So I thought I'd put together a thread around my move towards a better professional understanding of AI. This is a complex and very varied landscape but I hope some of this provide inspiration to anyone else in the same unfortunate position with difficult job market.
So for a first post I'll split this into two - business and the technology. I'll be approaching both.
1. Business
So I went from engineering to presales, then to product with new market and existing products, startups to being accountable for 70+ as part of a very large 3y/2.7Bn programme and the last role basically building out cloud for a multi-national conglomerate at a family level of products.. so my interest here is not "what is AI" but more the business, including the obvious financial approaches, operational models and all the normal fun IT stuff. How do you estimate a AI development using models for example? Cost Structures. Best practices etc.
I have a fairly good idea by cross referencing my experience, the technical business management/finops experience for the cloud work etc with observations but I wanted to get a structured approach, have industry feedback and also get a badge on the CV that says growth mindset.
Approach here is to enroll and pay for a "certified" 6 week course from a recognised university. I don't get a degree but I do get a confirmation of attendance and a mark at the end of it from the assignments. I will also have some live lectures and some group working.
Obviously I can't provide the course, but I will confirm my observations.
We've already seen an ecosystem of AI.. AI as a service etc. Simply put, before the course has stated I'm already aware of:
a) Large company vendors generate a general model (lots of CPU time) based on industry available data. For example the models that AWS provide through Marketplace for Bedrock.
b) Smaller vendors then often produce specialisations of the models or additional models that integrate using a more specialised set of data (that may or may not be specific for your company).
c) Your company then has to expend more time and cost (let's call this development) with specialist skills to train/characterise and confirm acceptance of the resulting model integrations (more development).
d) Competition and data privacy laws and regulations then become part of the morality frameworks, along with the purpose and exposure of individual's data or affected.
I start on the 20th, so this will be and interesting course.. I finish late January.
2. Technology itself
I have a BSc Software Engineering (parallel and distributed computation) so I have some maths, I have A-level maths in the distant past and some maths used for performing image processing - specifically FFT, phase correlation (dot product) and 2/3D maths around spread function distributions in terms of probability. I also spent some time around PHD/Professor maths/physics for quantum computing and photonic cryptography - in short I can appreciate things like error, distributions etc but have also played a bit with randomness and Markov chains but not in any structured fashion. I've basically built maths for implementations from scratch based on scientific papers before. Including finite response and I've also (at school) built a backwards propagation neural network from a Scientific American article back in the early 1980s/1990s.
So with that - basically most devs that have done some maths should be able to follow along.
Approach here is:
* learn the Python programming language, this is going to be painful as it's not a language I like. I preferred R to be honest..
* learn the maths required - specifically calculus, linear algebra and probability theory.
* Lean some ML dev stack - Jupyter notepad, panda, bumpy, and maths plotting. Basically libraries and tools at a very basic level.
* Start with ML courses:
** Stanford Online - ML specialisation
** Andrej Karipathy - Neural Networks
** Stanford Online - Deep Learning specialisation
I'll them probably play with some Kaggle challenges.
Maths - I have some maths school books that specifically deal with calculus, linear algebra and probability theory - that should be a good start.
The rest is simply a case of building myself a virtual machine, installing linux etc and then hacking away as I have done before as the founder of a quantum hackathon team. Thankfully I had some team members with really good maths skills that previously worked in quantum.
So if you feel that you want to embrace the AI change in the workplace, and put a step in that direction. This thread may be a good way.
So for a first post I'll split this into two - business and the technology. I'll be approaching both.
1. Business
So I went from engineering to presales, then to product with new market and existing products, startups to being accountable for 70+ as part of a very large 3y/2.7Bn programme and the last role basically building out cloud for a multi-national conglomerate at a family level of products.. so my interest here is not "what is AI" but more the business, including the obvious financial approaches, operational models and all the normal fun IT stuff. How do you estimate a AI development using models for example? Cost Structures. Best practices etc.
I have a fairly good idea by cross referencing my experience, the technical business management/finops experience for the cloud work etc with observations but I wanted to get a structured approach, have industry feedback and also get a badge on the CV that says growth mindset.
Approach here is to enroll and pay for a "certified" 6 week course from a recognised university. I don't get a degree but I do get a confirmation of attendance and a mark at the end of it from the assignments. I will also have some live lectures and some group working.
Obviously I can't provide the course, but I will confirm my observations.
We've already seen an ecosystem of AI.. AI as a service etc. Simply put, before the course has stated I'm already aware of:
a) Large company vendors generate a general model (lots of CPU time) based on industry available data. For example the models that AWS provide through Marketplace for Bedrock.
b) Smaller vendors then often produce specialisations of the models or additional models that integrate using a more specialised set of data (that may or may not be specific for your company).
c) Your company then has to expend more time and cost (let's call this development) with specialist skills to train/characterise and confirm acceptance of the resulting model integrations (more development).
d) Competition and data privacy laws and regulations then become part of the morality frameworks, along with the purpose and exposure of individual's data or affected.
I start on the 20th, so this will be and interesting course.. I finish late January.
2. Technology itself
I have a BSc Software Engineering (parallel and distributed computation) so I have some maths, I have A-level maths in the distant past and some maths used for performing image processing - specifically FFT, phase correlation (dot product) and 2/3D maths around spread function distributions in terms of probability. I also spent some time around PHD/Professor maths/physics for quantum computing and photonic cryptography - in short I can appreciate things like error, distributions etc but have also played a bit with randomness and Markov chains but not in any structured fashion. I've basically built maths for implementations from scratch based on scientific papers before. Including finite response and I've also (at school) built a backwards propagation neural network from a Scientific American article back in the early 1980s/1990s.
So with that - basically most devs that have done some maths should be able to follow along.
Approach here is:
* learn the Python programming language, this is going to be painful as it's not a language I like. I preferred R to be honest..
* learn the maths required - specifically calculus, linear algebra and probability theory.
* Lean some ML dev stack - Jupyter notepad, panda, bumpy, and maths plotting. Basically libraries and tools at a very basic level.
* Start with ML courses:
** Stanford Online - ML specialisation
** Andrej Karipathy - Neural Networks
** Stanford Online - Deep Learning specialisation
I'll them probably play with some Kaggle challenges.
Maths - I have some maths school books that specifically deal with calculus, linear algebra and probability theory - that should be a good start.
The rest is simply a case of building myself a virtual machine, installing linux etc and then hacking away as I have done before as the founder of a quantum hackathon team. Thankfully I had some team members with really good maths skills that previously worked in quantum.
So if you feel that you want to embrace the AI change in the workplace, and put a step in that direction. This thread may be a good way.
