AI-Enabled Chatbot to Production MLOps Application - Red Hat AI Portfolio Demo
Explore Red Hat's AI portfolio: from simple chatbots to full-scale MLOps applications for enterprise. Learn about Podman AI Lab, RHEL AI, and OpenShift AI for building and deploying AI solutions.
The State of Production Machine Learning in 2024
Explore challenges and best practices in production machine learning systems, covering deployment, explanation, security, monitoring, and scaling. Gain insights into the ML tooling ecosystem.
Scheming with Machines
Explore machine learning techniques for sandbox detection, feature selection, and model deployment in cybersecurity. Gain insights on client-side models and data acquisition strategies.
Machine Learning with TensorFlow
Introductory talk on TensorFlow, demonstrating how to create a simple neural network and use Google Cloud Machine Learning API for training and real-time predictions.
Machine Learning for Autonomous Vehicles
Explore machine learning for autonomous vehicles, covering data collection, model development, security, and industry trends. Gain insights from experts on the future of self-driving technology.
Machine Learning for Autonomous Vehicles
Explore machine learning applications in autonomous vehicles, covering data requirements, security, language choices, and model deployment considerations with industry experts Oscar Beijbom and Prayson Daniel.
Bridging Data Science and Data Infrastructure with Apache Airflow
Explore how Apache Airflow bridges data science and infrastructure, enabling collaboration between teams and facilitating the development of flexible, observable, and resilient data pipelines.
Charlas - Kin Gutierrez Olivares/Federico Garza Ramirez: Nixtla: Deep Learning para pronóstico
Introducción a Nixtla, una librería de Python para pronóstico de series temporales con deep learning. Explora modelos avanzados, aplicaciones comerciales y mejores prácticas para entrenamiento y despliegue.
Modern, Typed Python for Multimodal ML - From Training to Deployment
Explore the benefits of Python type hints in machine learning, from improving code quality to organizing multimodal data and bridging the gap between training and deployment.
Deep Learning with Python & TensorFlow
Explore deep learning with TensorFlow: compare Python ML libraries, create data flow graphs, and learn deployment strategies for production systems.
How Booking.com Serves Deep Learning Model Predictions
Discover Booking.com's approach to deploying deep learning models in production, covering training, automated retraining, deployment, and optimization for latency and throughput.
Real Time Stream Processing for Machine Learning at Massive Scale
Practical insights on building scalable data streaming ML pipelines for real-time processing using Python, Kafka, SpaCy, and Seldon. Covers key concepts and best practices through a Reddit comment moderation case study.
Automating Machine Learning Workflow with DVC
Discover how to automate and streamline machine learning workflows using DVC, from data ingestion to model deployment. Learn to organize pipelines efficiently, regardless of project size.
Secure Python ML - The Major Security Flaws in the ML Lifecycle and How to Avoid Them
Uncover critical security risks in machine learning lifecycle, from training to deployment. Learn practical mitigation strategies and tools to secure ML systems effectively.
Applying Machine Learning Capabilities to Wearable IoT Devices for Boxing Technique Management
Explore how machine learning algorithms on IoT devices can enhance boxing training using motion sensor data, from problem analysis to algorithm deployment on Arduino.
Building and Deploying Fair and Unbiased ML Systems - An Art, Not Science
Explore effective practices for developing and deploying ethical, fair, and unbiased machine learning models in production, addressing challenges of data bias and model complexity.
Real-Time Big Data Analytics with R
Explore real-time big data analytics using R, covering data distillation, model development, validation, deployment, refresh, and scoring. Learn to integrate R with Hadoop and database systems.
The New ML Lifecycle - Deploying and Managing
Explore the challenges and solutions in adapting infrastructure for ML deployment, including automation, integration, and governance at scale. Learn best practices for the new ML lifecycle.
Hidden Differences Between Azure ML Products Revealed
Explore Azure ML products, their differences, and use cases. Learn to create end-to-end ML workflows, integrate with SQL Server, and manage models in production using various Azure tools.
Machine Learning in C# - Introduction to ML.NET
Discover ML.NET: Build machine learning applications using C# and .NET. Learn data preparation, model evaluation, and deployment techniques for integrating ML capabilities into your .NET projects.
Quick Start with AI for Businesses
Strategies for businesses to quickly adopt AI, focusing on data collection, management involvement, and model capabilities. Proposes an embedded approach for efficient implementation and scaling.