1 Machine Learning Process
Machine Learning is math. In specific,
performing Linear Algebra on Matrices. Our
data values must be numeric.
Select Algorithm based on question and
The cost function will provide a measure of how far my algorithm and
its parameters are from accurately representing my training data.
Sometimes referred to as Cost or Loss function when the goal is to
minimise it, or Objective function when the goal is to maximise it.
Having selected a cost function, we need a method to minimise the Cost function, or
maximise the Objective function. Typically this is done by Gradient Descent or Stochastic
Different Algorithms have different Hyperparameters, which will affect the
algorithms performance. There are multiple methods for Hyperparameter
Tuning, such as Grid and Random search.
Results and Benchmarking
Analyse the performance of each algorithms and
Are the results good enough for
Is the ML algorithm training
and inference completing in a
How does my algorithm scales for both training and inference?
How can feature manipulation be done for training and inference in real-time?
How to make sure that the algorithm is retrained periodically and deployed into production?
How will the ML algorithms be integrated with other systems?
Can the infrastructure running the machine learning process scale?
How is access to the ML algorithm provided? REST API? SDK?
Is the infrastructure adapter to the algorithm we are running? Should GPU’s be considered rather than CPUs’?
How much, or how many of these?
How can these elements be grouped?
SaaS - Pre-built Machine Learning models
Data Science and Applied Machine
Tools: Jupiter / Datalab / Zeppelin
Machine Learning Research