Last year was really eventful for Artificial Intelligence and Machine Learning. These revolutionary innovations have enabled many industries to shift closer to the digital age. These technologies are leveraged for rapid drug development to battling coronavirus, chatbots, and quantum computing to examine consumer buying behavior patterns.
Machine learning and other AI elements have helped almost every conceivable sector, even retail and healthcare. To enforce disruption, well before the global pandemic, businesses switched to implementing the said technologies. Therefore, the results of AI and machine learning have not been subdued, considering the dreadful pandemic. However, COVID-19 will play a key role in evaluating next year’s technology developments in AI and machine learning.
Although COVID-19 provided the requisite impetus, many organizations have not handled the complex lifecycle of AI and machine learning models. Specify ModelOps. It uses AutoAI and DevOps technologies, like continuous integration and continuous deployment (CI/CD), to regularly update the models, giving the organization better performance. It helps businesses to operationalize and regulate AI models in ways more than just essential. It enables scalability, total accountability for mission-critical operations or business bottlenecks.
Besides, before it is deployed in production, ModelOps will configure an evaluation model. It can work quickly after that the ModelOps is configured for a model. Moreover, the models can be deployed on Edge, Cloud Environment, and AIoT devices via ModelOps. Supervised Learning, Reinforcement Learning, Unsupervised Learning, Deep Learning, and Robotic Process Automation model training can also be carried out. Thus, it is expected to become a significant trend in the coming years due to its versatility and vast usability.
Artificial Intelligence for Cybersecurity
Cyber threats have risen multifold in the wake of the COVID outbreak. Cyber-attacks such as malware, threats, DDS attacks, ransomware, cybersecurity measures continue to be disrupted, confidential information stolen, etc. It is adding up to the costs of many businesses and institutes. Therefore, before a breach occurs, CSOs aims to use AI and machine learning-based tools to detect anomalies in existing networks. It thereby mitigates losses due to cyber-attacks.
These tools gather data from communications networks, digital activities and websites, third-party vendors, and more. It determines trends of obscure or threatening practices or even recognizes unusual IP addresses. While hackers are now using machine learning to initiate their malicious attacks, AI will also be trained by organizations to outwit hackers. So, it is fair to say that in coming years this trend will become more popular.
Understanding the New Reality
COVID may have affected behavioral changes in customers. This involves shopping for items sourced locally, necessary items, and so on. Companies need to consider their preferences in modern reality, which goes beyond COVID. It is essential to examine the variables that play a prominent role in deciding customers’ purchasing patterns. Today, almost every brand promises to offer tailored services to its clients and patrons.
But, before endorsing a product or service, customers now need to know the authenticity behind those statements. For these, businesses must use machine learning technologies like predictive analytics to gain in-depth insight into what customers feel about their existing products.
The collected data will help brands make informed decisions to develop their offerings and resolve the pain points in customer and brand interaction. This will also aid in maintaining leads while creating new ones. Machine learning software can again help identify the untapped demand for the latter and suggest ways to hit it, too.
Business brands will use more such tools in the coming years to target new customers and increase their current sources of sales. And use resources to gain a competitive advantage over other competitors in the industry. Businesses can also rely on blockchain in some markets to ensure transparency, support data provenance, integrity, and usage tracking.