How to automate data science code with Jenkins and Docker: MLOps = ML + DEV + OPS

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MLOPS = ML + DEV + OPS

How many created AI models have been put into production in enterprises ? With investment in data science teams and technologies, the number of AI projects increased significantly and with it a number of missed opportunities to put then into production and assess the real business value. One of the solutions is MLOPS that delivers the capabilities to bring data science and IT ops together to deploy, monitor and manager ML/DL models in production.

Continuous integration (CI) and continuous delivery (CD), known as CI/CD pipeline, embody a culture with agile operating principles and practices for DevOps teams that allows software…


Current technologies are already making it possible to accelerate discoveries: few examples.

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Current technologies are already making it possible to accelerate discoveries, particularly in the field of medical research. This acceleration will certainly be accentuated with technologies that are developing at all speeds, such as decision-making algorithms derived from artificial intelligence, the ability to better identify genes, which will make drugs more personalized and better suited. If we look further ahead, future technologies like quantum computing could make major discoveries. …


From current to next decades technologies: Few examples

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Classical computing has experienced remarkable progress guided by Moore’s Law and the Von Newman architecture. Moore’s law tells us that every two years, we double the number of transistors in a processor and at the same time we increase performance by two or reduce costs by two. This pace has slowed down over the past decade and we are currently seeing emerging technologies. We must rethink information technology (IT) and in particular move towards heterogeneous system architectures with specific accelerators in order to meet the need for performance. What is also interesting is how advances in theoretical science the last…


A quick example of a Docker container and REST APIs to perform online inference

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The idea of this article is to do a quick and easy build of a Docker container to perform online inference with trained machine learning models using Python APIs with Flask. Before reading this article, do not hesitate to read Why use Docker for Machine Learning, Quick Install and First Use of Docker, and Build and Run a Docker Container for your Machine Learning Model in which we learn how to use Docker to perform model training and batch inference.

Batch inference is great when you…


A quick and easy build of a Docker container with a simple machine learning model

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The idea of this article is to do a quick and easy build of a Docker container with a simple machine learning model and run it. Before reading this article, do not hesitate to read Why use Docker for Machine Learning and Quick Install and First Use of Docker.

In order to start building a Docker container for a machine learning model, let’s consider three files: Dockerfile, train.py, inference.py.

You can find all files on GitHub.

The train.py is a python script that ingest and normalize EEG data in a csv file (train.csv) and train two models to classify the…


From building to running your Docker image

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Install Docker

For the installation, we can find all the necessary documentation on Docker’s website (https://docs.docker.com/engine/).

When we create Docker containers, we need to use some tools and terminologies such as Dockerfile, Docker Images or Docker Hub. Docker containers are running instances of Docker images. Docker images contain all the tools, libraries, dependencies, executable application source code necessary to run the application as a container. We can build the Docker image from common repositories or from scratch using a Dockerfile which is a text file containing instructions on how to build Docker container image. It’s…


Resolving The “Tt works in my machine” Problem

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First Things First: The Micro-services

The first thing to understand before talking about containerization is the concept of micro-services. If a large application is broken down into smaller services, each of those services or small processes can be termed micro-services and they communicate with each other over a network. The microservices approach is the opposite of the monolithic approach which can be difficult to scale. If one particular feature has some issues or crashes, all other features will experience the same. Another example is when the demand for a particular feature is seriously increasing, we are forced to increase the resources such as the hardware…

Xavier Vasques

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