{"id":1866,"date":"2019-08-06T03:00:00","date_gmt":"2019-08-06T03:00:00","guid":{"rendered":"http:\/\/kusuaks7\/?p=1471"},"modified":"2024-07-19T13:37:03","modified_gmt":"2024-07-19T13:37:03","slug":"the-surprising-truth-about-what-it-takes-to-build-a-machine-learning-product","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/the-surprising-truth-about-what-it-takes-to-build-a-machine-learning-product\/","title":{"rendered":"The Surprising Truth About What it Takes to Build a Machine Learning Product"},"content":{"rendered":"\t\t
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When I was in college, an ice cream shop opened nearby, and a few friends and I went to check it out. We walked in, and it looked completely normal \u2014 they had all the usual flavors like mint, chocolate, and the like. However, at the end of the counter, they had this flavor called \u201cThe Broccoli Surprise\u201d. A naturally curious individual, I had to try it. I asked the attendant behind the counter for a sample. It was white with little green specks, and it tasted sweet, creamy, and rich. I was confused \u2014 there was no broccoli flavor in here. So I asked, \u201cwhat\u2019s the surprise?\u201d \u201cThere\u2019s no broccoli,\u201d she replied with a smile.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Machine learning (ML) has a surprise, too. One of the biggest misconceptions about ML deployment within organizations is comprehending the difficulty and the value.<\/p>\n

Integrating ML into your business workflows can be broken down into five activities:<\/p>\n

Defining KPIs<\/strong>\u00a0\u2014 Key Performance Indicators allow us to measure and discuss what we are trying to improve. Common KPIs include customer retention, manufacturing yield, or employee turnover. Setting KPIs is a critical step in Machine Learning since they ultimately drive optimization along the way to a performant model.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Collecting Data<\/strong>\u00a0\u2014 Collecting the data that will be used to train your ML algorithms. Yes, you could use ML models others have produced if you lack data. However those business considerations are similar to other SaaS offerings, so let\u2019s leave them out of scope here.<\/p>\n

Infrastructure<\/strong>\u00a0\u2014 ML infrastructure includes various pieces of software: data management, annotation tools, model training, and testing environments. This infrastructure is an upfront investment, but makes iterating and improving the model and data set much more efficient.<\/p>\n

Optimizing ML Algorithm<\/strong>\u00a0\u2014 Here we consider factors like which model to use based on a given data set\/problem, the amount of necessary training data, the layers in your neural net, and hyperparameter tuning. There are a plethora of choices.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Integration<\/strong>\u00a0\u2014 Getting an ML model working in a vacuum is a great achievement, but it is not until the model is integrated with a real workflow that it starts to create a tangible business impact. Integration is the process of building pipes and structure which seamlessly pass information and data between users and computers.<\/p>\n

Based on many conversations with companies interested in deploying machine learning, there is a high perceived effort required in, and pay off from, optimizing a machine learning algorithm.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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There are a few possible reasons for this:<\/p>\n\n