Carlos Lara’s AI Blog

When you are building an AI/ML product, it’s paramount that you define clear success metrics from the beginning. These metrics will help guide the AI product development lifecycle and ensure that your team converges on the right product that solves business problems/user needs. There are two ways to assess AI/ML product success: 1) Business outcomes… Read more

What is the most important element that will determine the success or failure of an AI/ML project? Most people, including technical professionals in the field, would think it’s the datasets: Quality, quantity, and a data engineering pipeline to produce them. This is because machine learning algorithms perform only as good as the data used to… Read more

Every machine learning solution requires a dataset that encapsulates the business problem to be solved. A machine learning system will ingest this dataset, learn its complex patterns/relationships, and output a set of business predictions that help solve a specific business problem. This sounds great, but how do you acquire this dataset?

How do you build products that leverage machine learning? Machine learning is using data to answer valuable business questions. Answering these business questions should lead to the creation of tangible business value. This could be increased revenue, decreased costs, increased retention rate, increased operational efficiency, etc. Therefore, always focus on the business impact of artificial intelligence when… Read more