Creating a Docker image for your training job

A Docker image is, in many contexts, the most critical deliverable of a model developer to a more specialized systems infrastructure team in production for a training job. The project is contained in the following folder of the repository: https://github.com/PacktPublishing/Machine-Learning-Engineering-with-MLflow/tree/master/Chapter08/psystock-training-docker. In the following steps, we will produce a ready-to-deploy Docker image of the code produced:

1. You need to set up a Docker file in the root folder of the project, as shown in the following code snippet:

   FROM continuumio/miniconda3:4.9.2
   RUN apt-get update && apt-get install build-essential -y
   RUN pip install \
       mlflow==1.18.0 \
       pymysql==1.0.2 \
       boto3
   COPY ./training_project /src
   WORKDIR /src

2. We will start by building and training the image by running the following command:

   docker build -t psystock_docker_training_image...