GitOps for Machine Learning Pipelines
Explore GitOps for ML pipelines using Kubeflow on Kubernetes. Learn to leverage GitOps for training, inference, monitoring, and testing in ML lifecycles, with a demo on deploying Kubeflow components via Argo CD.
Machine Learning with Kubernetes
Explore MLOps with Kubernetes: Learn to build scalable ML pipelines using Kubeflow, optimize cluster design, and leverage Istio for enhanced observability and security in production environments.
Building Robust ML Production Systems Using OSS Tools for Continuous Delivery for ML
Learn to build robust ML production systems using open-source tools for continuous delivery, including workflow orchestration, data versioning, and model deployment and monitoring.
Integrating Feast Online Feature Store with KFServing
Explore integrating Feast online feature store with KFServing for efficient machine learning model serving, featuring insights from IBM experts on deployment and implementation.
MLOps Automation with Git Based CI-CD for ML
Explore MLOps automation using Git-based CI/CD for ML, covering challenges, collaboration, and streamlined deployment with cloud-native tools and Kubernetes.
Security of Machine Learning Systems
Explore security challenges in AI and ML systems, including adversarial attacks and MLOps. Learn to apply pen testing skills to ML, with demonstrations on manipulating image recognition and NLP models.
Batch Systems in Production with Kueue - Multi-Tenancy and Fungibility
Explore Kueue, a cloud-native job scheduler for Kubernetes, enabling multi-tenant batch systems with efficient resource management, fair use, and support for various workloads and frameworks.
Mastering LLM Delivery in Private Clouds: A Journey to Seamless Deployments with Kubernetes and OCI
Explore how Kubernetes and OCI artifacts simplify private LLM deployments, addressing data governance challenges and improving efficiency in artifact sharing between model developers and consumers.
Beyond Prototypes: Production-Ready ML Systems with Metaflow and Argo Project
Explore production-ready ML systems using Metaflow and Argo, focusing on rapid improvement and maintenance. Learn from Netflix's experience in composing and orchestrating advanced ML systems.
Discovering Chicago Film Sets with MLOps in Kubernetes
Explore Chicago film sets using Kubernetes and MLOps. Learn to integrate CNCF projects, design patterns, and best practices for data-driven applications while discovering popular shooting locations.
MLOps: Why DevOps Solutions Fall Short in the Machine Learning World
Explore MLOps challenges, differences from DevOps, and solutions for efficient machine learning development. Learn about GitLab's approach and the emerging field of LLMOps.
Cloud-Native MLOps Platform for Python and ML Engineers
Discover how Jina MLOps platform simplifies building and deploying AI applications in Python to the cloud, enabling engineers to focus on logic while handling infrastructure complexity.
MLOps - Machine Learning Production Grade Deployment Techniques - One-Shot Tutorial
Learn MLOps techniques for production-grade machine learning deployment across AWS and Azure platforms, including Elastic Beanstalk, EC2, Web Apps, and Docker with GitHub Actions.
Enabling AI Transformation in Telecom - MLOps Infrastructure and AI Command Centre
Explore AI transformation in telecom: MLOps infrastructure, AI Command Centre, and data science strategies for customer satisfaction, business growth, and digital innovation.
The Challenges of Deploying Real-time AI for Finance and How Open Source Can Help
Exploring challenges in deploying real-time AI for finance and how open-source solutions like feature stores can address them. Includes case studies and resources for implementing MLOps in fintech.
Horovod - Distributed Deep Learning for Reliable MLOps
Explore Uber's Horovod framework for distributed deep learning, covering its adoption, techniques, and integration with Spark for efficient large-scale model training and deployment.
Securing ML Workloads with Kubeflow and MLOps - Pwned By Statistics
Learn to secure machine learning workloads using Kubeflow and MLOps. Explore techniques like model extraction, hidden data attacks, and leakage detection to protect your ML pipelines and models.
Symptom-based Alerting for Machine Learning - Lessons from 30+ Use Cases
Practical guide for ML monitoring: essential metrics, prioritization, and tool selection. Insights from 30+ ML use cases to detect issues in real-time and overcome traditional software monitoring limitations.
Navigating the MLOps Journey - Key Considerations for Successful Implementations
Explore essential MLOps components for streamlining machine learning model development, deployment, and maintenance. Learn about infrastructure, data management, and team roles to enhance performance and foster innovation.
GenOps: Building an MLOps Platform to Support GenAI Workloads with Open-Source and Kubeflow
Explore building an end-to-end MLOps platform on GKE using open-source tools like Kubeflow to support Generative AI models, with practical tips and a demo for leveraging these technologies.
Open Source DataOps and MLOps Strategies
Demystifying data challenges: interoperability, standardization, and vendor lock-in. Strategies for implementing DataOps and MLOps infrastructures using open source technologies.