Machine learning developers are in great demand and one needs to have a good set of technical as well as soft skills in order to ace in machine learning programming. This guide will help rookie as well as established machine learning developers to know critical factors that can define their success or failure.

Here’s a guide that will help one identify the traits of successful machine learning developer.

  1. A good machine learning developer should be able to enjoy the iterative process involved in development. To initiate the process of developing a machine learning system, one needs to develop a simple version 0.1 quickly. Once the base is ready, a good machine learning engineer will be able to improvise and iterate the system in different phases.
  2. Iteration can be lengthy and time-consuming. A good machine learning developer should be able to know the viability of the project and needs to stop at a certain point. Improvisation is a never-ending process and one needs to identify the point where the value of the project doesn’t exceed the efforts & time put in.
  3. 100% success is never possible in machine learning programming and failure at some stage is imminent. A good machine learning developer knows that models and experiments can fail and one needs to constantly try and not get hampered by setbacks.
  4. Curiosity is a great trait to have in any individual. It drives new ideas and solutions. Likewise, a good machine learning developer needs to be curious and ask questions that can lead to new answers.
  5. Analysis & interpretation of data is amongst the key skills required in machine learning developer. One should be quick in identifying patterns in the data and visualize it through several mediums.
  6. Understanding metrics can really differentiate and good and mediocre machine learning developer. A good sense of metrics and the ability to define metrics can be the game-changer. Machine learning programming also demands blind experiments and one should be comfortable with precision, conversion rates, ROC etc.
  7. Metrics give a great overall view of how the system has been developed and its success or failure parameters. However, a good machine learning developer also looks for individual examples directly to gain an overall perspective. To dig in manually & scrutinizing random samples of data can help give great insights to machine developer.
  8. Developing a generalized approach of fixing bugs or misclassifications can go a long way for machine learning developer. A developer would spend more than necessary time behind fixing individual bugs, which would also lengthen the project time and make it complex. A good developer would collect all the data and issues, find patterns and resolve it on a larger scale in the next round of update.
  9. Donning the hat of customers and thinking from the end-user perspective is critical while developing a model. One can develop a model based on a solo perspective, but developing something from end user’s angle is critical, and that’s what defines the success and failure of the model. One can be easily driven biases and individual decisions, but a good machine learning developer always keeps the end-user perspective in the center of all the processes.

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