TensorFlow Serving Tutorial for Beginners

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs.

Basic Serving Tutorial

See the basic tutorial on the TensorFlow Serving site to learn how to export a trained TensorFlow model and build a server to serve the exported model.

Advanced Serving Tutorial

See the advanced tutorial on the TensorFlow Serving site to learn how to build a server that dynamically discovers and serves new versions of a trained TensorFlow model.

Serving Inception Model Tutorial

See the serving inception tutorial on the TensorFlow Serving site to learn how to serve the inception model with TensorFlow Serving and Kubernetes.

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Integrating Tensor flow API into ASP.net Web applications for Image detection

Introduction Tensor flow provides advanced Machine Learning API. For the example here we will provide a solution for the Image recognition. The same can be applied to other use cases such as Text recognition or any other data set that needs to solve the problems of AI and Machine Learning such as: Predictive Analytics Cognitive Processes and its Automation e.g. NLP Virtual Agents (Bot’s) Camera Image detection and processing e.g. Industrial Robotics and Drones Text Knowledge Analytics Application Development Steps In the first step we create an ASP.NET application for connecting the camera feed or input data. In the next step Install Tensor flow as per instructions on the website –> Click here To begin using Tensor flow we need to decide on the model to use for processing input data – There is an option to use either ready-made models or develop on own depending on the problem at hand. For simplicity we will use ready-made model called a deep convolut...

Machine Learning with ML.NET 1.0

As a person coming from .NET world, it was quite hard to get into machine learning right away. One of the main reasons was the fact that I couldn’t start Visual Studio and try out these new things in the technologies I am proficient with. I had to solve another obstacle and learn other programming languages more fitting for the job like Python and R. You can imagine my happiness when more than a year ago, Microsoft announced that as a part of .NET Core 3 , a new feature will be available – ML.NET . In fact it made me so happy that this is the third time I write similar guide . Basically, I wrote one when ML.NET was a version 0.2 and one when it was version 0.10 . Both times, guys from Microsoft decided to modify the API and make my articles obsolete. That is why I have to do it once again. But hey, third time is the charm, so hopefully I will not have to do this again until ML.N...

Using Tensorflow Object Detection API to build a Toy detector

Here I extend the API to train on a new object that is not part of the COCO dataset. In this case I chose a toy that was lying around. See gif below. So far, I have been impressed by the performance of the API. The steps highlighted here can be extended to any single or multiple object detector that you want to build. Tensorflow Toy Detector~ You can find the code on my Github repo Collecting data The first step is collecting images for your project. You could download them from google ensuring you have a wide variation in angles, brightness, scale etc. In my case I created a video of the little aeroplane toy and used Opencv to extract images from the video. This saved me a lot of time. I ensured that images were taken from multiple angles. You can also randomly change brightness for some of the images so that the detector can work under different conditions of lightning. Overall 100–150 pics will suffice. See some sample images below: ...

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