The Technical and Human Challenges of Machine Learning Projects

The Technical and Human Challenges of Machine Learning Projects

One of the most important elements of any machine learning (ML) and analytics project is the data you use for your models. While the algorithms themselves can be more or less standardized and routine from project to project, what changes is the specific data used in the predictive modeling.

 

But what also changes from project to project are the humans involved, and the human element can actually be even more challenging than the technical elements.

 

With that in mind, let’s first look at the technical—ie, data and architecture-related—challenges of machine learning and analytics projects. 

 

Technical Challenges of Machine Learning Projects

 

The primary technical challenge of any machine learning and analytics project is to properly prepare your data for use in your models. 

 

To do this, you will need to:  

 

  1. Import your data, which will probably be coming from several different sources and have many different formats. 
  2. Analyze your data, so that you can fully understand the structure of the data you’re importing. 
  3. Clean your data, by making it consistent and accurate.
  4. Extract, transform, and load your data, into your data processing engine so that it can be used in your models. 
  5. Pick your data set, by establishing if your existing clean data fits your machine learning model’s requirements or if you will need to source additional data.

 

In addition to all of the data pipeline steps described above, in order to build your ML models, you will also need to build a AI/ML pipeline, which includes picking your data for training, testing and validation, choosing your output and input variables, and picking an estimator. You’ll also need to choose the parameter value to run different outcomes, pick a scoring function to score the different outcomes, select the model with the best score, and put the best model into production

 

After that, you will continuously need to run your models with new data, score them, and adjust them. You then need to decide how to share the insight with the business users, which you can do either through an API that your favorite app can access or through your own application. A key aspect of making this part work will be using dashboards that are designed in partnership with the business users.



Human Challenges of Machine Learning

 

As seen above, the technical challenges of machine learning, and analytics as well, are all about building a data process and a solid infrastructure to support that process and share the information with business users. But it’s the infrastructure-building part where the human challenges of these types of projects come into play and also where (and why) most machine learning projects fail.

 

To properly build a machine learning data infrastructure that allows you to put to good use all of the data you’ve prepared for your machine learning project, you will need:

 

  1. A data engineer with, for example, SQL, Spark skills to import, analyze, and clean the data, and ETL/ ELT skills depending on if you decide to extract, and transform the data before loading it, or extract and load the data before transforming it.
  2. Database engineers with data lake technology knowledge that can design terabytes of data and also have proficiency with, for example, Spark, Hadoop, or some other data lake technology.
  3. Database admins who can manage, for example, HDFS, Hive, Iceberg, or MongoDB once the database is designed.
  4. DevOps, DBAs, and sysadmins to manage the data infrastructure—ie, to maintain it and to scale it to terabytes. 
  5. Cloud engineers to move the infrastructure to the cloud.
  6. A business intelligence engineer to design the KPI definitions and the dashboards. 
  7. Business users to elaborate a strategy and its requirements.

 

Somewhere in the six steps above is where most machine learning projects fail. In the end, most machine learning projects don’t make it from PoC to deployment. The ones that do manage to make it to deployment can’t operationalize at scale or are too costly to maintain.

 

This is why every machine learning project requires a machine learning platform that can help you put all of the above steps together and allow the different stakeholders to collaborate with each other in a manageable, scalable way. 

 

A Better Way: The ForePaaS Platform

 

The ForePaaS platform was designed to make machine learning and analytics projects successful by fostering collaboration and automating most infrastructure steps mentioned above. It can take you from PoC, to deployment, to operation, to scale, to continual maintenance, and to machine learning success at a fraction of the cost and internal manpower. 

 

Our platform empowers you to easily prepare your data for machine learning by letting you do all of the data preparation steps with ease. It also allows you to seamlessly manage the human aspect of machine learning projects by fostering open collaboration between your data scientists, your data engineers and your business users. 

 

In the end, your data is useless without a great human team, and your great human team is useless without great collaboration. 

 

The human element is what we’ll dive into in our next blog, but for now, you can learn more about the ForePaaS platform here

 

Try the ForePaaS Platform for free:

 

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The image used in this blog is royalty free from: https://unsplash.com/photos/KdeqA3aTnBY