Generative AI in IT OPS

Unlocking Efficiency and Innovation: Exploring the Potential of Generative AI in IT Operations

Introduction to Generative AI in IT Operations

In recent years, the field of artificial intelligence (AI) has seen tremendous advancements. One particular branch of AI that has caught the attention of the IT industry is generative AI. Generative AI refers to the ability of machines to generate new and creative outputs, such as images, text, or even code. This technology has the potential to revolutionize IT operations by unlocking efficiency and innovation like never before.

Understanding the Potential of Generative AI in IT Operations

Generative AI has the power to transform IT operations by automating and streamlining various tasks. For instance, it can be used to automatically generate code based on specific requirements, reducing the time and effort required for development. It can also assist in troubleshooting and problem-solving by generating potential solutions based on available data and past experiences. Many observability tool providers are already started incorporating Generative AI functionality part of the their product roadmap & Features.

Moreover, generative AI can enhance the efficiency of IT Infrastructure operations by optimizing resource allocation and workload management for various application capacity demands. By analyzing historical data and patterns, it can predict future demand and allocate resources accordingly, ensuring optimal performance and cost-effectiveness. Additionally, it can automate tedious and repetitive tasks, freeing up IT professionals to focus on more strategic and innovative initiatives.

The Benefits of Using Generative AI in IT Operations

The adoption of generative AI in IT operations offers numerous benefits. Firstly, it can significantly improve productivity by automating time-consuming tasks. This allows IT professionals to dedicate their time and energy towards more high-value activities that require their expertise. As a result, organizations can achieve higher efficiency and throughput in their IT operations.

Secondly, generative AI can enhance innovation within IT operations. By generating new ideas, solutions, and approaches, it enables IT teams to think outside the box and come up with novel strategies. This can lead to the development of innovative products and services, ultimately giving organizations a competitive edge in the market.

Furthermore, generative AI can improve the accuracy and reliability of IT operations. Through advanced algorithms and machine learning techniques, it can analyze vast amounts of data and identify patterns that humans may overlook. This enables organizations to make data-driven decisions and reduce the risk of errors or failures.

Real-Life Applications of Generative AI in IT Operations

Generative AI has already found practical applications in various aspects of IT operations. One such application is in software development based on my reading many articles. By using generative AI, developers can automatically generate code snippets or entire programs based on predefined specifications. It also supports converting code written in old legacy language to the latest one.  This not only speeds up the development process but also reduces the likelihood of errors and bugs. Generative AI can also support in creating a complete documentation of developed code.

Another application of generative AI is in network management. IT teams can leverage generative AI algorithms to analyse network traffic data for the potential large telecom service provider and identify potential bottlenecks or security threats. This proactive approach allows organizations to take preventive measures and ensure the smooth operation of their network infrastructure.

Generative AI can also be applied to IT service management. It’s my another favourite topic. By analysing historical service data and customer feedback, generative AI algorithms can generate recommendations for improving service quality and customer satisfaction and first level prescriptive analytics. This helps organizations continuously improve their IT services and meet the evolving needs of their customers.

Please refer my previous blogs written couple of years back on Machine Learning – AI in IT Operation.

https://www.linkedin.com/pulse/machine-learning-operations-sampath-manickam?trackingId=M4it2Dc3SeCGhSdS0pNWKA%3D%3D&lipi=urn%3Ali%3Apage%3Ad_flagship3_detail_base%3B7uxmxY4lQ1yY9XBuriswPg%3D%3D

Challenges and Limitations of Generative AI in IT Operations

While generative AI has immense potential, it also faces certain challenges and limitations. One of the main challenges is the availability and quality of data. Generative AI algorithms require large amounts of high-quality data to generate accurate and reliable outputs. However, obtaining such data can be challenging, especially in complex IT environments where data may be fragmented or incomplete.

Another challenge is the ethical implications of generative AI. As AI systems become more sophisticated, there is a concern that they may generate outputs that are biased, discriminatory, or even malicious. Ensuring ethical and responsible use of generative AI in IT operations requires careful consideration and proactive measures to mitigate these risks.

Additionally, generative AI algorithms may struggle with generating outputs that are truly innovative and creative. While they can generate outputs based on existing patterns and data, they may not possess the ability to come up with entirely new and ground-breaking ideas. This limitation highlights the importance of human creativity and expertise in complementing the capabilities of generative AI. An another limitation may due to regulatory compliance requirements in the respective Industry or country,  as Generative AI is based on open cloud based model and many Industries & Regulator are yet to adapt and  many global enterprises don’t prefer to share their internal data on Cloud or for the larger community unless it is of social market data that can be shared.

