By Neil Kumar, Senior Product Manager at Ocient
In today’s fast paced business climate, organizations must make strategic and actionable decisions quickly to keep up with the competition. Data is an essential element in making these critical decisions and the ability to gather, categorize, analyze, and interrogate that data can be a core differentiator to identifying the right next business steps. A critical component in this process is the ability to support machine learning (ML) in accordance with the unique requirements of technology ecosystems and end users.
At scale, this can be challenging for organizations for several key reasons:
- Data extraction introduces latency and reliability issues. The first step to any ML process is getting access to your data — which is most likely in a database, data warehouse, or data lake — extracting and transforming that data, and then loading it into a separate ML platform. All of these steps introduce latency and cost, and may degrade the value or accuracy of the data by the time it’s ready for modeling.
- Security and control concerns around data replication and access. In cases where user access controls (UACs) must be architected tool by tool, or aren’t supported at all, data security and control can hinder an organization’s ability to do more with its data. In addition, moving data between platforms may introduce more copies that must be managed.
- Rising costs of moving and processing data continuously. The data extraction and integration costs for continuous ML at scale can quickly get out of hand, especially as organizations look to leverage machine learning and AI across more parts of their businesses.
Leverage OcientML to Do More Data Science
OcientML is an innovative offering designed to tackle these challenges by shifting the ML stack into the database, eliminating the need to extract, transform, or load data to a separate platform. As a native component of the Ocient Hyperscale Data Warehouse™, OcientML enables teams to execute full volume, full fidelity data science activities directly within the database, spending more time on the science and less on data preparation, movement, and loading. In this blog, we will introduce you to OcientML, its features and benefits, and some applicable use cases for leveraging Ocient for scientific computing at scale.
OcientML reduces the time and complexity of setting up and managing a separate machine learning infrastructure, allowing users to build, update, and train ML models in the Ocient Hyperscale Data Warehouse leveraging large historical volumes of data alongside their latest up-to-date datasets.
With OcientML, users can control costs and improve business decisions by performing machine learning operations on the same platform used for other general and reporting needs. This enhanced functionality results in numerous benefits, including:
- Increased model accuracy by allowing full-resolution interaction with historical and up-to-date data, without downsampling or simplifying datasets
- Maximized speed and reduced iteration time by eliminating the need to switch between applications or wait for the data to arrive in another location to support machine learning capabilities
- Streamlined ML operations by simplifying the ML stack, enabling users to manage a single system for SQL analytics and machine learning, versus multiple point solutions
When it comes to work management and security, OcientML benefits from the same robust security and job-running capabilities as provided by the Ocient Hyperscale Data Warehouse. These include the ability to manage competing workloads with sophisticated workload management, ensuring efficient and effective processing of critical jobs and datasets. They can also be used to ensure that both users and models are secured effectively, and access is based on user profiles and simplified access controls.
As for the ML capabilities themselves, OcientML offers a repository of common ML models that can be used as-written or adapted to meet specific needs. These algorithms cover common structured and semi-structured data, and support a variety of model types including regression, classification, feedforward neural networks, and more. With the support of the Ocient Hyperscale Data Warehouse platform architecture, these jobs can run across Terabytes to Petabytes of data for highly accurate decision making. Furthermore, the OcientML set of connectors, including pyocient, allow for integration with the entire Python ecosystem of data analytics tools and more.
Utilize OcientML Across Industries and Use Cases
While OcientML is not the first in-database machine learning offering, it does bring valuable cost, performance, and scale benefits to workloads and use cases where the continuous analysis of high-volume data sets may otherwise be impractical, too expensive, or altogether infeasible.
OcientML is suitable for various hyperscale use cases in industries including telecommunications, advertising technology (AdTech), government, and financial services. For example, in telecommunications, it can be used for network optimization and customer churn analysis. In AdTech, it can be used for yield optimization and fraudulent click detection. In the government and public sector, OcientML can be used for weather forecasting and analysis, as well as geospatial predictions and analysis. In financial services, it can be used for anti-money laundering and credit risk management.
See OcientML in Action
If you are facing challenges in driving machine learning outcomes across significantly large and complex data, or have a technology environment that is complex and costly when supporting machine learning, contact Ocient to find out more about OcientML. We’d love to discuss how Ocient can improve your machine learning stack and lead to better decisions and outcomes for your business.