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tags: ResBaz2021
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# Machine learning lifecycle mangers: How mlflow can make our life easier
## Friday, May 21st, 2021 1\:00-3\:00

[Back to Resbaz HackMD Directory](https://hackmd.io/@ResBaz21/directory)

Just a few of the complicated phases in the ML lifecycle are data processing, development, experimentation (along with the associated parameters, metrics, artifacts, source code, and package dependencies), deployment, and monitoring. To do so, we'll need a platform-agnostic framework that can track and facilitate all stages of development. I'll demonstrate you how mlflow will help us through these stages.

# Getting Started
### install these packages
- jupyter notebook
- numpy
- mlflow
- keras
- scikit-image

# Link to jupyter notebook
- https://github.com/artinmajdi/resbaz_2021.git

## running your first notebook

1. type in command line: jupyter notebook
2. create a new notebook
3. import below packages

import numpy as np
import os
import mlflow
import git
import subprocess
from tqdm import tqdm

import mlflow_info
import keras
from keras.utils import np_utils
from keras.layers.core import Dense, Dropout, Activation

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## Introductions

Artin Majdi (instructor)
- UA ECE PhD candidate
- [e-mail]
- [e-mail]

Name, Affiliation, Title, Email, Social Media
- Your Name, University of Arizona, Your title, [e-mail], your social media

## Questions and Answers
In this section, you can post your questions and feel free to answer if you have it. Questions will be answered during or after the workshop.

1. Ask your question.
- Here is an answer

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:::info
**Session Feedback :mega:**

Use the link below to provide your feedback on the session:
[**Session Feedback Form**](https://forms.gle/TrnJpr9qRBEKdnVVA)
:::