Implementing Generative AI in IT Operations: Best Practices

To successfully implement generative AI in IT operations, organizations should follow a set of best practices. Firstly, it is crucial to define clear goals and objectives for using generative AI. This will ensure that the technology is aligned with the organization’s strategic priorities and addresses specific pain points or challenges in IT operations.

Secondly, organizations should invest in robust data management and infrastructure. This includes collecting and storing high-quality data, ensuring data privacy and security, and creating a scalable and efficient infrastructure to support generative AI algorithms.

Furthermore, organizations should foster a culture of collaboration between IT professionals and generative AI systems. This involves building trust and understanding between humans and machines, and leveraging the unique strengths of each. By combining human expertise and creativity with the analytical capabilities of generative AI, organizations can achieve optimal results in their IT operations.

Training and Upskilling for Generative AI in IT Operations

The successful adoption of generative AI in IT operations requires a skilled workforce that can leverage the technology effectively. Therefore, organizations should invest in training and upskilling their IT professionals to ensure they have the necessary knowledge and expertise.

Training programs should focus on developing a deep understanding of generative AI concepts, algorithms, and tools. IT professionals should also learn how to integrate generative AI into existing IT systems and processes, as well as how to interpret and validate the outputs generated by the technology.

Additionally, organizations should encourage continuous learning and experimentation with generative AI. This can be done through workshops, hackathons, or dedicated innovation labs where IT professionals can explore new applications and use cases of generative AI in IT operations.

Tools and Platforms for Generative AI in IT Operations

Several tools and platforms are available to support the implementation of generative AI in IT operations. These tools provide a range of functionalities, from training and deploying generative AI models to analyzing and visualizing the generated outputs.

One popular tool is TensorFlow, an open-source library developed by Google. TensorFlow provides a comprehensive framework for building and deploying generative AI models, and it supports various programming languages, making it accessible to a wide range of IT professionals.

Another notable platform is OpenAI, which offers a suite of generative AI models and tools. OpenAI’s models have been used for a variety of applications, including text generation, image synthesis, and even video game AI. The platform provides a user-friendly interface and extensive documentation, enabling organizations to quickly start experimenting with generative AI.

Conclusion: The Future of Generative AI in IT Operations

Generative AI holds immense potential for transforming IT operations and unlocking efficiency and innovation. By automating tasks, enhancing productivity, and generating new ideas, generative AI can revolutionize the way IT professionals work and contribute to the success of organizations.

However, the adoption of generative AI also comes with challenges and limitations. Organizations must carefully consider the ethical implications, ensure the availability of high-quality data, and recognize the complementary role of human expertise.

As the field of generative AI continues to evolve, it is important for organizations to stay informed and embrace this technology as a strategic advantage. By investing in training, leveraging the right tools and platforms, and learning from successful case studies, organizations can harness the power of generative AI to drive innovation and achieve operational excellence in IT operations.

#GenAI #CIO #ArtificialIntelligence #AI #ITOperation #ITAutomation #FutureIT #itleadership #digitaltransformations #itoperations #AIOPS #CTO

Assessing Digital Trends

Introduction

As we navigate the ever-changing landscape of the digital world, it is crucial for organizations to stay ahead of the curve and anticipate the trends that will shape their industries. In order to do so, gathering and analysing data is key. This article aims to explore the weak and strong signals of digital trends and assess their potential impact on organizations and industries. By evaluating the immediate threats and opportunities they pose, we can determine the best initiatives to pilot in the short term.

Mechanisms in Place to Capture and Examine Digital Trends

To effectively capture and examine digital trends, it is essential to have robust mechanisms in place. The following are three key mechanisms to ensure the data collected is relevant, accurate, and actionable:

1. The first mechanism is data analytics. By leveraging advanced analytics tools, organizations can extract valuable insights from large volumes of data. This allows them to identify patterns, spot emerging trends, and make informed decisions based on real-time information.

• The second mechanism is market research. Through surveys, interviews, and focus groups, organizations can gather qualitative and quantitative data on consumer behavior, market dynamics, and industry trends. This helps them understand the evolving needs and preferences of their target audience and adapt their strategies accordingly.

• Lastly, social listening plays a crucial role in capturing digital trends. By monitoring social media platforms, organizations can track conversations, sentiment, and emerging topics related to their industry. This provides valuable insights into consumer opinions, emerging trends, and potential threats or opportunities.

