Machine-learning

Kinesis can be a valuable component in machine learning solutions, particularly in scenarios where real-time data processing and analysis are required. This is part of a blog series giving a high level overview of the different services examined on the AWS Machine Learning Specialist exam.

Data Visualisations can play a number of different roles within Machine Learning projects. They can be a big part of the Data Exploration process to help find insights within the data, understand its structure and see any pattern. This is part of a blog series giving a high level overview of the different services examined on the AWS Machine Learning Specialist exam.

This blog explores the different Data Preparation services AWS has to offer. This is part of a blog series giving a high level overview of the different services examined on the AWS Machine Learning Specialist exam.

When working on large Machine Learning projects, you need data. However, raw data is rarely in the ideal format for training models and so some form of data preparation is typically needed. This is part of a blog series giving a high level overview of the different services examined on the AWS Machine Learning Specialist exam.

When working with machine learning you will need access to data and within AWS there are many different ways to store that data. Choosing the right storage method will depend on the type of the data you are storing, its volume and how quickly you need to access it! This is part of a blog series giving a high level overview of the different services examined on the AWS Machine Learning Specialist exam.

Data is all around us and is an essential part of Machine Learning. This is part of a blog series giving a high level overview of the different services examined on the AWS Machine Learning Specialist exam.

This is part of a blog series giving a high level overview of Machine Learning theory and the AWS services examined on the AWS Machine Learning Specialty exam

When presenting AI solutions, the machine learning model can seem like a black box and sometimes people will want to understand more about what is going on behind the scenes. How can we find a way to break down and explain the model without going too far into model interpretation?

Last week I boarded the train in the scorching heat and travelled to Dublin, to view the magnificent new Microsoft office and to take part in OpenHack, with one of my Kainos colleagues.

Last week I attended HackTheHub, a machine learning based hackathon in Belfast. I was super excited to attend as machine learning is such a hot topic right now and it’s something I don’t really have much experience in.

Now with major changes in regulations, it has become even more important that personal data is secured and only seen by the right people. With this being such a hot topic right now, I was extremely keen to work on a final year project that could tap into this every increasing issue.