The AI Cloud is a unique, AI-focused data infrastructure that collects, stores and manages a broad array of structured, semi-structured and unstructured real-time data at high volume. Designed from the ground-up for the needs of modern machine-learning at scale, the AI Cloud automates the management of messy, ever-evolving data sourced from many different systems, and the provisioning of dynamic cluster computing resources to digest and transform data. It integrates seamlessly with existing data lakes and data warehouses while providing a true real-time event management system for low-latency use cases and a document ingestion system for complex unstructured data.
Over 80% of data engineers' and data scientists’ time is spent in the time-consuming, low-leverage, spirit-sapping endeavor of writing scripts to massage and transform raw data into useful features and, in turn, using those features to predict outcomes of interest and make optimal decisions. We’ve made that tedious scripting a thing of the past. The AI Studio enables a powerful new abstraction to go from raw data to production decisions in minutes. Iterating on data pipelines, models, and business logic all become point-and-click endeavors for the most part. Under the hood, our system uses the world’s first “AI compiler” to auto-generate code to transform data, build features, create models, and generate data infrastructure supporting model execution. Powerful change management controls enable continuous performance monitoring, and our collaboration capabilities allow efficient re-use of relevant abstractions across projects.
The AI Runtime enables one-click generation of production infrastructure to support real-time decisioning. This includes the creation and management of production infrastructure, data pipelines, models, databases, and real-time APIs to power products with stringent reliability requirements. The Runtime comes equipped with a number of monitoring facilities to ensure that the quality of decision-making can be tracked in a continuous way and provide early warnings about potential breakdowns in data quality or model performance.
We offer each of our customers a package of the following services that is tailored to their business needs:
We provide usage-based access to our AI platform, including GUI for model creation, production-ready APIs, and charting tools that can be used on a standalone basis or can be integrated into existing dashboards. Machinify is offered both in our own multi-tenant cloud environment and on your own virtual private cloud.
Our data science experts are available to help you solve actual problems in your business. The Build-Operate-Transfer model reduces risk and accelerates time to market, while giving you full control at the end of the process.
Our data science and product teams provide assistance in developing full end-to-end products. Bringing together data science, UX design and development, as well as AI product management capabilities, Machinify can help accelerate successful widespread usage of end-user facing machine-learning driven experiences.
Allows Machinify to dynamically understand the schema of incoming data and seamlessly evolve the schema to keep up with changes to the structure of the data in real-time. All while providing a structured “relational” view of the data.
AI-enabled adaptive storage engine tunes itself to the incoming data structures for optimal performance. Utilizes columnar storage with smart per-column choice of compression for high-speed queries. Achieves a 30-1 compression ratio.
Enables interactive queries, data transformation, and fast modeling all on a turnkey, managed multi-user cluster.
Enables defining all the semantic concepts of interest, thereby creating a unified library of entities and their features to use as inputs in building AI models and as outputs to drive decisions.
Automates the process of turning data abstractions into production code, eliminating the need to write data manipulation code for the purposes of building models, as well as the need to re-implement the code and create custom data infrastructure for model deployment.
Brings the power of AI to the domain expert by automating the process of building an AI model given a target metric to predict.