Data Science Service

Flexible data access

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.

Data preparation at scale with OCI Data Flow

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.

Automated data profiling and preparation

Streamline exploratory data analysis workflows with cutting-edge data profiling capabilities, smart visualizations, and data preparation tools.

Open source data transformation and visualization 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.

• Connect to multiple data sources using Accelerated Data Science (17:23)

JupyterLab interface

Built-in, cloud-hosted JupyterLab notebook environments allow data science teams to build and train models using a familiar user interface.

Open source machine learning frameworks

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.

• Introduction to JupyterLab in OCI Data Science (21:38)

Powerful hardware, including graphics processing units (GPUs)

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.

Automated machine learning (Oracle AutoML)

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.

Automated hyperparameter tuning

Save time and effort by tuning models with automated hyperparameter tuning using the ADS Tuner feature.

• GPU VM shapes now available in OCI Data Science
• Accelerate machine learning use NVIDIA GPUs with RAPIDS
• Optimizing estimators and the ADSTuner

Model evaluation

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.

Model explanation

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.

• Interpretable machine learning (29:43)

Model catalog

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.

Reproducible environments

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.

Reusable jobs

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.

Version control

Data scientists can connect to their organization’s Git repository to preserve and retrieve machine learning work.

• New Conda Environment feature available in OCI Data Science