Efficient AutoML Code Generation for Edge Sensing
SensiML Analytics Studio, the core of the SensiML software suite, uses your labeled datasets to rapidly generate efficient inference models using AutoML and an extensive library of edge optimized features and classifiers. Using cloud-based model search, Analytics Studio can transform your labeled raw data into high performance edge algorithms in minutes or hours, not weeks or months as with hand-coding. Analytics Studio uses AutoML to tackle the complexities of machine learning algorithm pre-processing, selection, and tuning without reliance on an expert to define and configure these countless options manually.
Whether a seasoned ML expert or just learning the basics of data science, Analytics Studio offers a tool that can substantially increase your embedded algorithm development productivity.
The SensiML Python SDK for Advanced Users
Built on the familiar Jupyter Notebook execution environment for Python, the SensiML Python SDK provides full control of the AutoML pipeline for users wishing to work at this level. This gives you the tools for customizing your own functions, tuning parameters, training algorithms, and classifiers of any model. You always have full control when needed.The task of traversing the near limitless combination of input features, classifier types, and associated parameters is one well-suited to automation with results that typically outperform the heuristics and insight of a seasoned expert performing this task by hand. SensiML’s segmentation, feature pre-processing, and transform libraries includes over 80 routines built for efficient execution in the IoT sensing edge node.
Depending on selected target hardware, SensiML Analytics Studio leverages specific platform resources to improve efficiency with examples including use of Arm CMSIS DSP and Quicklogic FPGA acceleration. Resulting models (known as SensiML Knowledge Packs), can be assessed in bit exact device emulation of the model within Analytic Studio and delivered in binary, object, or full source code form.*
* Full source code output requires Standard or Enterprise level subscription.
ML classification spans a growing list of edge optimized algorithms including:
- Distance based classifiers (kNN, RBF)
- Decision trees
- Boosted trees
- Ensemble models
- Neural networks
The SensiML Endpoint AI Workflow
SensiML covers a broad array of embedded microcontrollers and sensors enabling developers to target the right system for their application needs. From small ultra-low power MCUs possessing tens of kB memory to multi-core x86 client nodes with GBs of SRAM, SensiML provides ML code output to suit the hardware capabilities of your chosen platform.
Whether you are an experimenter, startup, mid-sized manufacturer, or large enterprise, SensiML has a plan tailored to suit your requirements. Compare our different plan features or get started with our "Free Trial Edition" to determine what is best for your own IoT project. As such, you can fully explore the capabilities of the toolkit, collect and label your own data, and build and show real working AI edge models!
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