The last few years have seen an increased adoption of cloud infrastructure, which has in turn led to a growth in large-scale distributed architectures in data centers to accommodate cloud resource elasticity and resiliency better. Selecting the right approach to build secure, scalable, and reliable cloud infrastructure within a budget is always a challenge. This text focuses on offering practical solutions for designing and building a secure, scalable, and reliable cloud-based infrastructure where auto-scaling and multi-region deployments are the two key approaches to offer high availability. It covers designing secure and scalable microservices using cloud platforms. The content will provide an understanding of public cloud architecture, the design of microservices running on the cloud, and also the design patterns used in the cloud era. With real-world examples, you will learn how microservices can enable scalable distributed systems. Furthermore, you will be walked through multi-region deployments, auto-scaling, and traffic management in cloud environments, using a sample environment setup and useful tips and tricks for monitoring. Finally, you will see a mock implementation of cloud infrastructure on-premise for a private cloud or single-node cloud. By the end of this text, you will be able to build, manage, and deploy a highly scalable and reliable cloud-ready solution [1].

Machine learning operations (MLOps) comprises the practices, methods, and tooling that facilitate the deployment of reliable ML models in production environments. While many aspects of cloud data platforms are designed to enable reliability, only some managed ML services support the MLOps goals of continuous integration, continuous delivery, data lineage tracking, associated reproducibility, governance, and security. Furthermore, reliability encompasses not only the fulfillment of service-level objectives, but also systematic monitoring, alerting, and incident response automation. Architectural patterns are proposed to enable reliable deployment in cloud data platforms, focusing on the implementation of continuous integration and testing pipelines for ML models and the formulation of continuous delivery and rollout strategies. Continuous integration pipelines reduce the risk of regressions and ensure sufficient model performance at the time of deployment, while continuous delivery pipelines enable rapid updates to production models within acceptable risk profiles. The landscape of publicly available MLOps frameworks, tools, and services is also examined, emphasizing the pros and cons of established and rising solutions in containerization, orchestration, model serving, and inference. Containerization and orchestration contributes to the building of reliable deployment pipelines in cloud data platforms, whether general-purpose tools (e.g. Docker and Kubernetes) or solutions tailored for ML workloads. Containerized serving frameworks designed for high-throughput, low-latency inference can benefit a wide range of business applications, while auto-scaling and model versioning capabilities enhance the ease of use of cloud-native ML services.