Join Us at the TinyML Summit
SensiML will be presenting on Thursday, March 25th at 12 noon. We will cover how to build smart IoT products with production-quality AI tools.
SensiML will be presenting on Thursday, March 25th at 12 noon. We will cover how to build smart IoT products with production-quality AI tools.
SensiML Analytics Toolkit now provides pipeline support for one of the most popular open-source AI frameworks in existence: TensorFlow, specifically its TinyML variant TensorFlow Lite for Microcontrollers. This week we’ve added a step-by-step boxing wearing tutorial showing how SensiML data collection, annotation, and feature preprocessing are combined with a neural network classifier using TFL Micro.
SensiML Analytics Toolkit now provides pipeline support for one of the most popular open-source AI frameworks in existence: TensorFlow, specifically its TinyML variant TensorFlow Lite for Microcontrollers. The combination of SensiML and TensorFlow Lite for Microcontrollers offers best-in-class AI code generation for TinyML applications from consumer wearables to multi-sensor industrial monitoring devices.
This week SensiML joined a distinguished panel of healthcare, technology, and academic leaders to overview its contributions to towards an innovative COVID-19 screening technology for deployment later this year.
Scenario: Six months and $500,000 of investment into a key initiative to build a more intelligent product or streamline a strategic internal process using AI, and things are not going well.
The frequent culprit is poor upfront planning for a data-centric approach to development. Read more about this all too common issue that can undermine AI project success before you even start.
SensiML announces two COVID-19 initiatives:
1) SensiML Toolkit Starter Edition is now available for a limited time at $99 for a 90-day license.
2) SensiML to open-source a COVID-19 AI dataset for rapid virus screening from cough sounds. Learn how you can contribute to this project yourself.
Artificial Intelligence (AI) is becoming increasingly commonplace as organizations seek to bring effective decision making and operational efficiencies to business in ways that transform how humans and machines work together. As this transformation takes shape, the advantages of edge-based AI implementations over centralized or cloud-based models is changing how AI tools are being deployed and