{"cells":[{"cell_type":"markdown","source":["Project Name: project_name\n====================\n---\n\nProject: Description: project_description\n---------------------\n\nProject Tree\n---------------------\n\n\n```\n\n.project_workspace_path\n├── project_name\n|   ├── README.ipynb\n|   ├── model\n|   |   └── model.ipynb\n|   ├── notebooks\n|   |   └── exploratory_analysis.ipynb\n|   ├── preprocessing\n|   |   └── preprocessing.ipynb\n\nproject_dbfs_path:\n└── project_name\n|   └── model\n|       ├── model.pkl\n|       ├── input\n|       ├── output\n|       └── artifacts\n\n\n```\n\n**Please, complete here information on using and testing this project.**\n\n####  Scope:\n\n---\n\n####  Autor:\n\n\n---\n**We strongly recommend that you consider to use the bellow Machine Learning Canvas template, this document should be filled with the business users**"],"metadata":{}},{"cell_type":"markdown","source":["The Machine Learning Canvas Template\n====================\nThe Machine Learning Canvas comes as a template you can download and fill in. It helps structure your vision for an ML system, and it’s the first step towards making sure you connect ML’s capabilities to your organization’s objectives. It allows to describe:\n* How you’re using predictions to provide value for an end-user\n* What data you’re learning from\n* How you’re making sure the whole thing “works”\n\nBuilding high-value ML systems typically involves different roles: data scientists, software engineers, product designers, technical and business managers. The Machine Learning Canvas allows to keep everyone on the same page.\n\n___\n\n<html xmlns=\"http://www.w3.org/1999/xhtml\" lang=\"\" xml:lang=\"\">\n<head>\n  <meta charset=\"utf-8\" />\n  <meta name=\"generator\" content=\"pandoc\" />\n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0, user-scalable=yes\" />\n  <style type=\"text/css\">\n      code{white-space: pre-wrap;}\n      span.smallcaps{font-variant: small-caps;}\n      span.underline{text-decoration: underline;}\n      div.column{display: inline-block; vertical-align: top; width: 50%;}\n  </style>\n  <!--[if lt IE 9]>\n    <script src=\"//cdnjs.cloudflare.com/ajax/libs/html5shiv/3.7.3/html5shiv-printshiv.min.js\"></script>\n  <![endif]-->\n</head>\n<body>\n<h1 id=\"the-machine-learning-canvas-v0.4-designed-for-designed-by-date-iteration-.\">Project: project_name</h1>\n<table>\n<tbody>\n<tr class=\"odd\">\n<td><p><strong>Decisions</strong></p>\n<p>How are predictions used to make decisions that provide the proposed value to the end-user?</p></td>\n<td><p><strong>ML task</strong></p>\n<p>Input, output to predict, type of problem.</p></td>\n<td><p><strong>Value Propositions</strong></p>\n<p>What are we trying to do for the end-user(s) of the predictive system? What objectives are we serving?</p></td>\n<td><p><strong>Data Sources</strong></p>\n<p>Which raw data sources can we use (internal and external)?</p></td>\n<td><p><strong>Collecting Data</strong></p>\n<p>How do we get new data to learn from (inputs and outputs)?</p></td>\n</tr>\n<tr class=\"even\">\n<td><p><strong>Making Predictions</strong></p>\n<p>When do we make predictions on new inputs? How long do we have to featurize a new input and make a prediction?</p></td>\n<td><p><strong>Offline Evaluation</strong></p>\n<p>Methods and metrics to evaluate the system before deployment.</p></td>\n<td></td>\n<td><p><strong>Features</strong></p>\n<p>Input representations extracted from raw data sources.</p></td>\n<td><p><strong>Building Models</strong></p>\n<p>When do we create/update models with new training data? How long do we have to featurize training inputs and create a model?</p></td>\n</tr>\n<tr class=\"odd\">\n<td></td>\n<td><p><strong>Live Evaluation and Monitoring</strong></p>\n<p>Methods and metrics to evaluate the system after deployment, and to quantify value creation.</p></td>\n<td></td>\n<td></td>\n<td></td>\n</tr>\n</tbody>\n</table>\n<p><strong><a href=\"http://www.machinelearningcanvas.com\"><span class=\"underline\">machinelearningcanvas.com</span></a> by Louis Dorard, Ph.D.</strong> Licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.</p>\n\n</body>\n</html>"],"metadata":{}},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":3}],"metadata":{"name":"README","notebookId":2167426005249155},"nbformat":4,"nbformat_minor":0}
