Applied Hydrodynamic Modelling
Explore hydrodynamic modeling applications in environmental planning and structural design through case studies of the Coorong estuary and Brisbane River, highlighting unique approaches and key lessons learned.
How to Deploy an ML App on Azure App Service
Learn to develop and deploy an interactive machine learning app on Azure, covering UI creation, model development, and CI/CD configuration for seamless production deployment.
Cloud Directions, MLOps and Production Data Science
Explore cloud directions, MLOps, and production data science with Prof. Hellerstein. Learn key challenges, lifecycle management, and innovative solutions for scaling data-intensive applications in the cloud.
Scaling AI at H&M - Democratizing and Accelerating AI Usage
H&M's evolving AI platform democratizes and accelerates AI usage across the group, focusing on speed to production, data abstraction, feature store, and pipeline orchestration for efficient model management and development.
Lessons Learned from Big Data and AI/ML Collaboration for Giant Hogweed Eradication
Explore big data and AI/ML collaboration for invasive plant eradication, covering challenges, architecture, data flow, and lessons learned in scalable machine learning systems.
Collaborating on a Currency Reference Data Model and Standard - Panel Discussion
Explore collaborative efforts in creating a standardized currency reference data model for the financial industry, discussing challenges, solutions, and future steps.
Bringing CI/CD Practices to Machine Learning with MLOps
Learn how to apply CI/CD practices to machine learning pipelines, enhancing model development from data exploration to deployment for better, faster results in MLOps.
Turbo Charge Your Data Science Workflow with Microsoft Fabric
Discover how Microsoft Fabric's SaaS platform enhances data science workflows, leveraging MLflow and Semantic Link for advanced machine learning model development.
The Key to Unlock AI Accuracy Through Feature Generation
Discover how sophisticated feature generation acts as the cornerstone for developing accurate and effective AI models, illustrated through real-world examples from DeepMind and global corporate implementations.
Practical Advice for Building Machine Learning Applications - Lecture 28
Gain insights into practical strategies and best practices for implementing real-world machine learning systems, from system design to deployment considerations.
MLOps with Flyte - Challenges and Solutions in Machine Learning Operations
Explore MLOps challenges and solutions using Flyte, an open-source orchestration tool. Learn best practices for efficient ML model development and deployment at scale.
Balancing Speed and Accuracy in Model Development
Explore strategies for optimizing machine learning model development, balancing speed and accuracy. Learn to assess business objectives, improve data quality, and select efficient features and models.
Keras Preprocessing Layers
Learn to streamline model development with Keras Preprocessing Layers. Build end-to-end models, transform inputs, and improve performance using tf.data and multi-worker training.
Building Our First Deep Learning App From Scratch - Hello MNIST
Develop a deep learning app for digit recognition using MNIST dataset, covering model creation, app development, and publishing on the Google Play Store.
Build an Ecosystem, Not a Monolith
Explore the potential of specialist AI models in creating a more efficient and effective ecosystem, addressing technical challenges and collaborative development opportunities.
AIUK 2022 Workshop - Equadratures: The Open Source Model Development Platform
Explore Equadratures, an open-source platform for model development. Learn about polynomials, variable projection, and applications in this interactive workshop from the Alan Turing Institute.
Cloud-Based Convolution Neural Network Ensembles for Computer Vision Counting
Explore ensemble CNN techniques for computer vision counting, featuring cloud computing evaluation and strategies for achieving low RMSE scores in competitive settings.
How Bloomberg Uses AI to Speed Up Over-the-Counter Trading
Explore how Bloomberg leverages AI and Kubernetes to enhance over-the-counter trading efficiency, focusing on detecting offers in group chats and streamlining negotiations for traders.
Enterprise MLOps: Insights from a Lead ML Engineer
Insights into enterprise MLOps from a lead engineer, covering infrastructure, model development, and anti-money laundering applications in the banking sector.
Secure AI: Integrating OWASP Principles in Machine Learning Model Development
Explore integrating OWASP principles into machine learning model development to address vulnerabilities, ensure data privacy, and safeguard AI systems from potential threats, ensuring robustness and confidentiality.
Get to Know PyTorch - A Cutting Edge Open Source AI Framework for Deep Learning
Explore PyTorch, a leading open-source AI framework. Learn deep learning fundamentals, PyTorch basics, and recent developments. Discover how to contribute and leverage PyTorch for creating neural network models.