On-device training enables interesting personalization use cases where models can be fine-tuned based on user needs. For instance, you could deploy an image classification model and allow a user to fine-tune the model to recognize bird species using transfer learning, while allowing another user to retrain the same model to recognize fruits. This new feature is available in TensorFlow 2.7 and later and is currently available for Android apps. On-device training is also a necessary foundation for Federated Learning use cases to train global models on decentralized data.
In this tutorial, we'll learn How to Deploy on-Device Training in TensorFlow Lite Model with 4 Steps
#tensorflow #python #machine-learning #android
The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.
Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.
Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.
In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.
#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop
SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.
With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well.
View Documentation View Github
Installing the SageMaker Python SDK
The SageMaker Python SDK is built to PyPI and can be installed with pip as follows:
pip install sagemaker
You can install from source by cloning this repository and running a pip install command in the root directory of the repository:
git clone https://github.com/aws/sagemaker-python-sdk.git cd sagemaker-python-sdk pip install .
#machine learning #models #aws #tensorflow #a library for training and deploying machine learning models on amazon sagemaker #amazon sagemaker
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
In this article, you will learn to use a pre-trained model, apply transfer learning, convert the model to TF Lite, apply optimization, and make inferences from the TFLite model.
A Basic Introduction to TensorFlow Lite
Dogs and Cats dataset
I have downloaded the dataset and unzipped the file as per the following structure.
Python code to extract the data and create the data as per the below structure is available here.
#deep-learning #tensorflow #tensorflow-lite #machine-learning
If you need to deploy a machine learning model to a mobile device, it becomes challenging, as there’s limited space and processing power on the device. There’s no doubt that machine learning models suffer from such heavy model sizes and high latency when targeting mobile devices.
However, there are techniques to reduce size or increase performance so that they do fit and work on mobile (see the links below for more on these techniques). It should be noted that, despite these challenges, ML models are currently being deployed to mobile devices.
In this article, we’re going to discuss how to implement a housing price prediction machine learning model for mobile using TensorFlow Lite. We’ll learn how to train a TensorFlow Lite neural network for regression that provides a continuous value prediction, specifically in the context of housing prices.
TensorFlow Lite is an open source deep learning framework for mobile device inference. It is essentially a set of tools to help us run TensorFlow models on mobile, embedded, and IoT devices. TensorFlow Lite enables on-device machine learning inference with low latency and a small binary size.
There are two main components of TensorFlow Lite:
#machine-learning #tensorflow #tensorflow-lite #heartbeat #mobile-app-development