How to tackle common problems with Machine Learning Models

Updated: Apr 05, 2020 1:30 PM

4 common mistakes that an organisation should avoid in order to successfully subsume machine learning in their analytics strategy.

Managing various aspects of infrastructure surrounding the machine learning is the biggest challenge which organisations faces these daysManaging various aspects of infrastructure surrounding the machine learning is the biggest challenge which organisations faces these days

By Satish Pala

Machine Learning provides businesses with the knowledge to make more informed, data-driven decisions that are faster than traditional approaches. However, machine learning is not the be all and end all of analytics. It encounters many of the same challenges as different analytics methods. Here are 4 common mistakes that an organisation should avoid in order to successfully subsume machine learning in their analytics strategy.

Machine Learning Mistake 1

Inadequate Infrastructure: Managing various aspects of infrastructure surrounding the machine learning is the biggest challenge which organisations faces these days. Regularly used database management systems can sometimes fail under the variety and load of data that organizations look to collect and analyse today.

Ways to fix it: Check for the following things to ensure if the infrastructure is built to handle machine learning.

  1. Flexible Storage – A suitable organization-wide storage solution that is capable of meeting the data requirements and is capable of maturing with the technology advances should be designed. Data structure, usage and digital footprint should be considered while designing.
  2. Powerful Computing – A scalable, secure and powerful computing infrastructure allows data scientists to power through data preparation techniques and various models to reach the most ideal solution in the fastest time possible.
  3. Hardware Acceleration
  • When to use SSDs (soli state hard drives) – When tasks are I/O intensive like data preparation or software analytics that is disk enabled.
  • When to use GPUs (graphical processing units) – When tasks are computationally intensive which can be run in parallel like matrix algebra.
  • When it comes to distributed computing tasks where data is split across various connected computers, this helps reduce execution times and using a distributed environment that is suitable for machine learning can help better. Computing and storage resource consumption can prove to be very dynamic when it comes to machine learning.

Machine Learning Mistake 2

Data Quality Problems: The improvement of algorithms is often seen as the glamorous side of Machine learning and maximum time is spent preparing data and dealing with quality problems. Quality of Data is the key to obtain accurate results from your models. Some of the common data quality issues include:

  • Data which contains a huge amount of misleading or conflicting information – Noisy data.
  • When data consists of inconsistent values, categorical and character features with multiple levels, missing values, it is known as – Dirty data.
  • Data consisting of very few real values, it consists mostly of zeroes and missing values – Sparse data.
  • Biased or incomplete data – Inadequate data.

Ways to fix it?

Data governance and security should be addressed right at the beginning of a machine learning exercise. In Data preparation and integration, the data should be transformed into a logical format for consumption by machine learning algorithms and exploration of data is another key factor.

Machine Learning Mistake 3

Implementing Machine Learning Without Data Scientists: With the high requirement of employees, managing analytical content becomes even greater. Recruiting and retaining these technical experts who are in-demand has become the point of focus for all organizations. The most skilled analytics professionals that need a unique combination of mathematics, domain expertise and computer science are data scientists.

Ways to fix it?

Centres of excellence will work as an in-house analytics consultancy. The centre of excellence will allow for consolidation of all analytics talent in one place and allows for the use of analytical skills across the organization in an efficient manner.

Creating an internship program or recruitment program with universities is one way to go about recruiting fresh talent.

Talent development from within the organization. Invest in data science training for students who have a natural aptitude for problem solving and mathematics.

Machine Learning Mistake 4

Implementation without Strategy: A challenge is deciding when to incorporate newer and complex modelling methods into your analytics strategy. The move to machine learning may not even be required until the business needs and IT evolve.

Ways to fix it?
Machine learning should be positioned as an extension to the analytical processes in place. Using Machine learning to predict when a regression model is gong stale and when it needs to be refreshed is very useful. Model Factory Approach can help to build models automatically and can help in gain efficiency and accuracy. Ensemble modelling algorithms such as super learners, random forests etc. also provide a huge impact.

To effectively use machine learning in business, a clear understanding of ML in the broader scheme of things and by having familiarity with applications of machine learning is essentially required. Another key factor is to analyse the challenges that may be faced while using machine learning. Keeping an eye on the leaders in the field of machine learning will help greatly to avoid pitfalls.

The author is senior vice president – digital solutions, Indium Software.

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