ML-powered data used by Disney and HBO

Looking for options abroad, its executives reached out to us and we promptly assembled a nearshore team composed of experts in different fields such as mathematics, data manipulation, machine learning, computer vision and Python development.

It was decided that the US team would remain in charge of the platform’s general development and the nearshore team would focus on ML custom development, following the company’s strategy of outsourcing this very specific (and traditionally costly) part of the process. Our team has come to be known as this client’s ML team. While its in-house staff works on the product's database and UI/Front End aspects, our devs are dedicated to developing the APIs it uses as Machine Learning services. When the client identifies a new opportunity for creating a custom ML service, this is what the development process looks like:

• Research: During this discovery phase, the developers focus on finding references for solving the challenge presented by the client, and based on that, they propose a solution. In this phase, the team identifies the state-of-the-art options in the field, open source products, existing datasets, and other tools they could re-use.
• Validation: In order to endorse the theories and estimate the effort, the team runs different experiments, called Proofs of Concept. In this step, the team trains several models in order to obtain results, gather metrics and document them. Based on the outcomes, they can evaluate and compare the different options. As a result, a general documentation is generated, detailing the different approaches for tackling the API development, plus benefits, costs and risks. We share this document with the client so we can discuss the alternatives and select one of them. Part of the decision is to define sources of data to use, the main features to extract, the ML algorithms, training and evaluation methods to apply, etc.
• Development: After the decision is made, our team gets started with the API development. Some of the good practices we put in place are unit testing, continuous integration, and code reviews. One of the developers from the in-house team is key during this process because he's responsible for connecting the API with the client's platform. Also, from the very beginning of the process until the API is ready to go live, we work very closely with another in-house dev who is focused on DevOps so we can discuss the feature implementation and the required resources (hardware, software, etc) to do so.
• Final demo: When the API is up and running, we often have another demo with the sales team. The goal is to present the solution, answer the sales reps’ questions, and support them in understanding it. Afterwards, the sales team presents this information to customers, so it’s key for them to have a clear vision of what the solution can do.