Tag: Ml Algorithms

Currently, the two most dominant technologies in the world are machine learning (ML) and artificial intelligence (AI), as these aid numerous industries in resolving their business decisions. Therefore, to accelerate business-related decisions, IT professionals work on various business situations and develop data for AI and ML platforms.

The ML and AI platforms pick appropriate algorithms, provide answers based on predictions, and recommend solutions for your business; however, for the longest time, stakeholders have been worried about whether to trust AI and ML-based decisions, which has been a valid concern. Therefore, ML models are universally accepted as “black boxes,” as AI professionals could not once explain what happened to the data between the input and output.

However, the revolutionary concept of explainable AI (XAI) has transformed the way ML and AI engineering operate, making the process more convincing for stakeholders and AI professionals to implement these technologies into the business.

Why Is XAI Vital for AI Professionals?

Based on a report by Fair Isaac Corporation (FICO), more than 64% of IT professionals cannot explain how AI and ML models determine predictions and decision-making.

However, the Defense Advanced Research Project Agency (DARPA) resolved the queries of millions of AI professionals by developing “explainable AI” (XAI); the XAI explains the steps, from input to output, of the AI models, making the solutions more transparent and solving the problem of the black box.

Let’s consider an example. It has been noted that conventional ML algorithms can sometimes produce different results, which can make it challenging for IT professionals to understand how the AI system works and arrive at a particular conclusion.

After understanding the XAI framework, IT professionals got a clear and concise explanation of the factors that contribute to a specific output, enabling them to make better decisions by providing more transparency and accuracy into the underlying data and processes driving the organization.

With XAI, AI professionals can deal with numerous techniques that help them choose the correct algorithms and functions in an AI and ML lifecycle and explain the model’s outcome properly.

To Know More, Read Full Article @ https://ai-techpark.com/why-explainable-ai-is-important-for-it-professionals/

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What is ACI

Democratized Generative AI

In the last few years, artificial intelligence for IT operations (AIOps) and observability have been hot topics in the IT operations sector. Organizations are looking for improvements in development and operation processes as these technologies have become more accessible, with various benefits and challenges. With the power of artificial intelligence (AI), machine learning (ML), and natural language processing, IT professionals such as engineers, DevOps, SRE (Site Reliability Engineering) teams, and CIOs can detect and resolve incidents, drive operations, and optimize system performance.

Today, we will understand how AIOps and observability have benefited most enterprises and why they are important for your business.

The Challenges and Solutions of Observability and AIOps

AIOps and observability have been critical tools in modern IT operations that have changed the traditional way of managing data. However, IT professionals need help with certain challenges and limitations that can bottleneck the use of these tools properly. Let’s explore some key challenges and their solutions:

Complexity of Implementation

Implementing observability and AIOps involves a lot of complexity, as these technologies require investment in infrastructure and expertise to implement and maintain. Moreover, a shift in mindset from traditional IT operations, where monitoring and responding to issues are done manually, is also crucial.

Solution: The only way to overcome these challenges is by investing in proper training and infrastructure that supports AIOps and observability, along with continuous organizational improvement and learning. The IT teams should also embrace new technologies and methods to stay updated and competitive in the AI industry.

AIOps’s Limitation

Even though AIOps is a powerful tool, it has certain limitations as it can partially replace human expertise. On the other hand, ML can recognize trends and patterns, but it struggles with the underlying cause of an issue.

Solution: To solve these complex issues, human expertise is still needed, as small organizations may not require the complexity of AIOps. The IT teams have to intervene to identify patterns and trends with the help of the ML algorithm.

Organizations today are under pressure to keep their IT solutions and infrastructure up and running with minimal downtime. While it is a tough job and has become harder to achieve with modern architecture, AIOPs and observability coming together can help your company enjoy cost-effective solutions to data and IT issues.

To Know More, Read Full Article @ https://ai-techpark.com/observability-and-aiops/

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Event-driven Architecture In Hyper-automation

AI and RPA in Hyper-automation

In the fast-moving business world, IT professionals and enthusiasts cannot ignore the use of machine learning (ML) in their companies. Machine learning tends to give a better insight into improving business performance, like understanding trends and patterns that human eyes generally miss out on. Thus, Machine learning (ML) and artificial intelligence (AI) aren’t just words; rather, they have the potential to change the industry positively. Through this article, we will focus on the importance of implementing machine learning and its use cases in different industries that will benefit you in the present and future.

The Usefulness of ML in Different Industries

Machine learning is a game-changer, and let’s see here how different industries have made the best use of it:

Predictive Analytics for Recommendations

Predictive analytics are generally used to identify opportunities before an event occurs. For example, identifying the customers that have spent the most time on your e-commerce website will result in profit for your company in the long run. These insights are only possible through predictive analytics, which allows your company to optimize market spending and focus on acquiring customers that will generate profit.

 Automate Decision-making

Automated and intelligent decision-making solutions and tools can be used by you to make quick decisions for efficient teamwork. For instance, some industries require strict adherence to compliance, which can only be applied by decision-management tools that help in maintaining records of legal protocols. These tools can make quick decisions if the business fails to obey any compliance rules.

 Creating a Data-Driven Culture

Creating a data-driven culture helps in getting numbers and insights that are generated through data. A data-driven organization not only empowers your teams but also improves your decision-making efficiency and effectiveness. One such example of a data-driven culture is DBS Bank, which has embraced AI and data analytics to provide customers with personalized recommendations. This is helping the customers and the bank authorities make better financial decisions and also improving customer loyalty. By embracing a data-driven culture, DBS Bank has also invested in training employees in data analytics and big data.

Machine learning is an important tool for making automated decisions in various business processes. These models help you identify errors and make unbiased and informed decisions. By analyzing data through customer interaction, preference, and behavior, ML algorithms can help identify the correct patterns and trends, which will help your company in the long run.

To Know More, Read Full Article @ https://ai-techpark.com/ml-helps-make-decisions/ 

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