🎓 What will you learn?
In this virtual session, discover how to create repeatable and scheduled flows that automate notebook, data refinery, and machine learning pipelines: from data ingestion to model training, testing, and deployment. Explore an intuitive user interface and state-of-the-art data science tools. Combined, you can create continuous integration / continuous development pipelines for AI.
We will explore Watson Studio Pipelines, built off of Kubeflow Pipelines on the Tekton runtime – fully integrated into the Watson Studio platform.
- For DevOps folks, Pipelines taps into the Kubernetes ecosystem, leveraging its scalability and containerization principles.
- For Data scientists and MLOps practitioners, Pipelines offers a Builder UI to define, deploy and track Pipelines execution.
- For DataOps folks, Pipelines brings in ETL bindings to participate more fully in collaboration with peers by providing support for multiple ETL components and use cases.
🤖 Accelerate your Automation, Data & AI Journey!
Interested in Machine Learning and Data Science? Discover automation like you’ve never seen it before: in customer service, the AI lifecycle, document processing and classification, application monitoring for cloud-native applications, and Robotic Process Automation! Register for free and get ready to learn about all these amazing technologies with live-demos in this series of virtual events. Get hands-on and build your own app afterwards.
👍 What’s the benefit for you? In the last session of this event series you will have the opportunity to bring your own use case / problem / data / application / processes / documents / … and pitch it in 10 – 15 minutes to our IBM technical staff. They will give you feedback, provide technical support, and perhaps even become part of your project!
Rafał Bigaj, Software Architect, IBM
Rafał Bigaj is a Software Architect with long successful record of building and leading teams. Skilled staff trainer and motivator. Broad and practical knowledge in the area of cloud computing, machine learning and distributed systems development.