The hunter(data scientist) will become the hunted (automated) sooner than we thought
Data scientists are getting automated faster than the rest of the engineers
UB
How AI solution’s maintenance is different from traditional software
IDC predicts that up to 88 percent of all AI and ML projects will fail during
the test phase[1]. A major reason is that AI solutions are difficult to
maintain. In this post, I will highlight how the maintenance of AI solutions is
different and why MLOps is important.
Its critical to do business and technical assessment of the AI solution before(not after) starting the implementation
Imagine that you are either
* The Data Scientist, who is going to implement the solution, will require
technical feasibility and complexity assessment like is the right data
available? how inference will be integrated with the business process? and is
the acceptance criteria achievable with the given data set and constraints?
2 Proven ways to launch successful AI Startups
For business-facing (B2B) AI products, it’s often difficult to get the data necessary to build a prototype because a lot of highly specialized data is locked up within the companies that produce it. There are a couple of general ways in which AI teams can get around this problem:
How to do data and ROI feasibility of a machine learning based forecasting solution
A list of key questions that will help asses business and technical feasibility
of a machine learning solution
Lets take an example of a machine learning and deep learning based forecasting
solution. Imagine that you are the Machine learning engineer and want to asses a
forecasting use case from two