AI-Ready Data Infrastructure for Real-Time Sensor Data Analytics on the Edge
Explore practical strategies for building edge-based real-time sensor data analytics infrastructure, using race strategy analytics as a case study to demonstrate cross-industry applications.
Building an Enterprise Scale MLOps Platform - From Development to Deployment
Discover how NatWest built a streamlined MLOps platform to enhance productivity, automate ML workflows, and ensure security compliance in enterprise-scale machine learning operations.
Machine Learning System Endeavors - From Design to Production
Explore the journey of implementing a video processing AI system, from initial design through production challenges, focusing on ML pipeline management, bug tracking, and real-world impact in smart buildings.
Combining Behavioral Research and NLP to Combat Gender Biases
Discover how AI and behavioral research combine to identify and reduce gender biases, exploring MLOps practices, user interaction learning cycles, and responsible AI deployment on Kubernetes.
Что я хотел бы знать об MLOps год назад - Практический опыт внедрения Machine Learning
Discover essential MLOps insights through real-world experiences at Kolesa Group, covering ML model deployment, Docker, Kubernetes integration, and practical monitoring strategies for machine learning systems.
DevOps to MLOps: Scaling ML Models to 2 Million+ Requests per Day
Explore scaling ML models to handle millions of daily requests, covering MLOps fundamentals, cloud-agnostic architecture, and real-world production challenges in eKYC SaaS APIs.
Using Quix, Hugging Face, and InfluxDB for Anomaly Detection
Explore anomaly detection using Quix, Hugging Face, and InfluxDB. Learn about data pipelines, time series databases, and MLOps for effective machine learning implementation.
MLOps: From Jupyter to Production - Conf42 ML 2024
Learn to transition machine learning projects from Jupyter notebooks to production environments, covering MLOps best practices and real-world implementation strategies.
GitOps for MLOps - Automating Multi-Cluster Kubernetes Environments
Explore challenges and real-world examples of managing multi-region Kubernetes clusters for MLOps at scale, focusing on disaster tolerance and performance optimization.
MLOps and LLMOps: From Prompt to Observability - Lecture 1
Explore MLOps, AI observability, and LLMOps integration. Gain insights on implementing AI stores for enhanced management and governance of traditional and generative AI solutions across organizations.
AI Demand Forecasting: Building a Scalable Machine Learning System for Thousands of Products
Explore ASOS's journey in building a scalable AI demand forecasting system, addressing challenges in ML modeling, data processing, and integration for improved stock management.
A Guide to Putting Together a Continuous ML Stack
Implement continuous ML training by automating pipelines, detecting performance issues, and triggering model updates with fresh data for improved MLOps maturity.
From Silo to Collaboration - Building Tooling to Support Distributed ML Teams at Twitch
Explore Twitch's ML team structure, challenges, and solutions for distributed collaboration. Learn about feature stores, orchestration systems, and fostering a collaborative ML culture.
Transforming the Retail Industry with Transformers
Explore how transformers and language models revolutionize retail data processing, overcoming challenges in deployment and automation for improved business outcomes.
Build High Performance MLOps With ML Monitoring and AI Explainability
Explore MLOps best practices, focusing on ML monitoring and AI explainability. Learn to identify model failures, address operational issues, and streamline model deployment using cutting-edge techniques.
L'Oréal Canada's Bespoke Data Science Strategy Framework
Discover L'Oréal Canada's data science strategy framework. Learn to assess maturity, identify high-value projects, build ML teams, and implement effective practices in retail and e-commerce.
Cost Reduction Methods for Machine Learning in Production
Discover effective strategies to reduce costs when deploying machine learning models in production, focusing on optimizing models and infrastructure for cost-efficient MLOps.
Rise of Responsible MLOps
Explore industry-wide developments in responsible AI, focusing on fairness, transparency, privacy, and robustness in MLOps for ethical decision-making systems.
MLOps vs ModelOps - What's the Difference and Why You Should Care
Explore the differences between MLOps and ModelOps, focusing on continuous monitoring, automated remediation, and feedback loops for AI and analytical models.
AI Inference Workloads - Solving MLOps Challenges in Production
Overcome MLOps challenges in production inference workloads. Learn strategies for improving throughput, reducing latency, and maximizing GPU utilization in AI deployments.
How to Automate Machine Learning with GitHub Actions
Automate machine learning workflows using GitHub Actions to speed up development cycles and streamline model deployment.