If you are reading this, you are probably interested in or thinking about building a product that uses some form of Artificial Intelligence or Machine Learning. Maybe you want to make predictions from the data you are generating using a trained ML model, or maybe you want to automate existing repetitive and time-consuming tasks with a high-performing AI solution. You might also be thinking about how you can ship these solutions as new products or features for your users, so you can provide them value and collect real-world data, and feed it back to your product, so it can become better over time.
We should see AI as part of the current innovation strategies and tools we have been offered.
In recent years, large consumer and data-driven companies such as Netflix, Facebook, and Google have already deployed ML at scale in their production pipeline as recommendation systems, targeted ads, and optimized search results. These solutions were enabled by leveraging their massive amount of data and big data tools, but even without these key pieces, any company should be able to ship their first AI product.
The course that you are about to start will provide you with guidelines and best practices for the productization of Machine Learning solutions and how businesses can benefit from incorporating AI into their current pipelines.
This is, in fact, a really important point this course focuses on how to measure the business impact and success of your product not only from the technology point of view but also how it is presented to users and these benefits from it.
Throughout the course, we will learn that once a solution is moved into production, there are some concerns we might face related to data dependencies and changes in the external world, model complexity, reproducibility, testing, monitoring, model deployment, and quick delivery of new models. At the end of this self-study course, you will feel confident on how to build a solution that prevents these setbacks to happen, as well as the best way to tackle these.