Real-World PyTorch: From Zero to Hero in Deep Learning and LLMs - Tensors, Operations, Model Training
Comprehensive PyTorch tutorial covering tensors, operations, GPU usage, datasets, neural networks, and model training. Hands-on examples for mastering deep learning and LLMs.
Self-Hosted LLMs on Kubernetes - A Practical Guide
Practical guide for deploying and managing self-hosted Large Language Models on Kubernetes, covering model selection, containerization, and resource provisioning for enhanced data privacy and flexibility.
Fine-tuning Phi-3 for LeetCode: Dataset Generation and Unsloth Implementation
Learn to generate LeetCode datasets and fine-tune Phi-3 models using Unsloth for improved coding problem-solving performance. Explore dataset creation, model training, and result evaluation.
How to Get Started with MLOps in Azure Databricks
Explore the machine learning lifecycle and operationalize models using Databricks Machine Learning, focusing on scaling deployments with MLOps for company-wide implementation.
Computer Vision Workshop on OpenShift: AI/ML Platform for Model Development and Deployment
Explore computer vision use cases on OpenShift, from data collection and annotation to model training and deployment, with practical demonstrations on cars and trains detection.
Resurrecting a Recommendations Platform
Explore the implementation of a deep learning model for recommendations while maintaining a legacy platform, focusing on data, machine learning, and A/B testing tiers in cloud migration.
Transfer Learning - Analyst-Sourcing Behavioral Classification
Explore advanced threat detection using AI and transfer learning techniques. Learn about active learning, data sharing, and improving phishing domain detection across organizations.
Machine Learning Made Easy on Kubernetes - DevOps for Data Scientists
Explore DevOps for ML on Kubernetes: streamline data prep, model training, testing, and deployment using tools like Tensorflow, Kubeflow, and Pachyderm. Ideal for data scientists and SRE teams.
Distributed Deep Graph Learning at Scale
Explores a novel system for scaling Graph Neural Network training to large real-world graphs, introducing pipelined push-pull parallelism for faster and more efficient distributed processing.
Managing ML Models at Scale - Intuit’s ML Platform
Explore Intuit's ML Platform for scalable, secure model lifecycle management using GitOps, SageMaker, Kubernetes, and Argo Workflows. Learn about feature management, cost optimization, and enterprise-level ML model deployment.
An Open Source Malware Classifier and Dataset
Explore open-source malware classification using machine learning, featuring a diverse Windows PE file dataset, pre-trained models, and reproducible code for enhanced static malware detection research.
Scaling ML Pipelines with KALE - The Kubeflow Automated Pipeline Engine
Discover KALE, the Kubeflow Automated Pipeline Engine, for scaling ML pipelines. Learn how to transform Jupyter Notebook experiments into production-grade pipelines for efficient model training and real-time serving.
Building and Managing a Centralized ML Platform with Kubeflow at CERN
Explore CERN's centralized ML platform using Kubeflow, covering data preparation, model training, and serving. Learn about resource optimization, integration challenges, and cloud expansion possibilities.
Understanding What Artificial Intelligence Actually Sees
Unveiling AI's perception: Learn how AI interprets data, train custom object detection models with minimal data, and implement them in real-time web apps.
Using Kubernetes for Machine Learning Frameworks
Learn to leverage Kubernetes for training and deploying machine learning models using TensorFlow, Apache MXNet, and PyTorch. Explore isolation, auto-scaling, and GPU support for efficient ML workflows.
Image Recognition with Deep Learning - From Prototype to Production
Explore image recognition using deep learning, from prototype to production. Learn to develop and implement neural networks for clothing characteristic detection, with insights on training, deployment, and front-end integration.
Deep Learning for Developers
Explore state-of-the-art deep learning techniques through code demos using Apache MXNet and Keras, covering CNN, GAN, and LSTM architectures for advanced AI applications.
Scaling Machine Learning with Spark
Learn to build scalable machine learning solutions with Apache Spark. Explore distributed computing, MLflow, feature engineering, and integrating Spark with deep learning frameworks like TensorFlow and PyTorch.
Machine Learning Made Easy With PyCaret
Discover how PyCaret simplifies machine learning workflows, automates processes, and boosts productivity. Learn to integrate this low-code library for faster, more efficient ML experiments.
Scaling Machine Learning with Spark
Learn to build scalable machine learning solutions using Apache Spark, MLlib, MLflow, TensorFlow, and PyTorch. Explore distributed computing concepts, manage ML lifecycles, and combine Spark with deep learning for end-to-end workflows.
Deep Learning and Spark
Learn to classify millions of messages using trained deep learning models (fasttext) with Spark. Includes explanations of key concepts and a practical demo on model training and message classification.