experiments in dark matter and neutrino detection, covering sensitivity limits, simulations, particle detection techniques, and detector characterization.
Machine Learning for Quantum Applications - Part 2
Explore advanced machine learning techniques for quantum applications, focusing on cutting-edge developments and practical implementations in quantum systems.
Quality Improvement Cycle: Study for Substance Use Disorders
Analyze and interpret data collected during the "Do" phase of the Quality Improvement Cycle for substance use disorder treatment.
Creating Your Own Singing Voice Synthesizer: Overcoming Data Collection Challenges
Explore techniques for creating personalized singing voice synthesizers, from manual labeling to automated data collection, overcoming challenges in dataset creation for various languages.
Installing and Configuring Prometheus - A Comprehensive Guide
Install, configure, and leverage Prometheus for effective system monitoring. Master data collection, querying, visualization, and alerting to gain valuable insights into your applications and infrastructure.
Apache Kafka Real-Time Personalization Architecture
Explore real-time personalization architecture using Apache Kafka and Spark for advanced content recommendation systems in data engineering and AI.
Exploring Edge Computing in Automotive: From Autonomous Cars to Factories
Discover how edge computing transforms automotive data processing, addressing challenges in decentralized environments like factories and vehicles.
Inside Spark and Kafka: Data Processing and Real-Time Analytics - Episode 1
Explore Charmed Spark and Kafka: Canonical's solutions for Apache Spark and Kafka on Kubernetes. Discover industry trends, real-time applications, and data processing innovations.
Putting an AI-Powered Data Analyst to the Test
Evaluate an AI-powered analyst prototype across beginner to advanced analytics queries, showcasing its capabilities in data analysis and interpretation.
Retrieval Augmented Generation (RAG) for LLM-Powered Apps
Explore retrieval augmented generation (RAG) for enhancing LLM-powered applications, gaining insights into its importance and practical implementation.
Enterprise Jargon and RAG Systems - Effective Tactics for Improved Performance
Discover effective tactics to enhance RAG and retrieval systems for handling enterprise jargon, improving LLM-powered tools and applications for businesses.
Why Retrieval Augmented Generation (RAG) is Overrated
Explore the limitations of Retrieval Augmented Generation (RAG), including hallucinations, complexity, and cost. Learn practical approaches to make RAG more effective in AI applications.
Connecting a Llama 2 Chatbot to the Internet - Search and Custom Functions
Connect a Llama 2 chatbot to the internet, enabling Bing searches and custom function integration for enhanced capabilities like Google searches, paper lookups, and API connections.
Preparing Fine-tuning Datasets with Open Source LLMs
Learn to prepare fine-tuning datasets using open-source LLMs like Llama 2. Explore data preparation scripts, Q&A dataset creation, and API setup for effective model customization.
Chat Fine-Tuning for LLMs - Instruction Format, Datasets, and Implementation
Master LLM instruction fine-tuning techniques, from understanding special tokens to implementing various prompt formats. Gain hands-on experience with datasets and Jupyter lab, culminating in practical chat fine-tuning results.
Embeddings vs Fine-Tuning - Part 3: Unsupervised Fine-Tuning
Explore unsupervised fine-tuning techniques for embeddings, including dataset cleanup, Google Colab implementation, and expert tips for optimal results.
Fine-tuning Multi-modal Video and Text Models
Explore fine-tuning techniques for multi-modal video and text models, covering dataset generation, clipping, querying, and evaluation using IDEFICS 2 and Jupyter Notebooks.
KubeRay: A Ray Cluster Management Solution on Kubernetes
Explore KubeRay's integration with Kubernetes, its capabilities for Ray cluster management, and performance benchmarks for simplified deployment of Ray applications.
A Spline Tour of Deep Learning: The Scattering Way
Explore deep learning's progress, challenges, and theoretical foundations. Gain insights into the geometric interpretation of deep networks and spline approximation, with a focus on Scattering Networks.