Several months or years in, you are running into all kinds of obstacles that prevented you from getting the value you want from your investments in ML. For example:<\/p>\n
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- You can\u2019t figure out how to get that model off the ground \u2013 from sitting on your analyst\u2019s virtual machine into your consumers’ hands to impacting your top or bottom line.<\/li>\n
- You aren\u2019t sure how to streamline the process of feeding data to your model.<\/li>\n
- Your team members developed each of their models in a distinct way, adding to the expense and uncertainty of building and maintaining them.<\/li>\n
- Your model seems to break whenever developers change your IT infrastructure, adding more unanticipated costs to your value proposition.<\/li>\n
- You can\u2019t make any major business or data model changes without leading to a major rebuilding effort.<\/li>\n
- Your model is performing worse and proving less valuable over time.<\/li>\n
- You can\u2019t update your model because it\u2019s too much of a resource-consuming task.<\/li>\n<\/ul>\n
These kinds of problems often prevent companies from delivering value in a timely manner to address business needs. As any practitioner in the field will attest, it takes more than a team of data scientists<\/a> and business consumers to make ML a winning proposition at the right price and right time.<\/strong><\/p>\n
You need more than ML. You need MLOps<\/a>.<\/p>\n
How We Think About MLOps<\/h2>\n
![\"MLOps](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2022\/04\/MLOps-Image.png\")
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