Introducing Incu AI

Due to the abundance of available models and their varying performance indicators, selecting the appropriate AI model for a specific task can be challenging. Developers often struggle to discover the most suitable model for their needs, leading to inefficiencies and suboptimal outcomes, even without extensive trial and error. Consider a scenario in which a developer needs to choose an artificial intelligence model specifically designed for the purpose of picture recognition. Convolutional Neural Networks (CNNs), ResNet, Inception, and other models with varying strengths and weaknesses are readily accessible on the market. Comparing these models to one other requires a significant amount of time and computational resources, which can result in project delays and increased costs. Incu AI simplifies this method by consolidating many AI models and providing transparent performance evaluations. Our platform enables developers to efficiently evaluate models by utilizing standardized metrics and real-world performance data, allowing them to make prompt and informed decisions.

Lack of Communication

There is a substantial disparity in communication between end-users and developers in the field of artificial intelligence. End-users frequently lack the technical proficiency to comprehend the capabilities and constraints of different AI models, while developers may not completely comprehend the precise requirements and expectations of the end-users. This disconnection might result in misaligned objectives, where the implemented AI models fail to sufficiently meet the practical needs of the users. For instance, inside a healthcare environment, medical practitioners may require an artificial intelligence model for the purpose of diagnosis. Insufficient comprehension of the medical context and user requirements by developers can lead to inaccurate or irrelevant outcomes from the model. This not only erodes confidence in AI solutions but also impedes their acceptance in crucial domains. Incu AI resolves this problem by offering a platform that facilitates more efficient interaction between developers and end-users. By utilizing comprehensive performance indicators, integrating user feedback, and implementing an efficient deployment procedure, our platform guarantees that the AI models fulfill both technical and practical requirements.

Privacy and Compliance

Preserving data privacy and adhering to compliance regulations during AI inference jobs is a significant obstacle, particularly in industries that handle sensitive information like healthcare, banking, and personal data. Conventional AI inference procedures typically necessitate the transmission of data to external servers, which raises issues over potential data breaches and unauthorized access. Furthermore, data handling and processing in AI systems must strictly comply with regulatory regulations such as GDPR, HIPAA, and CCPA, which impose rigorous limits. Noncompliance can lead to significant sanctions and the erosion of user confidence. Incu AI improves privacy protection by incorporating homomorphic encryption algorithms. This enables users to execute AI models on their confidential data while safeguarding it from any security breaches. Our platform guarantees compliance with regulatory norms, creating a secure environment for AI inference and promoting trust and reliability.

Awareness of New Models

The swift progression of novel AI models is a challenge for developers and end-users alike to remain abreast of the most recent advancements. Keeping abreast of the latest advancements in new models can be overwhelming, since they often come with enhanced performance, efficiency, and capabilities. For example, a developer involved in a natural language processing (NLP) project may encounter challenges in keeping up with the newest advancements in models such as BERT, GPT-3, or T5, each of which provides unique benefits. Failure to keep up with these improvements might result in the utilization of obsolete models, resulting in below-average performance. Incu AI guarantees that consumers remain constantly informed about the most up-to-date AI models and their functionalities. Through regular updates to our platform and the provision of thorough performance comparisons, we empower users to utilize state-of-the-art technology in their projects.

Absence of Verifiable Inference

In traditional AI systems, there is often no transparent mechanism to verify that an inference was performed correctly or securely. This lack of verifiable inference creates trust issues, especially in sensitive applications requiring provable accuracy and integrity. Incu AI addresses this gap by implementing blockchain-backed Proof of Inference, ensuring every computation is transparent, tamper-proof, and auditable. This fosters confidence in AI outputs for users, developers, and enterprises.

Data Layers

Incu AI integrates datasets from various markets to facilitate AI Incu AI. By partnering with Rivalz Network AI, Incu AI utilizes the power of data processing and augmentation, enabling the trusted data source for Inference.

Incu AI is also building its own data marketplace for users to freely provide their dataset and projects can load a dataset in a single line of code, using our own powerful data processing methods with Apache Arrow Format.

Computing Layers

Inferium offers a secure production solution to deploy any models from the store on dedicated and autoscaling infrastructure. By partnering with Aethir Cloud and io.net, Inferium gives users a wide range of selection on deploying models for Space.

ML-Driven Inference Store

Inferium provides a full infrastructure for developers to verify, deploy, validate and run inference on their own model. Users can easily access, test model and provide feedback.

Through learning process, Inferium allows user to easily identify the most effective model for their specific needs, streamlining the model selection process.