MAKANA
Confidence. Intelligence. Results.What is MAKANA?
MAKANA, from Mobius Logic, is a cloud-based MLOps platform that makes it easy for data scientists and software application developers to implement DevOps for Machine Learning (ML) through comprehensive support for all phases of the ML life cycle.
I can finally see which model version is being applied to produce my signal predictions and the expected release date for the next model version, all from a centralized dashboard.
Pravin Chandrasekaran
Why customers use MAKANA?
- Streamline and automate the end-to-end ML life cycle and tie it to existing DevOps processes
- Enhance collaboration between data scientists, application developers and business users
- Integrate Data Science and AI projects into the core enterprise operations by enabling easy access to validated ML models and faster retraining of current ML models
Solve AI Problems
Use the MAKANA Designer:
- Build ML models faster
- Automate the testing of several algorithms to deliver the best ML model
Do More with Less
Enhance your Data Scientist’s Productivity by:
- Automating tedious tasks and simple projects
- Integrating data science activities into the DevOps pipeline so you are ready to release to production the moment the ML model is validated
- Jump from proof-of-concept to a live ML model in a few clicks
Transform to an AI-Driven Enterprise
Let AI drive value in every corner of your enterprise by:
- Enabling collaboration between your AI teams and business leaders
- Expanding your business processes to include the AI and Data Science teams
- Automate workflows and share knowledge across various teams using the MAKANA Enterprise Portal
MAKANA allows you quickly realize the value of your ML models and retain that value over time, ensuring your data scientists, business users and software development teams are collaborating effectively.
Built on Microsoft Azure, MAKANA supports all phases of the ML model life-cycle with full support for popular, open-source Python packages such as scikit-learn, TensorFlow, PyTorch, and MXNet.
Deploy almost Anywhere
- Deploy your models as web services to the cloud, to your local development environment, or to Azure IoT Edge devices
- Use CPU, GPU, or field-programmable gate arrays (FPGA) for inferencing
- Embed ML models into analytics apps based on Microsoft Power BI
Automate the end-to-end ML life cycle
- Use Azure Pipelines to enable continuous integration by automatically starting a training run when you check a change into a Git repo
- Create release pipelines that are triggered when new models are created in a training pipeline
Turn your training process to a reproducible pipeline
Use ML pipelines to stitch together all the steps involved in your model-training process:
- Data preparation
- Feature extraction
- Hyperparameter tuning
- Model evaluation
