Overview
Practicus AI offers a unified platform for generative AI and data intelligence, designed to streamline and optimize AI workflows for a variety of environments, including cloud, on-premises, and air-gapped private networks. With a focus on ease of use, the platform simplifies model development and management, enhances GPU utilization for complex tasks, and supports scalable deployments to meet growing demands. The AI Studio provides a no-code/low-code environment for creating and deploying AI models, transforming data into actionable insights with minimal coding effort. Additional features such as powerful Exploratory Data Analysis tools, automated machine learning (AutoML), and instant AI APIs facilitate efficient data analysis and integration into applications.
Practicus AI is built to maximize cloud-native capabilities, leveraging open-source technologies for innovation and freedom from vendor lock-in. The platform includes robust observability tools to monitor AI/ML performance, manage logs, and detect anomalies in real-time. Its analytics suite, powered by an open-source Iceberg Lake House, enables deep data insights and scalable querying. With secure, enterprise-grade features like single sign-on (SSO), centralized secret management, and secure GitOps, Practicus AI ensures unmatched security and compliance. The platform's flexibility, scalability, and comprehensive feature set make it an ideal choice for organizations looking to harness the full potential of AI and data intelligence.
Highlights
- Explore and analyze cloud data lakes, data warehouses, databases and mode. Sample billions of rows up to 100 times faster. Switch between data engines (Pandas, DASK, Spark, GPUs with RAPIDS) as you need.
- Process, clean and prepare your data without any coding. When clicking is not enough, use 200+ Excel compatible formulas. Add custom Python code using the built-in editor for more complex requirements. Export the final clean data to a file, data lake or database directly from the app. To build repeatable data pipelines, export to pure Python code and run anywhere you need.
- Build AI models on past data with one-click using Automated Machine Learning (AutoML) and then make predictions on unseen data. If you are a data scientist, or working with one, export to Jupyter code and share your experimentation details in a central database (MLflow). Predict with any model and without the need to deploy using our app, or export simpler models to pure Spreadsheets.
Details
Features and programs
Financing for AWS Marketplace purchases
Pricing
Additional AWS infrastructure costs
Type | Cost |
---|---|
EBS General Purpose SSD (gp3) volumes | $0.08/per GB/month of provisioned storage |
Vendor refund policy
enabled
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Please visit https://practicus.ai/blog for release notes
Additional details
Usage instructions
Practicus AI Cloud Node is the backend for Practicus AI application and SDK. A Practicus AI AWS cloud node instance can be created and deleted using the cloud tab inside our app with just a click. Once instantiated, you can confirm Practicus AI Cloud Node Linux service has been successfully started and is active by running the below on an SSH client.
systemctl status practicusnode
Steps to confirm installation:
1- Open an SSH client. 2- Locate your private key file that you used to launch the instance. (i.e. my_key.pem) 3- Run this command, if necessary, to ensure your key is not publicly viewable. chmod 400 my_key.pem 4- Connect to your instance using its Public DNS: Such as: ec2-12-34-56-78.compute-1.amazonaws.com Example: ssh -i "my_key.pem" ubuntu@ec2-12-34-56-78.compute-1.amazonaws.com 5- Run the below command to verify Practicus AI Cloud Node service is active and running systemctl status practicusnode
For more information please visit the "getting started" section in our documentation.
Resources
Vendor resources
Support
Vendor support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.