Opportunities and Threats Posed by Technology

Technology brings both opportunities and threats to organizations. The following are four opportunities and four threats that technology poses for their respective organizations.

1. Opportunities:

1. Enhanced customer experience: Technology enables organizations to personalize interactions, offer seamless omni-channel experiences, and leverage artificial intelligence to anticipate customer needs.
2. Increased operational efficiency: Automation, robotics, and data analytics can streamline processes, reduce costs, and improve productivity across various departments.
3. Expanded market reach: Digital platforms and e-commerce enable organizations to reach a global audience, expand their customer base, and explore new markets without geographical limitations.
4. Innovation and product development: Technology provides organizations with tools and platforms to drive innovation, develop new products and services, and stay competitive in a rapidly evolving marketplace.

b. Threats:

1. Disruption from new entrants: Technology lowers barriers to entry, allowing startups and disruptors to enter established markets with innovative business models and solutions.
2. Cybersecurity risks: With increased reliance on digital systems and data, organizations face the constant threat of cyberattacks, data breaches, and privacy concerns.
3. Talent acquisition and retention: The rapid pace of technological advancements creates a demand for skilled professionals who can navigate and leverage emerging technologies. Organizations must compete for top talent in the industry.
4. Legacy systems and resistance to change: Organizations with outdated infrastructure and resistance to change may struggle to keep up with the pace of technological advancements, hindering innovation and growth.

Evaluating Alternatives Based on Impact and Organizational Readiness

When considering initiatives to pilot in the short term, it is crucial to evaluate their potential impact and the organization’s readiness to implement them. The following are the sample cases of four alternatives have been accessed based on these factors mentioned below.

Alternative 1: Implementing a customer relationship management (CRM) system

Potential Impact: High – A CRM system can enhance customer experience, improve data management, and drive sales growth.

Organizational Readiness: Moderate – The organization has some technological infrastructure in place but may require additional training and resources to fully implement a CRM system.

Alternative 2: Investing in artificial intelligence (AI) and machine learning (ML) capabilities

Potential Impact: High – AI and ML can automate processes, improve decision-making, and drive innovation.

Organizational Readiness: Low – The organization lacks the necessary infrastructure and expertise to implement AI and ML technologies.

Alternative 3: Launching a mobile app

Potential Impact: Moderate – A mobile app can improve customer engagement and provide a convenient platform for transactions.

Organizational Readiness: High – The organization already has experience in developing digital solutions and has the necessary resources to launch a mobile app.

Alternative 4: Embracing cloud computing

Potential Impact: Moderate – Cloud computing can enhance scalability, flexibility, and cost-efficiency.

Organizational Readiness: High – The organization has already migrated some of its operations to the cloud and has a strong understanding of its benefits.

Responding to High-Impact Opportunities and Threats

In order to respond effectively to high-impact opportunities and threats, participants in this study have explored various strategies and assessed their organizational readiness.

Opportunities: By leveraging their existing strengths and resources, organizations can respond to the identified opportunities. For example, enhancing customer experience through personalized interactions can be achieved by investing in customer analytics and implementing targeted marketing campaigns.

Threats: To mitigate the potential threats posed by technology, organizations need to prioritize cybersecurity measures, invest in employee training and education, and establish partnerships with technology experts and consultants.

Prioritizing Response Based on Impact and Organizational Readiness

To determine the priority of their response, participants have used a 2×2 matrix, categorizing opportunities and threats based on potential impact and organizational readiness. This matrix helps identify initiatives that have a high potential impact and are aligned with the organization’s readiness.

Impact	High Impact, Low Organizational Readiness: Initiatives falling into this quadrant require careful planning and resource allocation to ensure successful implementation. Organizations may need to invest in training, infrastructure, and partnerships to fully leverage these opportunities.	High Impact, High Organizational Readiness:  Initiatives falling into this quadrant should be prioritized as they offer significant opportunities for growth and success. Examples include launching a mobile app and investing in AI and ML capabilities
	Low Impact, Low Organizational Readiness:  Initiatives falling into this quadrant may not be a priority at the moment. Organizations should focus on higher impact opportunities and build their readiness before considering these initiatives	Low Impact, High Organizational Readiness:  Initiatives falling into this quadrant can be pursued, but with a lower priority. These may include implementing a CRM system and embracing cloud computing, which offer moderate benefits but require less immediate attention.
	Organizational Readiness

Exploring Digital Pilots for New Capabilities

In order to develop new capabilities and stay ahead in the digital landscape, participants have identified at least three digital pilots that can be launched. These pilots provide organizations with the opportunity to test new technologies, business models, and strategies.

