Automated Pipeline for Large-Scale Neural Network Training and Inference
Explore cloud-native ML pipeline for automated anomaly detection using BiLSTM models. Learn to manage thousands of models with data collection, training, validation, and deployment techniques.
Reaching Lightspeed Data Science - ETL, ML, and Graph with NVIDIA RAPIDS
Accelerate data science workflows using GPUs and RAPIDS. Learn performance gains, easy migration, and new possibilities in ETL, ML, and graph analytics.
MLOps at Scale: Predicting Bus Departure Times Using 18,000 ML Models
Explore MLOps at scale through a case study on predicting bus departure times using 18,000 ML models. Learn practical insights for implementing large-scale machine learning projects.
MLOps and Automation Workshop - Bringing Machine Learning to Production in a Few Easy Steps
Learn to deploy ML/AI to production faster using MLOps technologies. Explore automation techniques and best practices for efficient machine learning operations.
Using Model Serving in Streaming Applications
Explore model serving approaches in streaming applications using Cloudflow framework. Learn to develop, orchestrate, and operate distributed streaming applications on Kubernetes efficiently.
Building an AI Capability in the United States Army
Explore the development of AI capabilities in the U.S. Army with insights from Chief Data Scientist LTC Isaac J Faber PhD.
Your First ML Model in Production - Considerations and Examples
Explore strategies for impactful data science projects, from identifying business objectives to deploying ML models in production. Learn real-time scoring, API integration, and lifecycle management with practical examples.
Algorithmic Decision Making - Exploring Practical Approaches to Liability, Fairness, and Explainability
Explore practical approaches to liability, fairness, and explainability in algorithmic decision-making with industry experts.
Applied Machine Learning in Healthcare - Practical and Legal Considerations
Explore practical and legal aspects of applying machine learning in healthcare, including data challenges, algorithm development, and ethical considerations.
Predicting and Preventing Customer Technical Issues Using Machine Learning and RPA
Predict and proactively resolve customer technical issues using machine learning and robotic process automation, improving service efficiency and customer satisfaction in telecom.
Metrics That Matter: Communicating ML Value to Stakeholders in Insurance
Explore creative strategies to gain stakeholder buy-in for ML projects in insurance, focusing on metrics that matter beyond traditional ROC scores.
The Algorithm is Not Enough: UX Meets Data Science
Explore the synergy between UX and data science, learning how to integrate user-centric design with data-driven insights for more effective product development.
Winning Your First 50 Enterprise Customers - Practical Strategies to Launch a ML Startup
Discover practical strategies for acquiring and scaling enterprise ML customers, from landing your first client to validating your business model and expanding to 50 customers.
Banorte's AI Journey - Yielding 3 Billion USD Revenue in 5 Years
Explore Banorte's AI transformation journey, yielding $3 billion in revenue over 5 years. Learn strategies for implementing high-ROI AI projects and avoiding transformational fatigue.
Serverless for Machine Learning Pipelines
Explore serverless architecture for ML/DL deployment, utilizing AWS services to organize scalable and affordable deep learning workflows for efficient model training and operationalization.
Principles of Good Machine Learning Systems Design
Learn key principles for designing effective machine learning systems, from project scoping to deployment and maintenance, with insights on team structuring and the ML production ecosystem.
Raising the Quality of Online Conversations with Machine Learning
Explore how The Washington Post uses machine learning to moderate online comments, enhancing digital discourse quality and addressing technical challenges.
Machine Learning Deployment and Product Integration - Best Practices and Lessons Learned
Explore best practices for deploying ML models and integrating them into core business functions. Learn from real-world experiences in on-demand inference deployment.
Accelerating Talent in AI - Overcoming Industry Challenges
Explore key obstacles in AI talent acquisition and strategies to overcome them, empowering academia, graduates, and businesses to bridge the industry-ready talent gap.
Enhancing Machine Learning Applications with Wisdom of the Crowd
Explore ensemble techniques to boost ML model performance, explainability, and usability for complex problems by leveraging multiple specialized models.
Machine Learning in Finance - Lessons Learned
Explore ML's impact on finance, its current capabilities, and best practices for implementation. Learn to identify opportunities and avoid pitfalls in trading and banking applications.