Importance of Machine Learning Project Management

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If you are directing several machine learning training projects, you may have noticed that their importance is increasing rapidly. Having a repetitive process framework would be helpful for the team to generate many predictive models.

It is becoming necessary to understand how to manage ML projects, as the total number of members in a team executing the machine learning projects is increasing. Machine learning projects are very iterative. While going through your ML lifecycle, you will be iterating a section until you reach an adequate level of performance before going forward to the next task.

Data Collection and Labeling

All the machine learning pipelines use data that has a label of its own. Take Tesla Autopilot, for example. It predicts when cars are about to cut into your lane. This can be acquired systematically by simply observing when a car is changing its lane into Tesla’s land and then rewinding the video to label that a car is about to cut into a lane.

Another example: let’s say a social media company creates some model that predicts user engagement in order to ensure that people get in their news feed what is of interest to them. When users are given the content based on the prediction, they can check the engagement. This interaction can be turned into a labeled observation without any human effort.

Most of these machine learning training projects require multiple people, but if you are a single person labeling the data, it is good if you label your document to maintain consistency.

Active Learning

You don’t always have to label all data. And this is why in case you encounter large amounts of data, you must use active learning to figure out which data to label. Note that you must restrict the amount of time you spend on labeling because it is an expensive affair. If you can manage to label your entire data, then you should. Active learning, if done properly, can add an extra layer of complexity to your projects.

Machine Learning Project Management Questions

  1. What is the difference between Data Science, Machine Learning, Artificial Intelligence projects?
  • Data Science is a field that uses different scientific processes, techniques and algorithms, and certain systems to pull out knowledge and information from structured or unstructured data. It is a study that deals with tons of data s usinfusing modern techniques and tools.
  • Machine Learning contains algorithms and procedures that are required to build predictive models from given data. Machine learning training plays a major role in data science projects.
  • Artificial Intelligence is a lot different than machine learning. AI includes more other capabilities than machine learning possesses. It is an area in the computer world that focuses on building machines that work and react like human beings.
  1. Are machine learning projects difficult to manage?

Those who have not completed their machine learning training may face difficulties in project management. Here are some of the reasons why projects fail.

  • Poor project management
  • Predicting the time of task completion is difficult
  • Inability to define goals and adhere to them

For a team to know in advance what’s hard and what’s easy is not quite as easy as it sounds. There is a high probability that machine learning projects may fail without any suspected reason.

  1. Should I use a Software Development Project Management approach.?

One might think that since machine learning projects are as good as software projects, so we can use the approach we use when building software. If you think so too, you aren’t the only one! You can easily differentiate between the two projects with respect to the following factors.

  • Feasibility of a project
  • Knowing whether the system is working perfectly.
  • Tracking your progress
  • Task elimination.

Apart from these differences, machine learning projects involve many upfront works like data cleaning and data analyzing. This is totally different from software projects. Hence, many you will see that not all software development processes will work with a machine learning project.

To cut a long story short, there are parts of machine learning project development that are somewhat similar to software development projects, but there are more differences than similarities.

  1. How to use an agile framework for ML projects.?

Before understanding how to use the agile approach while building a machine learning project, it is advisable to hold on and recall your key concepts from your machine learning training.

Below are some key concepts:

  • Agile is a sequence of experimental cycles and iterative adaptation.
  • Each cycle aims to explore, build, and then observe the action of ML. After that, analyzing the observation to work on the following experiment
  • The end of an iteration should be marked after the competition of the process
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