In this webinar Dr. Alessandra Tosi goes through the steps of the data-science pipeline, with a particular focus on business applications. Dr. Tosi stresses the importance of correctly framing the business problem in terms of goals, value and success metrics, and how to translate this into the machine-learning framework.
Alessandra is a senior scientist and product owner, with a PhD in probabilistic machine learning.
For the past five years, data science has been praised as a technology that can unlock new applications and hidden insights for organisations. However, today it is struggling to live up to expectations.
Even with low cost widely available cloud computing, it can take significant time and compute power to train machine learning models on large data sets. This is expensive and is often at odds with the net-zero carbon goals of many organisations today. Throwing more data at a problem isn’t always the best answer, and by using AI that is responsible by design, we can reduce these problems while maintaining performance. Active learning is one of the methods we use at Mind Foundry to achieve this.
Hyper-parameter tuning can be observed in many everyday processes, but few people are aware of its existence. This blog aims to change that, and demonstrate its true value in data science.
