Abstract: The implementation of Machine Learning Operations (MLOps) has become an essential component of modern enterprises seeking to harness the power of machine learning to drive innovation and business value. MLOps involves the integration of machine learning models into production workflows and the establishment of best practices for the development, deployment, monitoring, and maintenance of these models. As organizations increasingly adopt MLOps practices, it is vital to understand the foundational requirements, known as """"tablestakes,"""" and the imperatives that ensure successful MLOps implementation.
We will explore the critical tablestakes and imperatives for effective MLOps, including considerations for model versioning, automated deployment pipelines, monitoring, and governance. We will discuss the challenges that organizations face in operationalizing machine learning models and the best practices that address these challenges. We will explore the options available on Oracle Cloud Infrastructure (OCI) that facilitate MLOps practices. Specifically, we will introduce OCI Data Science (OCI DS), a cloud-based platform that provides a comprehensive suite of tools and services for developing, training, and deploying machine learning models. OCI DS enables seamless collaboration between data scientists, engineers, and other stakeholders, while ensuring scalability, security, and cost-effectiveness.
We will also delve into how OCI enables large-scale MLOps through the integration of Kubeflow, an open-source machine learning platform that provides end-to-end workflows for managing and deploying machine learning models on Kubernetes. Kubeflow's modular design and compatibility with OCI allow organizations to build robust MLOps pipelines that streamline the entire machine learning lifecycle. We will discuss the options available for leveraging NVIDIA technologies on OCI for accelerated machine learning workloads. NVIDIA's advanced GPUs, combined with OCI's high-performance infrastructure, provide the computational power required for training and inference on large and complex machine learning models.
By the end of this presentation, attendees will gain a deeper understanding of the diverse set of tools and services available on OCI for implementing MLOps at scale. The knowledge gained will equip participants with the insights needed to enhance their organization's machine learning capabilities, optimize operational efficiency, and drive data-driven decision-making.
Bio: Allen is a Principal Machine Learning Architect and AI Researcher working for Oracle Cloud Infrastructure.