Machine learning is an ever-growing area of interest for developers, businesses, tech enthusiasts and the general public alike. From agile start-ups to trendsetting industry leaders, companies know that successful implementation of the right machine learning product could give them a substantial competitive advantage. We have already seen businesses reap significant benefits of machine learning in production through automated chatbots and customised shopping experiences.
In the eCommerce space, machine learning can significantly increase conversion rates by showing users the right product and offer at the right time.
The traditional approach has always been algorithm centric. To do this, you need to design an efficient algorithm to fix edge cases and meet your data manipulation needs. The more complicated your dataset, the harder it becomes to cover all the angles, and at some point, an algorithm is no longer the best way to go. Luckily, machine learning offers an alternative. When you’re building a machine learning-based system, the goal is to find dependencies in your data. You need the right information to train the program to solve the questions; it is likely to be asked. To provide the right information, incoming data is vital for the machine learning system. You need to provide adequate training datasets to achieve success.
Oleg Tarasenko, one of our developers, recently developed Crawly, a web scraping framework for Elixir. In our latest blog, Oleg and Grigory provide a step-by-step tutorial on how to build a machine learning tutorial in Elixir. Check it out and let us know what you think?