3/1/2024 0 Comments Superduper video![]() “MongoDB made us an official technology partner and we have already run webinars and live coding sessions with major accounts such as Cisco. Meanwhile, on the AI side, it supports arbitrary models from the Python AI ecosystem, models from PyTorch, Sklearn, Hugging Face and popular AI APIs from vendors such as OpenAI, Anthrophic, and Cohere. On the data side, it supports MongoDB, PostgreSQL, MySQL, SQLite, DuckDB, Snowflake, BigQuery, ClickHouse, DataFusion, Druid, Impala, MSSQL, Oracle, pandas, Polars, PySpark, Trino, and s3. While the product is just a few months old, it has already drawn significant traction from major ecosystem players, giving enterprise teams comprehensive support for popular databases and models. For vector search, it either uses in-database vector functionality provided by database vendors or its own vector-index implementation capabilities. Using this offering, developers can use not just standard machine learning models, for applications like classification, regression, and recommendation systems, but also the latest generative AI models to enable LLM-based chat and vector search, as well as highly custom models for specialized use cases. This gives end-to-end open-source control to the developer and administrator(s) over algorithms, data, compute and infrastructure,” Hagenow explained. The environment may be deployed in standalone experimental mode, on a single client, or with scalable compute in a cloud or on-premise environment via Kubernetes, using best-in-class open-source deployment software. This transforms the database into a(n) (‘super-duper’) AI development and deployment environment. “SuperDuperDB may be installed simply from open-source as a Python package and allows developers to set up a single scalable deployment of all his/ her AI models and APIs, which directly communicates with the database. To solve this challenge and give teams an easy to combine their algorithms with the data which infuses it with value, Hagenow and team created SuperDuperDB, a framework that brings AI models -including streaming inference and scalable model training- directly to the database being used by the enterprise, rather than the other way around. “Startups and innovation in the domain of ‘making AI easier’ have either tended to focus solely on making it easy to deploy algorithms on compute resources or on combining the algorithms and data in a convoluted series of pipelines, in a field known as MLOps,” Hagenow told VentureBeat. ![]() This takes time and can often delay the launch of projects. They have to use tools from the “ MLOps” and “DevOps” ecosystems to extract data from main databases and move it to specialized vector databases through a series of intricate and fragile pipelines. Solving the AI problem with SuperDuperDBĪI is rapidly becoming a standard technology powering modern enterprise operations, but building applications that tap powerful ML models and proprietary data to deliver business value is no cakewalk.Įven with a wide range of ML models and APIs, developers have to put a lot of effort just to bring them into production. The framework is available on Product Hunt starting today.
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