We’re all overloaded by a lot information coming up on daily basis related to the AI. New applications, incredible results, amazing future on supporting humans vs. scary scenarios about machines taking control as well as losing jobs.
I recently joined the seminar by Politecnico Milano, one of the most valuable players worldwide in educating high tech figures in the all the field of engineering.
The view point was on EU level as well as into the Italian scenario, here is one of the outcomes.
Identify the goal & solutions – the Business Analyst, with a deep knowledge like the AI engineer, could help on identify the possible advantages on adopting AI solutions, evaluating the valuable information by the company
Check system capacity – it depends on data volume, runtime analysis instead of background workflows, this indicates the requirements
Identify the Methodology – probably the most delicate phase of the project, here the presence of the data scientist is mandatory due to the necessary choise to do: deep learning, on-line learning, model prediction etc.
100% AGILE approach – project held adopting the waterfall methodology, because the initial results could change time by time expectations and goals
Learning by Doing – nothing has been already written on the stone, algorithm working in one scenario could not be applied everywhere
Quality of the data – they must be well structured otherwise it’s difficult to reach any possible goal
Validation – this is crucial, any output by AI analysis must be initially validated before pushing the solution in production
Other contents will come soon…