
Discover key strategies for successful application transformation, focusing on innovative patterns that drive efficiency and modernization in software development.

Explore Docker's role in Data Science, Machine Learning, and AI, including GPU processing techniques and Industrial AI demos, presented by experts from Baker Hughes.

Explore NBCUniversal's data-driven approach to audience targeting using Apache Mesos, DC/OS, Spark, Kafka, and Cassandra for real-time analytics and personalized data services.

Discover how Astronomer transitioned from AWS-managed services to open-source solutions, enhancing flexibility and independence in their data engineering platform.

Explore how to enhance machine learning development using Azure and containers in Visual Studio Code, boosting efficiency and streamlining workflows.

Learn to optimize ML projects using Conda and Docker. Explore challenges, solutions, and best practices for efficient development and deployment workflows.

Learn to build and deploy a customer churn prediction model using Streamlit and Docker. Discover how to package machine learning models into interactive applications for effective customer retention strategies.

Overview of Fast Data systems, their deployment on Mesos and DC/OS, and their impact on Big Data processing. Learn about the evolving landscape of data pipelines and efficient operational strategies.

Learn effective strategies for identifying, attracting, and hiring top DevOps talent. Gain insights into the DevOps role, essential skills, and industry-specific recruitment techniques.

Explore the journey of operationalizing data science workloads at scale using Kubeflow and GitOps, from initial challenges to a standardized approach for ML experiments, API creation, and batch predictions delivery.

Explore how OCI's artifact distribution specification addresses ML asset management challenges, focusing on consistency, efficiency, provenance, and governance in Bloomberg's Data Science Platform.

Explore Uber's journey in containerizing Hadoop infrastructure, addressing technical challenges, designing zero-downtime migration, and transforming team culture for efficient large-scale operations.

Explore automated ML model evaluations on Kubernetes, focusing on MLSecOps practices for enhanced security and efficiency in both online and offline scenarios.

Explore how Databricks on Google Cloud leverages GKE for scalability, enabling AI/ML infrastructure to handle growing demands. Learn from NEAR's experience and discover extensive integrations with Google Cloud services.

Structure data science projects using Docker for seamless model deployment. Learn advanced techniques, explore code, address vulnerabilities, and weigh pros and cons of Docker in data science workflows.

Discover the future of Red Hat's containerized integration solution beyond Fuse 7 and learn how to modernize Fuse 6 workloads for upcoming needs.

Explore forensic container checkpointing in Kubernetes, learning tools and methods to analyze checkpoints and extract crucial information for uncovering potential malicious activity.

Explore federated analysis for germline variant interpretation in cancer genomics, focusing on privacy-preserving data sharing techniques and overcoming challenges in workflow security and data standards.

Discover how Cilium enhances container networking performance for AI workloads, exploring its implementation in Sokovan orchestrator and its advantages over Docker's overlay network for improved throughput and latency.

Master end-to-end container testing with Cogate, a powerful tool that streamlines Kubernetes deployments, handles cross-project dependencies, and enables deployment previews through automated gating systems.

Discover how to efficiently manage multiple OpenStack clouds using Kubernetes and GitOps automation, focusing on scalable deployment strategies and operational best practices for containerized environments.