Oracle Cloud Infrastructure (OCI) Data Science is an end-to-end machine learning (ML) service that offers JupyterLab notebook environments and access to hundreds of popular open source tools and frameworks. Build and train ML models with NVIDIA GPUs, AutoML features, and automated hyperparameter tuning. Deploy models as HTTP endpoints or use Oracle Functions. Manage models through version control, repeatable jobs, and model catalogs.
Data scientists can access and use any data source in any cloud or on-premises. This provides more potential data features that lead to better models.
Integration with OCI Data Flow provides an easy interface to create and run large-scale Spark jobs from the Data Science environment. In addition, a PySpark environment enables iterative development of Data Flow applications in notebook sessions.
Streamline exploratory data analysis workflows with cutting-edge data profiling capabilities, smart visualizations, and data preparation tools.
OCI Data Science supports the data scientist’s favorite open source data manipulation tools such as Pandas, Dask, and Numpy, as well as popular open source visualization tools such as Plotly, Matplotlib, and Bokeh to help data scientists explore data.
Built-in, cloud-hosted JupyterLab notebook environments allow data science teams to build and train models using a familiar user interface.
OCI Data Science provides familiarity and versatility for data scientists, with hundreds of popular open source tools and frameworks. Build machine learning models with TensorFlow or PyTorch, or add frameworks of choice.
With NVIDIA GPUs, data scientists can build and train deep learning models in less time. When compared to CPUs, performance speedups can be 5 to 10 times faster.
The Accelerated Data Science library supports Oracle AutoML, as well as open source tools such as H2O 3 and auto-sklearn. AutoML offers adaptive sampling, automated feature selection, algorithm selection, and hyperparameter tuning. AutoML generates an accurate model candidate to save the data scientist significant time.
Save time and effort by tuning models with automated hyperparameter tuning using the ADS Tuner feature.
Automated evaluation generates a comprehensive suite of metrics and visualizations to measure model performance against new data and to compare model candidates. This makes it easier for data scientists to produce high-quality models.
Automated model explanation includes global and local explanations with specific model predictions for the overall behavior of a model. For model consumers, automated model-agnostic explanations improve understanding and trust, address regulatory needs, and increase the speed of machine learning adoption.
Deploy machine learning models as HTTP endpoints for serving model predictions on new data in real time. Simply click to deploy from the model catalog, and OCI Data Science handles all infrastructure operations, including compute provisioning and load balancing.
Easily deploy data science models as Oracle Functions—a highly scalable, on-demand, serverless architecture in Oracle Cloud Infrastructure.
Team members use the model catalog to preserve and share completed machine learning models. The catalog stores the artifacts and captures metadata around the taxonomy and context of the model, hyperparameters, definitions of the model input and output data schemas, and detailed provenance information about the model origin including the source code and the training environment where the model was trained.
Leverage prebuilt, curated conda environments to address a variety of use cases and like NLP, graph analytics, Spark, and NVIDIA RAPIDS. Publish custom environments and share with colleagues, ensuring reproducibility of training and inference environments.
Data scientists can move from experimentation to production by operationalizing data science tasks with jobs. Jobs are used to automate repeatable tasks such as retraining and redeploying models.
Data scientists can connect to their organization’s Git repository to preserve and retrieve machine learning work.
Customers use OCI Data Science to improve data science collaboration and to save time and costs building machine learning models.
Use Python, the most popular language for data science, with JupyterLab and hundreds of open source libraries and frameworks like Dask, scikit-learn, and XGBoost. Or, install libraries of choice for ultimate flexibility.
Accelerated Data Science (ADS) is an end-to-end Python library covering the entire data science lifecycle, making it faster and easier to produce high-quality models.
With self-service, on-demand infrastructure, data scientists select the amount of compute and storage resources they need to tackle projects of any size, without worrying about provisioning or maintaining infrastructure.
Team-based security policies allow data scientists to include team members in projects. These policies control access to models, code, and data, facilitating collaboration while protecting sensitive work. Security controls are fully integrated with Oracle Cloud Infrastructure Identity and Access Management.
Access dozens of tutorial and example notebooks, covering topics from how to access data to the math behind model explanation techniques. Get a jumpstart on tackling different business problems with proven methodology and implementation tips.
Product |
Comparison Price ( /vCPU)* |
Unit Price |
Unit |
Compute - Standard - E2 |
OCPU per hour |
||
Compute - Virtual Machine Standard - X7 |
OCPU per hour |
||
Compute - Standard - E3 - OCPU |
OCPU per hour |
||
Compute - Standard - E3 - Memory |
Gigabyte per hour |
||
VM.GPU2.1 (NVIDIA P100) |
GPU per hour |
||
VM.GPU3.x (NVIDIA V100 Tensor Core - 16 GB) |
GPU per hour |
||
Block Volume Storage |
Gigabyte storage capacity per month |
||
Block Volume Performance Units |
Performance units per GB/month |
Product |
Comparison Price ( /vCPU)* |
Unit Price |
Unit |
Object Storage - Storage |
Gigabyte storage capacity per month |
Product |
Comparison Price ( /vCPU)* |
Unit Price |
Unit |
Compute - Virtual Machine Standard - X7 |
OCPU per hour |
||
Load Balancer Base |
Load Balancer hour |
||
Load Balancer Bandwidth |
Mbps per hour |
||
Block Volume Storage |
Gigabyte storage capacity per month |
||
Block Volume Performance Units |
Performance units per GB / month (10 VPUs at $0.017 for balanced performance) |
Product |
Comparison Price ( /vCPU)* |
Unit Price |
Unit |
Compute - Virtual Machine Standard - X7 |
OCPU per hour |
||
VM.GPU2.1 (NVIDIA P100) |
GPU per hour |
||
VM.GPU3.x (NVIDIA V100 Tensor Core – 16 GB) |
GPU per hour |
||
Block Volume Storage |
GB storage capacity/month |
||
Block Volume Performance Units |
Performance units per GB / month (10 VPUs at $0.017 for balanced performance) |
*To make it easier to compare pricing across cloud service providers, Oracle web pages show both vCPU (virtual CPUs) prices and OCPU (Oracle CPU) prices for products with compute-based pricing. The products themselves, provisioning in the portal, billing, etc. continue to use OCPU (Oracle CPU) units. OCPUs represent physical CPU cores. Most CPU architectures, including x86, execute two threads per physical core, so 1 OCPU is the equivalent of 2 vCPUs for x86-based compute. The per-hour OCPU rate customers are billed at is therefore twice the vCPU price since they receive two vCPUs of compute power for each OCPU, unless it’s a sub-core instance such as preemptible instances. Additional details supporting the difference between OCPU vs. vCPU can be accessed here.
Tzvi Keisar, Senior Principal Product Manager, Oracle
Oracle Cloud Infrastructure (OCI) Data Science released a new feature called Model Deployment to enable the serving of machine learning models as HTTP endpoints and provide real-time scoring of data.
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