Our first AI Apprenticeship application closed today.

We received a total of 144 applications of which 68 made the first cut to be considered for the workshop on 13th Jan 2018. We will be reviewing these 68 closely and only invite 40 for the workshop out of which we will only offer 20 places for the first batch of apprenticeship.

What is interesting is that we have 27 applicants who could have make it to the workshop round but were disqualified as they had more than 3 years of working experience. We have put them into the” PMETs” list and have communicated to them we hope to review our programme and see how best to help them to transition into an AI-first world in later part of 2018.

For those we have already “rejected” – we have advised them the next-steps they should take to increase their likelihood of being accepted into future AIAP rounds. We are looking specifically for candidates who have:

  • intermediate level programming skills and have developed data products, applications or services
  • some experience with maths and stats
  • familiar with cloud computing and existing cloud providers such as Azure, AWS, GCP
  • familiar with big data technologies such as Hadoop and Spark

A few candidates indicated they did a programming module in University or a 8-weeks MOOC/weekend workshop – this unfortunately is not good enough to get into the AIAP. Very simply, we do not have time to train you to be a programmer. You need to come in already at an intermediate level with a solid foundation, and from there we can provide you the guidance, resources and time to learn and deep dive into AI.

So if you did not make it to the first round of AIAP selection, what can you do? Here is what we recommend:

  1. Go learn Python and/or R. There are many excellent online resources. There is no need to go for a paid course (Seriously, if you need to attend a Python or R course to learn the language, then likely you are not the candidate we are seeking).
  2. Watch the excellent and legendary “Elements of Statistical Learning” series by Hastie and Tibshirani. See this post from  R-bloggers https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
  3. Get familiar with Keras for deep learning (there is more to AI then DL, but this is  a good start).
  4. Get familiar with Spark as a data platform for your AI/ML workloads
  5. Get an Azure (or AWS, GCP) account and go build data products in the cloud
  6. No projects from the office or idea what to build? Go to your local community and grassroots organization and offer to work on a project to help them build something! Until you build a real-world project, you will never experience the non-technical issues on the ground.

Thank you again to the 144 AIAP applicants who have submitted your applications,  it is a validation of the programme and we look forward to helping some of you develop into AI Engineers in 2018!