Thanks to the internet and open-source community, there has never been a better time to start building products that leverage AI and Data to create valuable insights for various organizations. In this article, I will share some amazing techniques that will make your Machine Learning Solutions much better. These are techniques that have shown a lot of success in projects, but too many ML practitioners look beyond these and jump straight to expensive Deep Learning Methods. To anyone who has been following my work for a while, this will not surprise you. In terms of benefits, implementing a degree of randomness into your Machine Learning Pipelines will improve your overall network- in terms of performance, robustness, and even costs.

One of the hardest things about building a strong career in Artificial Intelligence, Data Science, or Machine Learning is to develop your skills in Math. Unfortunately, Math is one of those fields that scares a lot of people. Not learning Math properly will seriously compromise your problem-solving skills. For more details- check out my article- Why You need Math for Machine Learning. It focuses on Machine Learning, but the principles apply to many more domains.

Machine Learning researchers at Meta have released a new Large Language Model (LLM) called Sphere. With its amazing performance on search-related tasks, and ability to parse through billions of documents, combined with Meta's other work into NLP In this article, Meta has positioned itself well to disrupt the search market. I will cover the technology behind this architecture itself. I will do another article on the implications behind Meta open-sourcing everything about their model, later down the line. That requires its own attention.