This year has been very eventful for artificial intelligence and machine learning. From being leveraged for quick drug discovery to fight coronavirus, to chatbots, and from quantum computing to analyzing customer purchase behavior trends, these disruptive technologies have helped several industries in inching closer to the digital age. Almost every industry imaginable, even retail and healthcare, were benefited from machine learning, and other AI aspects. Even before the global pandemic, companies were turning to adopt the said technologies to enforce disruption. Hence, despite the ghastly pandemic, the impacts of AI and machine learning have not been subdued. However, COVID will play a crucial role in determining the tech trends of AI and Machine learning next year.


While COVID provided the necessary impetus, several organizations fail to manage the complex lifecycle of AI and machine learning models. Enter ModelOps. It uses AutoAI and DevOps technologies like continuous integration and continuous deployment (CI/CD) for updating the models on a regular basis, which gives better results to the business. It helps companies in ways more than simply operationalizing and governing AI models. It allows scalability, total accountability for mission-critical activities, or bottlenecks in business. Further, ModelOps can configure a model for evaluation before it is actually deployed on the production. After the ModelOps is configured for a model, it can run easily from there.

Moreover, ModelOps can be used to deploy the models on Edge devices, Cloud Environment and AIoT devices. It can also run the model training for Supervised Learning, Reinforcement Learning, Unsupervised Learning, Deep Learning and Robotic Process Automation. Thus, owing to its flexibility and vast usability, it is set to become a huge trend in the coming years.


AI for Cybersecurity
Amid the COVID outbreak, cyber threats have increased multifold. Cyber-attacks like malware, threats, DDS attacks, ransomware, continue wrecking cybersecurity measures, steal sensitive information, etc.; adding up the costs of several enterprises and institutes. Hence, CSOs and CISOs plan to employ AI and machine learning-based tools to detect anomalies in existing systems, before a breach occurs and therefore minimize the losses due to cyber-attacks. These tools shall collect data from communications networks, digital activity and websites, third-party vendors and more, to assess patterns for shady or threatening practices, or even to pinpoint suspicious IP addresses. While the hackers are now using machine learning to launch their malicious threats, organizations will also train AI to outwit hackers. So, it is safe to assume that this trend is going to get more mainstream in coming years.


Understanding the New reality
It is true that COVID has influenced behavioral changes in customers. This includes shopping locally sourced items, necessary items and so on. Companies need to analyze the factors that play a prominent role in determining the purchase pattern of the customers, understand their expectations in the new reality, which surely extends beyond COVID. Almost every brand today, promises to deliver personalized services for its clients and patrons. However, the buyers now need to know the authenticity behind such claims, before endorsing a product or service. So, for these, companies must employ machine learning applications like sentimental analytics, predictive analytics to gain in-depth insight into what customers feel about their existing products and their anticipations. The data collected can help brand make informed decisions on how to enhance their offerings and resolve the pain points in the customer-brand relationship. This shall also help in retaining leads while generating new ones. For the latter, again machine learning tools can help identify the untapped market and suggest ways to reach it too. In coming years, business brands are going to use more such tools to target potential customers and expand their existing revenue sources and utilize the resources to gain a competitive edge over other market rivals.

In certain markets, companies may also rely on blockchain to ensure transparency, support data provenance, integrity, and usage tracking.


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