How to Build a TinyML Application with TF Micro and SensiML

TinyML reduces the complexity of adding AI to the edge, enabling new applications where streaming data back to the cloud is prohibitive. Sure, we can detect audio and visual wake words or analyze sensor data for predictive maintenance on a desktop computer. TinyML allows us to take advantage of these advances in hardware to create all sorts of novel applications that simply were not possible before. At SensiML our goal is to empower developers to rapidly add AI to their own edge devices, allowing their applications to autonomously transform raw sensor data into meaningful insight.

We have taken years of lessons learned in creating products that rely on edge optimized machine learning and distilled that knowledge into a single framework, the SensiML Analytics Toolkit, which provides an end-to-end development platform spanning data collection, labeling, algorithm development, firmware generation, and testing. Building a TinyML application touches on skill sets ranging from hardware engineering, embedded programming, software engineering, machine learning, data science and domain expertise about the application you are building

#tensorflow 

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How to Build a TinyML Application with TF Micro and SensiML
Mckenzie  Osiki

Mckenzie Osiki

1621931885

How TensorFlow Lite Fits In The TinyML Ecosystem

TensorFlow Lite has emerged as a popular platform for running machine learning models on the edge. A microcontroller is a tiny low-cost device to perform the specific tasks of embedded systems.

In a workshop held as part of Google I/O, TensorFlow founding member Pete Warden delved deep into the potential use cases of TensorFlow Lite for microcontrollers.

Further, quoting the definition of TinyML from a blog, he said:

“Tiny machine learning is capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of ways-on-use-case and targeting battery operated devices.”

#opinions #how to design tinyml #learn tinyml #machine learning models low cost #machine learning models low power #microcontrollers #tensoflow latest #tensorflow lite microcontrollers #tensorflow tinyml #tinyml applications #tinyml models

The Best Way to Build a Chatbot in 2021

A useful tool several businesses implement for answering questions that potential customers may have is a chatbot. Many programming languages give web designers several ways on how to make a chatbot for their websites. They are capable of answering basic questions for visitors and offer innovation for businesses.

With the help of programming languages, it is possible to create a chatbot from the ground up to satisfy someone’s needs.

Plan Out the Chatbot’s Purpose

Before building a chatbot, it is ideal for web designers to determine how it will function on a website. Several chatbot duties center around fulfilling customers’ needs and questions or compiling and optimizing data via transactions.

Some benefits of implementing chatbots include:

  • Generating leads for marketing products and services
  • Improve work capacity when employees cannot answer questions or during non-business hours
  • Reducing errors while providing accurate information to customers or visitors
  • Meeting customer demands through instant communication
  • Alerting customers about their online transactions

Some programmers may choose to design a chatbox to function through predefined answers based on the questions customers may input or function by adapting and learning via human input.

#chatbots #latest news #the best way to build a chatbot in 2021 #build #build a chatbot #best way to build a chatbot

How to Build a TinyML Application with TF Micro and SensiML

TinyML reduces the complexity of adding AI to the edge, enabling new applications where streaming data back to the cloud is prohibitive. Sure, we can detect audio and visual wake words or analyze sensor data for predictive maintenance on a desktop computer. TinyML allows us to take advantage of these advances in hardware to create all sorts of novel applications that simply were not possible before. At SensiML our goal is to empower developers to rapidly add AI to their own edge devices, allowing their applications to autonomously transform raw sensor data into meaningful insight.

We have taken years of lessons learned in creating products that rely on edge optimized machine learning and distilled that knowledge into a single framework, the SensiML Analytics Toolkit, which provides an end-to-end development platform spanning data collection, labeling, algorithm development, firmware generation, and testing. Building a TinyML application touches on skill sets ranging from hardware engineering, embedded programming, software engineering, machine learning, data science and domain expertise about the application you are building

#tensorflow 

7 Mistakes You Should Avoid While Building a Django Application

Django…We all know the popularity of this Python framework. Django has become the first choice of developers to build their web applications. It is a free and open-source Python framework. Django can easily solve a lot of common development challenges. It allows you to build flexible and well-structured web applications.

A lot of common features of Django such as a built-in admin panel, ORM (object-relational mapping tool), Routing, templating have made the task easier for developers. They do not require spending so much time on implementing these things from scratch.

One of the most killer features of Django is the built-in Admin panel. With this feature, you can configure a lot of things such as an access control list, row-level permissions, and actions, filters, orders, widgets, forms, extra URL helpers, etc.

Django ORM works with all major databases out of the box. It supports all the major SQL queries which you can use in your application. Templating engine of Django is also very, very flexible and powerful at the same time. Even a lot of features are available in Django, developers still make a lot of mistakes while building an application. In this blog, we will discuss some common mistakes which you should avoid while building a Django application.

#gblog #python #python django #building a django application #django #applications

Gerhard  Brink

Gerhard Brink

1624006278

The Rising Value of Big Data in Application Monitoring

In an ecosystem that has become increasingly integrated with huge chunks of data and information traveling through the airwaves, Big Data has become irreplaceable for establishments.

From day-to-day business operations to detailed customer interactions, many ventures heavily invest in data sciences and data analysis  to find breakthroughs and marketable insights.

Plus, surviving in the current era, mandates taking informed decisions and surgical precision based on the projected forecast of current trends to retain profitability. Hence these days, data is revered as the most valuable resource.

According to a recent study by Sigma Computing , the world of Big Data is only projected to grow bigger, and by 2025 it is estimated that the global data-sphere will grow to reach 17.5 Zettabytes. FYI one Zettabyte is equal to 1 million Petabytes.

Moreover, the Big Data industry will be worth an estimate of $77 billion by 2023. Furthermore, the Banking sector generates unparalleled quantities of data, with the amount of data generated by the financial industry each second growing by 700% in 2021.

In light of this information, let’s take a quick look at some of the ways application monitoring can use Big Data, along with its growing importance and impact.

#ai in business #ai application #application monitoring #big data #the rising value of big data in application monitoring #application monitoring