The Journey to Streamlining our ML Platform Interface Using our CLI Tool
<p>In the ever-evolving world of Machine Learning Operations (MLOps), efficiency and productivity are crucial for organisations looking to capitalise on the potential of their machine learning models. The aim is to provide platform users with a streamlined and standardized interface that allows them to direct their focus towards delivering value proficiently while platform offerings are continually refined, and underlying infrastructure is optimally enhanced. To meet these needs the MLOps team at Virgin Media O2 (VMO2) developed a powerful Command-Line Interface (CLI) tool designed specifically for executing common ML tasks.</p>
<p>In this blog post, we will explore the advantages of the CLI tool and the journey that led to its creation. As a platform team with over 15 projects comprising approximately 50 models in production across multiple teams, the MLOps team maintains an ML platform, described in our <a href="https://medium.com/@vmo2techteam/how-were-accelerating-machine-learning-at-scale-at-virgin-media-o2-a728d8a47ec" rel="noopener"><strong>previous blog post</strong></a><strong>,</strong> which streamlines and optimises the end-to-end machine learning lifecycle, from development to deployment and beyond.</p>
<h2><strong>Executing command operations</strong></h2>
<p>In the typical workflow for executing Python scripts, you begin by navigating to the directory housing your Python script within your terminal or command prompt. Thereafter, you enter the <code>python</code>command followed by the designated script name, such as <code>python main.py</code></p>
<p>In most scenarios, the process of executing commands is not as straightforward due to the necessity of command-line arguments. Consider a scenario where specific arguments like input and output files, along with a designated operation, are prerequisites for the script’s functionality. Consequently, the command evolves into a more intricate form</p>
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