Do you know that Kubeflow 1.0 has just been recently released by the Kubeflow Community? Kubeflow is the open source project integrating TensorFlow, PyTorch and other machine learning capabilities into a cohesive tool, from data ingestion, through machine learning, and even inferencing.
This week, we’ll discuss the three major gaps that prevent enterprise from taking advantage of AI/ML and how to bridge them – with Cisco’s help.
Cisco believes that IT must be intimately involved with the data science projects to ensure that data governance, both on-premise or on-cloud, be properly administered and managed.
Applications are the foundation of your business. As IT professionals, you need to provide the right infrastructure for these apps to perform as expected and deliver the best experience possible.
Cisco believes that it is critical for IT to be part of the data science team, and that integrated teams can make a business impact with data science.
Data pipelines constantly change to accommodate new data sources. Unclear requirements and a dynamic environment are actually essential to development of artificial intelligence and machine learning for a competitive advantage.
Data Scientist charged with the responsibility of mining value out of numerous data sources, often feel that they need ever more speed to do their jobs quickly. To support them, IT teams are always looking for performance, scale, and flexibility.
Cisco bridges the gap between data scientists and IT by working with artificial intelligence and machine learning help optimize data pipeline infrastructure.
If machine learning boils down to data management and using the machine learning packages, what challenges are enterprise facing today that make use of that data?
