Best Machine Learning Software for MariaDB

Immuta's Data Access Platform is built to give data teams secure yet streamlined access to data. Every organization is grappling with complex data policies as rules and regulations around that data are ever-changing and increasing in number. Immuta empowers data teams by automating the discovery and classification of new and existing data to speed time to value; orchestrating the enforcement of data policies through Policy-as-code (PaC), data masking, and Privacy Enhancing Technologies (PETs) so that any technical or business owner can manage and keep it secure; and monitoring/auditing user and policy activity/history and how data is accessed through automation to ensure provable compliance. Immuta integrates with all of the leading cloud data platforms, including Snowflake, Databricks, Starburst, Trino, Amazon Redshift, Google BigQuery, and Azure Synapse. Our platform is able to transparently secure data access without impacting performance. With Immuta, data teams are able to speed up data access by 100x, decrease the number of policies required by 75x, and achieve provable compliance goals.

TIMi allows companies to use their corporate data to generate new ideas and make crucial business decisions more quickly and easily than ever before. The heart of TIMi’s Integrated Platform. TIMi's ultimate real time AUTO-ML engine. 3D VR segmentation, visualization. Unlimited self service business Intelligence. TIMi is a faster solution than any other to perform the 2 most critical analytical tasks: data cleaning, feature engineering, creation KPIs, and predictive modeling. TIMi is an ethical solution. There is no lock-in, just excellence. We guarantee you work in complete serenity, without unexpected costs. TIMi's unique software infrastructure allows for maximum flexibility during the exploration phase, and high reliability during the production phase. TIMi allows your analysts to test even the most crazy ideas.

Hex combines the best of notebooks and BI into a seamless, collaborative interface. Hex is a modern Data Workspace. It makes it easy for you to connect to data and analyze it in collaborative SQL or Python-powered notebooks. You can also share work as interactive data apps or stories. The Projects page is your default landing page in Hex. You can quickly find the projects you have created and those you share with others. The outline gives you an easy-to-read overview of all cells in a project's Logic View. Each cell in the outline lists all variables it defines and any cells that return an output (chart cells or Input Parameters cells, etc.). Display a preview of the output. To jump to a specific position in the logic, you can click on any cell in the outline.

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

Open-Source AI layer for databases. Machine Learning capabilities can be integrated directly into your data domain to increase efficiency and productivity. MindsDB makes it easy to create, train, and then test ML models. Then publish them as virtual AI tables into databases. Integrate seamlessly with all major databases. SQL queries can be used to manipulate ML models. You can increase model training speed using GPU without affecting the performance of your database. Learn how the ML model arrived at its conclusions and what factors affect prediction confidence. Visual tools that allow you to analyze model performance. SQL and Python queries that return explanation insights in a single code. You can use What-if analysis to determine confidence based upon different inputs. Automate the process for applying machine learning using the state-of the-art Lightwood AutoML library. Machine Learning can be used to create custom solutions in your preferred programming language.