SUSE AI Community Guidance for Your Success
The end game is having a production-grade AI project that generates iterative insights that feed into business decisions—it all starts with data science and modeling. Predictive modeling that relies on machine learning will eventually lead to unexpected inferences that could have a major impact on the business and the services provided.
Ensure the right skills are available that can leverage AI and Machine Learning technology and eventually transform the business. Whether you need to develop these skills in-house, or hire in some data scientists, or get advice from outside consultants and suppliers—it’s pretty likely that your work force will need to adapt to handle this.
The process to achieve a production model is key to eventually improving efficiencies in business and IT operations. The goal here is to eliminate the complexity of the Artificial Intelligence infrastructure for core, cloud and edge through a holistic approach that spans from services to infrastructure to support.
AI/ML stack provides a guidepost that businesses can use in developing the right environment and models for AI projects. Once you have these building blocks and this end-to-end technology guidance, you can achieve an optimized infrastructure for your AI project that fits in your architectural environments.
SUSE AI Orchestrator
SUSE AI Orchestrator is a new cloud-native tool that translates a data model into the execution steps of an AI platform pipeline or workflow in an automated way.
Using SUSE AI Orchestrator automates pipeline or workflow across AI platforms, fosters collaboration between data scientists and AI operators, and deploys and monitors an entire AI platform on-premise or in the cloud.
K3ai—Edge infrastructure for AI
The K3ai project is building a solution based on Rancher K3s (Kubernetes) and popular AI tools and platforms. In its current form, K3ai supports Kubeflow pipelines, Tensorflow, NVIDIA GPU and more. It offers infrastructure for edge devices with full capability of a Kubernetes cluster—making it ideal for AI/ML containers.Learn More
SUSE AI Stack
When SUSE set out to create a new, indispensable AI/ML tool for its customers, we were certain that we didn’t want to create just another workspace/orchestrator/analysis tool.
What the wild west that is AI/ML needs more than anything else is a tool that returns control of the tools to the AI consumer and improves the chances of success for every AI/ML project. From the outset, in creating the SUSE AI Orchestrator we followed three primary guidelines:
- Ensure Data Scientists are able to stay focused on creating, analyzing, and refining their data models
- Return control of the AI Platforms, and the expenditures on them, to AI Operations
- Enable new avenues of collaboration between Data Scientists, AI Engineers, and AI Operators
SUSE Linux Enterprise High-Performance Computing
SUSE Linux Enterprise High Performance Computing (HPC) provides a platform for data analytics workloads such as artificial intelligence and machine learning. Fueled by the need for more compute power and scale, businesses around the world today are recognizing that an HPC infrastructure is vital to supporting the analytics applications of tomorrow. From the core to the cloud, SLE HPC provides SUSE-supported capabilities (i.e., Slurm for workload management) for today’s HPC environments.Learn More
Artificial Intelligence – Addressing the Challenges of Today's Data Scientists
Enterprises are turning to AI, machine learning and analytics to make the right inferences from that data. However, they are challenged to get their AI project into production while satisfying all the requirements for being deployed across multiple environments with security and manageability. SUSE has a better way. Learn how to reduce the complexity of the AI infrastructure through a holistic approach spanning services, infrastructure and support.Register Now
Artificial Intelligence—Addressing the Challenges of Today's Data
Recent events around the world have taught us that processing and interpreting volumes of data is important for both business continuity and enhancing end user experiences. Enterprises are turning to AI, machine learning and analytics to make the right inferences from that data. So in this session from the All Things Open conference, we’ll talk about how enterprises are challenged to get that important AI project into production, while satisfying all the requirements for being deployed across multiple environments with security and manageability.
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