Reports show that about 80% of enterprise data is categorized as unstructured, and getting insights from it requires advanced skills. However, there is a growing shortage of data scientists, and finding, retaining, and hiring experts with these advanced skills can be quite expensive. Companies looking to embrace digital transformation must unlock this data in an automated and organized way that allows analysts and decision-makers to use it. Once this data is available, data science and machine learning can create a sustainable, competitive advantage for your business.

Whether you’re unlocking the insights buried in text, documents, and images or creating data science and machine learning models, StraViso’s Quantum Cloud allows you to solve your business’s most vexing problems by bridging the gap between the need for advanced skills in an easy-to-use platform and the need for answers. Quantum Cloud supports secure real-time data pipelines and offers predictive analytics, forecasting, anomaly detection, data exploration, and business visualization. They also make available APIs for sentiment analysis, operational intelligence, triage recommendations, etc. Quantum Cloud offers the feature of collecting public data, which is helpful when you want to create an OCR pipeline without spending too much time on manual annotation. Additionally, Quantum Cloud supports multiple versions of the same pipeline, allowing you to compare outputs from different versions.

Plan, develop, and deploy your ML projects at scale

Intuitive, explainable AutoML for rapid insight generation

By harnessing automated machine learning (AutoML), Quantum Cloud empowers business domain experts to take charge and expedite the generation of valuable insights. Unlike traditional methods that rely on data scientists to code complex models, Quantum Cloud offers a user-friendly experience where users can rapidly build, validate, iterate, and explore machine learning models with guidance. With Quantum Cloud’s automated insight generation, the platform unveils hidden signals and crucial relationships within your data at an accelerated pace. This functionality ensures that valuable insights are not only accessible but also rapidly discovered, providing a substantial competitive advantage in decision-making.

Recognizing that machine learning’s impact is contingent on effective communication with stakeholders, Quantum Cloud facilitates quick sharing of insights and results across the organization. Interactive and exportable visualizations make complex machine-learning outcomes comprehensible to various business stakeholders, fostering a culture of trust and understanding. Quantum Cloud is committed to making machine learning an intuitive and explainable process, removing barriers, and enabling a broader audience to harness its benefits.

Build business-critical ML models within minutes

Quantum Cloud revolutionizes the machine learning landscape by offering a seamless and efficient solution for building business-critical models within minutes. This platform is a beacon for data scientists and developers, empowering them with the tools and capabilities to rapidly create, deploy, and manage high-quality models in an unprecedented timeframe—just 1 minute. The significance of this accelerated pace lies in the platform’s commitment to enhancing the time-to-value metric by incorporating cutting-edge machine learning operations (MLOps) and a suite of integrated tools.

At the core of Quantum Cloud’s value proposition is the ability to expedite the deployment of ML models, thereby providing businesses with a competitive advantage in swiftly adapting to dynamic market conditions. The platform seamlessly integrates MLOps, streamlining the model development lifecycle and optimizing resource utilization. Data scientists and developers benefit from a cohesive environment where they can build robust models and navigate deployment complexities effortlessly—this amalgamation of speed and sophistication positions Quantum Cloud as a game-changer in machine learning.

Quickly and easily drive ML models into action

Beyond the rapid model deployment, Quantum Cloud empowers users to translate machine learning predictions into tangible business decisions quickly. The platform facilitates a seamless transition from analytics to action, ensuring that the insights derived from ML models are leveraged effectively to drive business outcomes. This transformative capability is crucial in a fast-paced business environment, where the ability to act swiftly on insights can make the difference between success and stagnation.

Quantum Cloud recognizes the diversity of business processes and their unique requirements. In response, the platform offers a versatile set of features that allow users to export, customize, and integrate models into other business applications promptly. This flexibility ensures that Quantum Cloud is not a one-size-fits-all solution but a customizable toolkit that adapts to the specific needs of individual businesses. This feature is particularly valuable for organizations with distinct processes or industry-specific demands, as it enables them to tailor the ML models to align seamlessly with their operational framework.

Transparency and governance for the entire ML lifecycle

In large enterprises, the intricate network of ML models at different development phases, including staging and production, can be overwhelming, often reaching the hundreds or even thousands. Successfully navigating this complexity requires a comprehensive solution that provides visibility and governance throughout the entire ML lifecycle. Quantum Cloud adeptly addresses this challenge by meticulously tracking each model’s history, offering a detailed account of its progression from development to deployment. This streamlines the management process and deepens the understanding of the model’s evolution.

Moreover, Quantum Cloud bolsters governance by introducing a robust mechanism for managing approvals and changes to a model’s stages. This functionality guarantees that the appropriate stakeholders have authority over deployment stages, fostering a structured and accountable approach to ML model management.