While artificial intelligence is considered the golden age within technology and projects, it is not as plain sailing as it sounds. There are various obstacles that engineers and developers a-like will face because machine learning problems require a whole different skill set than just coding and logic definition. Automation has more applications than ever before. Understanding the limitations and challenges ML specialists face is essential so that expectations can be simmered regarding what machine learning developers and engineers can do.
The ML Developer Challenges
Preparing the Data
Through preparation, integration, and coding capabilities, developers would be required to ensure that ML algorithm processes work. Data preparation is one of the critical challenges machine learning specialists face. Cleansing, labeling, and general checking are essential responsibilities for a developer, not only in internal but also external environments.
The recruitment of a team is also a key challenge. Only 7.9% of data scientists work specifically as machine learning developers and engineers. Without the necessary personnel with the right skills, this heightens the risk of staying behind competitors and being outpaced by newly emerged start-ups.
Developing the Program and Algorithm
Traditional software development can be straightforward as long as you understand the coding logic. Machine learning, however, has more layers. Engineers not only build the data and program logic to develop an output, but they would also need to:
- organize the data massive;
- train the algorithm;
- output and write a program that ensures the machine learns the data to perform the correct actions.
It makes things more complicated and increases the uncertainties where even the most experienced coding developers may not have the answer.
Developing the Data for the Algorithm
After developing the algorithm, the correct data set needs to be prepared. Information is also not cheap, either. You will need to understand what problem you want the ML algorithm to resolve. The data needs to ensure the machine learns the appropriate information set to deliver the correct output.
Data Privacy Breaches and Complaints
Further development of machine learning technologies tends to be blocked due to data privacy concerns. The amounts of data used for the repetition of tasks and capturing patterns require excessive processed massive. However, neglect of digital privacy, such as transparent use, mainly concerns personal information. And the world community is working on the problem.
The new General Data Protection Regulations (GDPR) has made it more restrictive in the way personal information can be used. Big data technologies ensure that the regulations cannot be violated.
When being gifted with specific programming skills, choose the correct ML framework is challenging. Luckily, there are a variety of ML models available to cater for a variety of languages. However, the right toolkit may not be available for individual developers where an ML framework may not be compatible with a language. However, if you’re a Python developer, the ML journey will be smoother.
What can we do to answer challenges?
Challenges we face while practicing ML and engaging in a machine learning project increases risks of failure. But if we’ll get thru, the reward will be enormous. To succeed, we need to be patient, respect the challenges the ML brings, and find people who genuinely understand machine learning.