Understanding the ML Lifecycl
<p>The Machine Learning (ML) Lifecycle is a crucial framework that guides the development and deployment of machine learning models. It encompasses a series of interconnected stages. This article provides a glimpse into the various facets of the ML Lifecycle, industry standards, best practices and how these steps might vary depending on the scope of the project.</p>
<h1>Problem Formulation and Data Collection</h1>
<h2><strong>Problem Formulation</strong></h2>
<ul>
<li>Understand the business or research problem and define it clearly, specifying the ML task (e.g., classification, regression, clustering).</li>
<li>Collaborate with stakeholders and domain experts to gain a comprehensive understanding of the problem’s requirements and constraints.</li>
<li>Set clear success criteria to evaluate the model’s performance.</li>
</ul>
<h2><strong>Data Collection</strong></h2>
<ul>
<li>Identify relevant data sources, both internal and external, that contain the necessary information to address the problem.</li>
<li>Use APIs (e.g., RESTful APIs) for accessing organization specific data programmatically, and data providers for accessing other licensed datasets</li>
<li>Use Web scraping libraries (e.g., BeautifulSoup, Scrapy) for extracting data from websites</li>
<li>Ensure data quality by performing data validation, checking for missing values, and identifying potential biases.</li>
</ul>
<p><strong>Best Practices</strong></p>
<ul>
<li>Clearly define the problem statement and objectives before collecting data to avoid unnecessary efforts.</li>
<li>Ensure data privacy and compliance with regulations when collecting sensitive data.</li>
<li>Leverage APIs or licensed datasets as required to access reliable and pre-processed data.</li>
</ul>
<p><strong>Industry Standards</strong></p>
<ul>
<li>In industries like finance and healthcare, data collection requires adherence to strict regulatory guidelines.</li>
<li>- Data privacy and security are of utmost importance in industries handling personal or sensitive information.</li>
</ul>
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