Oracle Cloud Infrastructure (OCI) Data Science is a fully managed platform for teams of data scientists to build, train, deploy, and manage machine learning (ML) models using Python and open source tools. Use a JupyterLab-based environment to experiment and develop models. Scale up model training with NVIDIA GPUs and distributed training. Take models into production and keep them healthy with MLOps capabilities, such as automated pipelines, model deployments, and model monitoring.
Combine the power of large language models (LLMs) and retrieval-augmented generation (RAG) with your data to easily query diverse enterprise data sources.
Building a machine learning model is an iterative process. Learn about every step, from data collection to model deployment and monitoring.
Gain access to automated workflows for building models. Operationalize ML more easily with reusable jobs and end-to-end orchestration for the ML lifecycle. Run distributed, high performance workloads with access to lower-cost GPUs.
Expect the best of ML on Oracle through major partnerships, such as Anaconda. Bring in models, data, and code in the format you need.
Benefit from white glove treatment for strategic ML partnerships. Oracle has data scientists on staff, dedicated to ensuring your organization’s success.
Identify risk factors and predict the risk of patient readmission after discharge by creating a predictive model. Use data, such as patient medical history, health conditions, environmental factors, and historic medical trends, to build a stronger model that helps provide the best care at a lower cost.
Use regression techniques on data to predict future customer spend. Examine past transactions and combine historical customer data with more data on trends, income levels—and even factors such as weather—to build ML models that determine whether to create marketing campaigns to keep current customers or to acquire new customers.
Build anomaly detection models from sensor data to catch equipment failures before they become a more severe issue or use forecasting models to predict end-of-life for parts and machinery. Increase vehicle and machinery uptime through machine learning and monitoring operations metrics.
Prevent fraud and financial crimes with data science. Build a machine learning model that can identify anomalous events in real time, including fraudulent amounts or unusual types of transactions.
Tzvi Keisar, Director of Product Management
Generative AI has captured the minds of everyone around the world, emerging as a beacon of innovation across all industries. However, the process of harnessing the power of generative AI to solve specific problems isn’t simple, especially in the enterprise. Building or fine-tuning foundation models for domain expertise can be complex and resource-intensive. To simplify the use of LLMs and make them more accessible to a wider audience, Oracle is releasing a no-code solution for harnessing them in the enterprise: AI Quick Actions for OCI Data Science.Read the complete post