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Machine Learning Proves Challenging - Still Featured

Machine Learning Proves Challenging - Still "Solved rubik\u2019s cube \/ gan cube."

Technology and science fascinate us, there are innovations and discoveries that push the boundaries of science every year and has made artificial intelligence a concept we are much familiar to these days. Although more of the usage of applied AI (applied machine learning), which obviously requires skills, resources, and knowledge going beyond data science present a challenge usually absent in the academic and scientific research settings.

Some examples of organisations succeeding and failing in integrated machine learning have been traced by Alyssa Simpson Rochwerger and Wilson Pang, in their new book Real World AI: A Practical Guide for Responsible Machine learning.

Here are the four key challenges that Rochwerger and Pang highlight in their book and how product leaders can avoid repeating failures in the world of innumerable machine learning strategies.

Defining the problem

It is to be acknowledged that "doing the right thing" is different from "doing the thing right". Defining the problem plays a major role in the choices you make for the technologies, data sources, and to breif people working on your project. So, to know the problem is a challenging task in itself.

There are high chances that many people fail just because they're trying to figure out the wrong problem, and hence they fail to account for all the variables, latent biases which are mostly crucial to a model's success or failure. A detailed definition of the problem will determine the kind of model, data, talent, and investment you need so that you can actually perfeom tasks with stunning accuracy.

In the era of machine learning, determining how well you want to solve the problem is also a part of defining the problem.

Gathering training data

Gathering and organizing the data needed to train the models in applied machine learning is again one of the major key challenges as well. We can consider this in contrast to scientific research where training data is usually available and the goal is to create the right machine learning model.

Public datasets are not useful for training models in many applied machine learning applications and for that you either need to gather your own data or buy them from a third party. Both the options come at no surprise but with their own set of challenges.

Verifying data quality and provenance is also crucial to the quality of machine learning models in cases where the data comes from different databases. You could end up producing a useless model if you are making assumptions about how the data you’re using got there, and so you need to highly accurate and updated with your work.

Maintaining machine learning models

What is a machine learning model?

Machine learning models are prediction machines that work on finding patterns in data obtained from the world and forecasting future outcomes from current observations.

So accordingly as the world around us changes, so do the data patterns, and models trained on past data gradually decay.

Moreover, this is a key part of any successful machine learning strategy. Making sure you have the correct infrastructure and processes to collect a continuous stream of new data and updating your models time and again is an important aspect as well.

Gathering the right team

Your models are going to affect people’s work and life, and your company’s bottom line as well in applied machine learning. The reason for a successful implementation of a machine learning strategy is an isolated team of data scientists.

Applied AI includes people from different disciplines and backgrounds working together and needing a cross-functional team irrespective of all these factors, where not all of them are technical as well.

This field also requires technical support beyond data science skills. There is integration of models into other softwares done by the help of software engineers.

Developing the right machine learning strategy

Although we have rounded up some of the major key challenges you'll face in applied machine learning. But you will still need more elements to make your machine learning strategy work better.

Rochwerger and Pang discuss about various strategies dealing with dilemmas, production challenges, security and privacy issues, and the ethical challenges of applied machine learning. They will provide you with a  plenty of real-world examples that show how you can do things right and avoid botching your machine learning initiative, and would be of a great help if you're wondering to begin today.

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Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

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