Every year, machine learning researchers fascinate us with new discoveries and innovations.
Here are four of the key challenges that Rochwerger and Pang highlight in their book.
It’s free, every week, in your inbox.
Knowing the problem you want to solve is a challenge that applies to all software engineering tasks.
Any experienced developer will acknowledge that doing the right thing is different from doing the thing right.
In some cases, its because theyre trying to solve the wrong problem.
Consider image classification problems.Deep neural networkscan perform such tasks with stunning accuracy.
you might set up the deep learning model on your own server and run your images through it.
In this case, inference will be done in the service providers servers.
In this case, youll need a more specialized approach.
Youll have to consider constraints on the machine learning model and the data.
You need a neural connection that is light enough to run on the compute resources of edge devices.
And youll need a special dataset of labeled images of weed vs non-weed plants.
In machine learning, defining the problem also includes determining how well you want to solve the problem.
But if youre creating a cancer-detection neural internet, then youll need a much higher standard.
Every missed case can have life-impacting consequences.
In many applied machine learning applications, public datasets are not useful for training models.
you gotta either gather your own data or buy them from a third party.
Both options have their own set of challenges.
After gathering the data, theyll need to label the images as plant or weed.
There are dozens of platforms and companies that provide data labeling services for AI applications.
In other tweaks, such as healthcare and banking, the training data will contain sensitive information.
In others, youll need to label new data manually.
Dont forget to allocate resources for the ongoing training of your model.
And thats why an isolated team of data scientists will seldom implement a successful machine learning strategy.
A business problem that can be solved by a model alone is very unusual.
Applied machine learning needs across-functional teamthat includes people from different disciplines and backgrounds.
And not all of them are technical.
Subject matter experts will need to verify the veracity of training data and the reliability of the models inferences.
Product managers will need to establish the business objectives and desired outcomes for the machine learning strategy.
User researchers will help to validate the models performance through interviews with and feedback from end-users of the system.
And an ethics team will need to identify sensitive areas where the machine learning models might cause unwanted harm.
Applied machine learning also needs technical support beyond data science skills.
Software engineers will have to help integrate the models into other software being used by the organization.
You still need more elements to make your machine learning strategy work.
Theres no reason to be afraid of AI.
Its not magic, and its not even rocket science.
you could read the original articlehere.
