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For enterprises, transitioning to AI is more complicated than adding new plugins to the current website. Infrastructure, data storage, and data input should be considered and secured from negative effects. Interoperability with all AI requirements, as well as smooth operation of the current systems, must be ensured. Additionally, once the transition is over, the employees must be given proper training on working with the new system.
Turium believes that the cloud is the enabler, data is the driver, machine-learning provides the tools and AI is the differentiator. We bring them together to help make smarter, faster decisions that help enable growth—at scale. We do so by deploying AI/ML models on top of a reliable database and iteratively improving them depending on user decisions and feedback to make them operational. Our solutions offer the framework for end-to-end MLOps, advancing AI/ML from the realm of experimentation into practical application. Whether you’re looking to get started, optimize, or scale, Turium has you covered.
Unlock new opportunities. Increase productivity. Increase your influence. We incorporate artificial intelligence (AI) capabilities into our software to provide you with more intelligent, automated solutions. Our AI technologies support diverse environments and scale to suit evolving business demands, from machine learning to computer vision and natural language processing (NLP) to forecasting and optimization.
Turium’s industry-leading data integration features build a solid foundation of meaningful insights from large data to bring value-added decisions from the start.
Our systems have robust and scalable data infrastructures behind them. Implement a comprehensive data culture that covers all processes related to information management, including data collection, data mining, data creation, data aggregation, exploration and linguistic assets to complex data structures to meaningful human-readable information to represent an organization's value system and connect data scientists, engineers, analysts, executives, and operational end users around a shared semantic layer. Turium allows you to specify detailed access control policies during the integration stage and then intelligently propagate those policies throughout the system. This enables models to be confidently pushed for collaboration with sophisticated data security and transparent information management.
Machine Learning models are scattered across functions in most businesses, and data science teams are frequently isolated from operational routines that impact business decisions. As a result, there is a maze of models vulnerable to idea and data drift — as well as model performance degradation.
Turium has pioneered an Ecosystem approach, a completely new method to enterprise modeling to produce an overall solution — rather than developing a single all-encompassing model. Turium's ML Ops is a turnkey solution built on a Container Platform—an enterprise-grade container platform designed for cloud-native, non-cloud-native, and AI/ML and analytics applications with open-source Kubernetes—that can run on bare-metal or virtualized infrastructure, any public cloud, and at the edge; extends the container platform's capabilities to the entire ML lifecycle, allowing you to simplify ML workflows, operationalize ML lifecycles, and accelerate AI deployments. Supports a wide range of data science and machine learning tools at each stage of the model lifecycle, integrating the whole ML model lifecycle and providing DevOps-like agility to ML workflows.
If you look at any technology solution, there is a domain and a software part. Turium's ontology finds value in the intersection of things by attaching each model to a specific organizational objective.
We start by understanding our customers, their context, and the touchpoints they want to cover for designing specific business KPIs to deliver. Domain knowledge gets integrated with AI/ML via the set of factors/hypotheses that define the business solution features by leveraging the experience of domain experts in our diverse team to align the models as closely as possible to the problems they are solving. Knowing the real-world context and how the models are used allows developers to design and fine-tune them to provide the best results. The stated goal acts as the modeling ecosystem's system of record for analyzing, evaluating, and implementing successive model solutions over time. Turium enables a cycle for continual accuracy improvements after you install a model with an objective. Turium connects models to tangible values, making them available throughout the platform and allowing them to be integrated directly into operational processes. Decisions made inside workflows are written back to improve the correctness of future model usage, maintaining a close connection between technical operations and business logic.
Turium builds and deploys AI/ML by combining enterprise data capability with end-to-end AI/ML deployment and monitoring infrastructure.
Models are tightly integrated with end-to-end platform capabilities, which include everything from feature curation and health checks to model administration, inference/serving, and result monitoring. A method for guided deployment that makes use of model inference pipelines and REST endpoints. Track model versions and effortlessly update models using the model registry. Have total visibility into the use of runtime resources. Track, measure, and report model performance for each scoring request, as well as store and analyze inputs and outputs. Integrations with third-party applications are scalable, providing information about model correctness and interpretability.
As the number of connected devices grow, real-time decision-making independent of cloud computing becomes more crucial than ever. Turium Edge AI drives models at the edge of networks, IoT, sensors, and environments.
Turium uses a microservices architecture that makes it possible to modify and deploy vision pipelines easily. Pipelines can span across heterogeneous computing platforms, including CPUs, GPUs, NPUs and even the cloud, providing scalability for heavy workloads and future-proofing edge investment. For example, Turium has the capacity to deliver real-time situational awareness by transforming live video into notifications and actions that matter. It works at the edge without requiring raw video to be streamed externally to a third-party service, expediting the delivery of outcomes. Model-neutral, modular, and lightweight technologies from Turium enable edge AI.
Interoperability is central to Turium, enabling homegrown, open-source, and third parties to work alongside one another in the same platform, supporting different stages of the AI lifecycle.
Our approach uses an application programming interface (API) to access the data. The API is placed as an overlay above all data silos or data-generating systems; it “talks” to each of them, works with metadata, uses all data without moving them out of their respective systems, and feeds analytical software and ML and AI applications. APIs are system agnostic and work with services like Amazon SageMaker and IBM Watson; Turium-managed models using industry-standard tools like sklearn, SparkML, and TensorFlow; or custom logic to define expert systems. Intrinsically,
With Responsible AI, we provide our clients with technology that is transparent, accountable, sustainable, and secure.
Turium helps its clients unlock new opportunities faster, with responsibility being the bottom line of what we do. Ensuring appropriate design, development, and deployment of our technologies is the way forward. We interact and engage with our clients to help them understand and adopt the growing imperative of a safe and responsible AI, which we believe is essential for long-term growth.
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