Deploying ML models using Docker

To create a Docker container with our newly created web service (or two of them), we need to install Docker on our system. Once we’ve installed Docker, we can use it to compile the container.

The crucial part of packaging the web service into the Docker container is the Dockerfile. It is a recipe for how to assemble the container and how to start it. If you’re interested, I’ve suggested a good book about Docker containers in the Further reading section so that you can learn more about how to create more advanced components than the ones in this book.

In our example, we need two containers. The first one will be the container for the measurement instrument. The code for that container is as follows:

FROM alpine:latest
RUN apk update
RUN apk add py-pip
RUN apk add --no-cache python3-dev
RUN pip install --upgrade pip
WORKDIR /app
COPY . /app
RUN pip --no-cache-dir install -r requirements.txt
CMD ["python3", "...