Cloud-based machine learning (ML) company, Elemeno unveils a machine learning operations (MLOps) platform that enables firms to make use of artificial intelligence (AI). Innate user experience can be delivered to the users who are engaged in developing ML models using this platform.
The SaaS ML-Ops platform focuses on reducing typical engineering responsibilities through its user-friendly interface. It automates numerous procedures so that data scientists can spend more time on creative pursuits rather than engineering. A feature store is at the core of the platform, and it’s the key to making ML model creation, deployment, and operation go as smoothly as possible. Elemeno AI’s ultimate objective is to make developing AI solutions for businesses as simple as possible.
Lucas Bonatto, the CEO of Elemeno AI, stated, “Launching our public beta means getting one step closer to solving the intricacies that are causing companies to slowly adopt AI. According to the Stanford AI Index Report of 2022, the median overall adoption of AI in organizations around the globe was only 2p.p. when compared to the same period of last year. This is in contrast with the large number of investments made in the space. Elemeno ML-Ops is the first step to solving this problem. We’re offering $50 in free credits to the first 100 users that sign up on our platform”.
AWS, Redshift, Google BigQuery, etc. are required for organizations to connect their data sources to the platform and need to configure feature ingestion pipelines. Feature transformations can be adjusted, and tables can be customized. Ensuring the availability of current features while inference, the platform automatically handles data. For training purposes and debugging a model in production, base data from cold storage is utilized.
Deployment is the final phase. Companies can use Elemeno AI’s no-code solutions with supported frameworks such as Tensorflow, PyTorch, Scikit Learn, Keras, ONNX, and Tensorflow Lite. The platform eliminates the requirement for model clients to compute or store a copy of the features required by connecting the features in the deployed models. If a model were to be utilized in a mobile app, for example, the developers would simply need to provide the identification of the entity for which a prediction is needed, and the platform would discover the most recent features and apply them to the model.
Several firms make use of the cutting-edge technology of Elemeno AI. Several installation challenges like the absence of end-to-end solutions that allow enterprises to manage the whole lifespan of their ML models can be tackled using the platform.