Data Science Service
Oracle Cloud Infrastructure (OCI) Data Science is a fully-managed platform for teams of data scientists to build, train, deploy, and manage machine learning 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.
Cloud Native and AI Day | Monday, March 20 | Free—Join in person or online
Discover best practices, tips, and recommended toolsets to simplify the adoption of cloud native and AI technologies to fast-track your next app innovation. Join us in person or online.
Building a machine learning model is an iterative process. Learn about every step, from data collection to model deployment and monitoring.
Discover how to develop a strategic investment model for scaling AI. This Gartner report outlines recommendations for the right AI investment mix and provides a formula for calculating ROI.
Artificial intelligence is rapidly becoming integrated across business functions. IDC explores best practices and recommendations for enterprise AI.
Why OCI Data Science?
-
High-performance data science
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.
-
Open platform
Expect the best of ML on Oracle through major partnerships, such as Anaconda. Bring in models, data, and code in the format you need.
-
Best-in-class support
Benefit from white glove treatment for strategic ML partnerships. Oracle has data scientists on staff, dedicated to ensuring your organization’s success.
Data science customer successes and partnerships
Data Science use cases
-
Healthcare: Patient readmission risk
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.
-
Retail: Predict customer lifetime value
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—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.
-
Manufacturing: predictive maintenance
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.
Finance: Fraud detection
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.
Getting machine learning models to production and beyond with MLOps on OCI
Tzvi Keisar, Senior Principal Product Manager
Training models to generate accurate predictions is a complex task that requires extensive expertise in the field of data science. However, even after the model is built, the journey isn’t over. You have another important task to perform: getting the model to generate predictions on new data in real life, often called “model productionalization.” This task is just as complex as building the model. In fact, you might have read articles about the staggering percentage of AI projects that fail when trying to deploy into production.
Read the complete post