Part of human nature is anticipating ‘what’s next’ about most anything. So, it only makes sense to delve into a subject that is on the mind of both CXOs and technologists alike, Machine Learning (ML). So, what does the future hold for ML? In my modest opinion, there are several trends of Machine Learning (ML) for 2022. Those trends are based on how the global industry has been operating and moving over the last 12–18 months.
Internet of Things and Machine Learning
Both humans and machines are generating data, and that data is growing exponentially. Computers, edge devices, smart devices, interconnected systems and industrial Scada’s are generating massive amounts of data, yet only a very small portion of this data is analyzed. However, cloud technologies to intake, process and store real-time data are evolving rapidly and 5G network implementation will facilitate mid-stream to intake and analyze data. Experienced ML developers and IoT professionals are putting energy and attention into this field. Metaverse will play a key role in this section.
Automated Machine Learning
Due to the growth of data generated by systems and machines, the traditional process development of Machine Learning, where developers and engineers used to spend a good amount of time analyzing and modeling data, is no longer feasible. As the volume and veracity of data grows, ML engineers are being forced to speed up this process and make it more efficient. The best option to keep the pace is to apply Automated Machine Learning, known as AutoML, which can generate sustainable models.
Machine Learning on Cybersecurity
The continuous evolution of technology has transformed most applications and devices into smart elements of the IT landscape. That evolution has resulted in significant technological improvement, as well as much more data being generated. These smart devices are continuously connected to the Internet and there is a pressing need to increase cybersecurity. Technology professionals can use Machine Learning to build antivirus models that block hackers and any potential cyberattacks as well as reduce dangers by anticipating or identifying certain behaviors.
Low-Code/No-Code for Machine Learning Development
As data continues to grow, and impact the decisions made by most any professional, those professionals will need access to tools to help process that data. Since most business analysts lack software coding and programing skills a new challenge is being created, where they still have to deliver analytical solutions, without data processing expertise. Those professionals are being forced to acquire new skills related to data analysis, at a larger scale than ever before. Low-code/no-code applications for Machine Learning development are becoming more important and needed, facilitating business analysts to deliver analytical solutions.
It is interesting to see experienced Machine Learning engineers adopting low-code applications to develop Machine Learning solutions.
MLOps
Maintaining Machine Learning models and monitoring the evolution of new data, which is used to re-train those models, is one of the most crucial tasks to be done after deploying a Machine Learning solution. MLOps (Machine Learning Operationalization Management) is becoming a highly demanding discipline. There are a good number of tools available on the market to perform MLOps, spanning both the open-source and commercial software markets. We are seeing a growing number of frameworks also available that capture best practices to be adopted.
Machine Learning in RPA
In recent years we have witnessed a great adoption of Robotic Process Automation (RPA) by many organizations worldwide. RPA allows a system to automate any repetitive process, releasing man-hours to focus on more critical and impactful and creative tasks. The trade-off is that an RPA bot can process something that has been pre-defined. If a minimum deviation happens in that process, the RPA bot will fail. Using Machine Learning within RPA can solve this situation, giving organizations more capability and flexibility to respond faster to ‘acceptable’ changes in any process while focusing energy on critical tasks.
Ethics in Artificial Intelligence
With the increased adoption of Artificial Intelligence by regular users. Those users have experienced issues, as well as biased results produced by Artificial Intelligence applications. Those biased results of Artificial Intelligence have uncovered many human biases in many areas were Artificial Intelligence applications, among other ‘human biased behaviors’. Normalizing and correcting those Artificial Intelligence systems is needed. We are seeing also the regulation of Ethics in Artificial Intelligence in some countries, trying to set the ground in this field.
The Rise of Reinforcement Learning and Generative Adversarial Networks
As more unstructured data is being analyzed, Generative Adversarial Networks is a sophisticated neural network-based algorithm capable to produce plausible variations from existing distribution samples. In other words, it is capable to produce variations of pictures, video, text, and any other type of input. GANs are rapidly evolving and being adopted in many applications.
On the other hand, Reinforcement Learning is another sophisticated algorithm also based in neural networks but capable to learn by multiple stimuli: try-error, which is exactly how humans learn. Reinforcement Learning is being adopted more in industrial processes and game simulation, as well as cybersecurity.
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