How to Deploy Your Tensorflow Models on Heroku with A Button Click

In this tutorial, we'll learn How to Deploy Your Tensorflow Models on Heroku with A Button Click

Step 1: Build and save your model using the SavedModel format

Step 2: Create a configuration file to be used by Tensorflow serving.

Step 3: Zip the model file in tar.gz format and save it on a public S3 bucket

Step 4: Serve with Cruise!

 

#heroku #tensorflow #python 

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Workshop Alert! Deep Learning Model Deployment & Management

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

How to Deploy Your Tensorflow Models on Heroku with A Button Click

In this tutorial, we'll learn How to Deploy Your Tensorflow Models on Heroku with A Button Click

Step 1: Build and save your model using the SavedModel format

Step 2: Create a configuration file to be used by Tensorflow serving.

Step 3: Zip the model file in tar.gz format and save it on a public S3 bucket

Step 4: Serve with Cruise!

 

#heroku #tensorflow #python 

Deploying Machine learning models using Flask on your website

Understanding of Machine Learning using Python (sklearn)
Basics of Flask
Basics of HTML,CSS

#machine-learning #deployment #ml-model-deployment #flask #deploying

A library for training and deploying machine learning models on Amazon SageMaker

SageMaker Python SDK

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

Deploying Django app on Heroku with GitHub

Heroku provides many powerful features for deploying a project up on a live server to access it from anywhere in the world. The easiest way is to integrate it with GitHub and deploy code living on GitHub. Heroku can automatically build and release (if the build is successful) pushes to the specified GitHub repository.

#django #deployment #python #github #heroku #deploying