01-intro

1. Introduction

Instructor: Alexey Grigorev

1.1 Introduction

1.2 Environment preparation

Note: You don't have to rent an instance in the cloud. You can follow the same instructions for setting up your local environment.

Code:

Recommended development environment: Linux

Step 1: Download and install the Anaconda distribution of Python

wget https://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh
bash Anaconda3-2022.05-Linux-x86_64.sh

Step 2: Update existing packages

sudo apt update

Step 3: Install Docker

sudo apt install docker.io

To run docker without sudo:

sudo groupadd docker
sudo usermod -aG docker $USER

Step 4: Install Docker Compose

Install docker-compose in a separate directory

mkdir soft
cd soft

To get the latest release of Docker Compose, go to https://github.com/docker/compose and download the release for your OS.

wget https://github.com/docker/compose/releases/download/v2.5.0/docker-compose-linux-x86_64 -O docker-compose

Make it executable

chmod +x docker-compose

Add to the soft directory to PATH. Open the .bashrc file with nano:

nano ~/.bashrc

In .bashrc, add the following line:

export PATH="${HOME}/soft:${PATH}"

Save it and run the following to make sure the changes are applied:

source .bashrc

Step 5: Run Docker

docker run hello-world

If you get docker: Got permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Post "http://%2Fvar%2Frun%2Fdocker.sock/v1.24/containers/create": dial unix /var/run/docker.sock: connect: permission denied. error, restart your VM instance.

Note: If you get It is required that your private key files are NOT accessible by others. This private key will be ignored. error, you should change permits on the downloaded file to protect your private key:

chmod 400 name-of-your-private-key-file.pem

1.3 (Optional) Training a ride duration prediction model

Note: The NYC taxi data is now in parquet format, not CSV. Here's a video that explains how to read parquet data.

Links:

• Notebook

1.4 Course overview

1.5 MLOps maturity model

Links:

• MLOps Maturity model

1.6 Homework

More information here: homework.md

Notes

Did you take notes? Add them here:

• Local MacOS envprep by Adetbekov
• Environment Setup by Ayoub
• Intro, Environment Setup, and MLOps Maturity Models by Bala
• GCP Environment Setup by Piyush
• Microsoft Azure Environment Setup by Olaide
• Environment Preparation using GCP and pyenv by Dani
• Useful links by Zioalex
• Notes by Alvaro Navas
  • Environment setup on GCP (recycled from Data Engineering Zoomcamp)
  • Docker cheatsheet
  • Conda cheatsheet
  • Git cheatsheet
  • Python and data libraries cheatseet
• Cloud server setup using Deploifai (on AWS, Azure, or GCP) by 98sean98
• Notes from first lesson by Neimv
• Notes for Week 1 by Ron Medina
• Notes by Francisco Delca (environment: local ubuntu + virtualvenv)
• What is MLOps? - Non-technical intro by Lorenz
• Notes for Week1 by Bhagabat
• What is MLOps? - Storytime by Pathik Ghugare
• Send a PR, add your notes above this line