Enterprise data often resides on-premises due to data gravity and regulatory requirements, while AI and ML development demand scalable cloud resources. Now, data teams no longer need to compromise between infrastructure. A hybrid approach allows organizations to leverage their existing on-prem data lakes while accessing cloud agility for tasks like model training and model development.
Using Cloudera’s hybrid platform, data preparation can occur on-premises, while bursty workloads such as model training can move to the cloud. Once trained, the model can be deployed on-prem or in the cloud, depending on the use case. This approach aligns with the modern machine learning and AI use cases flooding the market, such as deploying an LLM for chatbots.
Join us to learn how:
Hybrid cloud architecture enables AI/ML workloads to span on-premises and cloud environments
To prepare data on-premises while using the cloud for rapid model training and experimentation
Cloudera Accelerators for ML Projects let you go from concept to production faster than ever
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