1. Implementing a chatbot: By leveraging artificial intelligence, organizations can develop chatbots to automate customer interactions, provide personalized support, and enhance customer satisfaction.
2. Embracing data-driven decision-making: Organizations can invest in data analytics tools and develop processes to gather, analyze, and visualize data. This enables them to make informed decisions and drive innovation.
3. Exploring blockchain technology: Blockchain has the potential to revolutionize various industries by providing secure, transparent, and decentralized solutions. Organizations can pilot blockchain-based projects to explore its applications and benefits.

Conclusion

In conclusion, assessing digital trends based on the data gathered is crucial for organizations to navigate the digital landscape successfully. By evaluating the impact, immediacy, and organizational readiness, organizations can identify the most promising opportunities and mitigate potential threats. Through strategic initiatives and digital pilots, organizations can develop new capabilities and stay ahead in an ever-evolving digital world. It is essential for organizations to embrace digital transformation and leverage emerging technologies to drive innovation, enhance customer experience, and ensure long-term success.

The Key Ingredient — Intelligence Quotients for Effective Leadership

“IQ represents your knowledge, EQ represents your character, SQ represents your charisma and AQ represents your resilience”.

As a leader, it is crucial to understand the different types of intelligence that contribute to effective leadership.

According to Psychologists, there are actually Four Types of “Intelligence:”

1. Intelligence Quotient (IQ)
2. Emotional Quotient (EQ)
3. Social Quotient (SQ)
4. Adversity Quotient (AQ)

Intelligence Quotient (IQ) : This is the original measure of your level of comprehension. You need IQ to solve maths, memorize things (like remembering multiplication tables & formulas in school) and recall lessons. It refers to cognitive abilities, such as problem-solving, critical thinking, and decision-making. A high IQ is beneficial for analysing complex situations and making sound judgments.

Emotional Intelligence (EQ) : This is the measure of your ability to maintain peace within oneself and others, keep to time, be responsible, be honest, respect boundaries, be humble, genuine and considerate. It involves empathy, self-awareness, and emotional regulation. EQ allows leaders to understand the needs and concerns of their team members, family members, enabling them to respond appropriately and build strong relationships.

Social Quotient (SQ) : This is the measure of your ability to build a network of friends and maintain it over a long period of time. SQ refers to the ability to understand and navigate social dynamics and essential for today’s Internet of World. Leaders with high SQ are skilled at building relationships, resolving conflicts, influencing irrespective of their hierarchical level and fostering collaboration within the team. They have a keen awareness of group dynamics and can effectively communicate and influence others.

People who have higher EQ and SQ tend to go further in life than those with a high IQ but low EQ and SQ. Most schools capitalize on improving IQ levels while EQ and SQ are played down. A man of high IQ can end up being employed by a man of high EQ and SQ even though he has an average IQ.

“Your IQ represents Knowledge, EQ represents your Character, while your SQ represents your Charisma”.

Now there is a 4th one, a new paradigm.

Adaptability Quotient (AQ) : This is the measure of your ability to go through a real tough patch in life (everyone will go through it at least once or more during their lifecyle) and come out of it without losing the mind. When faced with troubles, AQ determines who will give up, who will abandon their family or friends or life itself. It is the ability to adapt and thrive in changing or highly stressful environments. I recall a movie called The Shawshank Redemption that teaches the management lesson on adapting to the environment, and it is your resilience.

In today’s fast-paced world, leaders need to be agile and flexible, able to embrace new ideas and technologies. AQ enables leaders to navigate uncertainty, ever demanding change and guide their teams through transitions.

The role of EQ, IQ, SQ, and AQ in effective leadership and psychological stability

Effective leadership requires a balance of EQ, IQ, SQ, and AQ. These intelligences work together to create a psychologically stable environment and drive success to self, family, organization and in turn to the whole society and the world.

EQ plays a crucial role in leadership by fostering strong relationships and effective communication. Leaders with high EQ are more attuned to the emotions and needs of their team members, creating a supportive and inclusive work culture. This leads to increased employee engagement, satisfaction, and productivity. Additionally, EQ enables leaders to manage their own emotions and stress, allowing them to make rational decisions even in challenging situations.

IQ is equally important in leadership as it enables leaders to analyze complex problems, strategize, and make informed decisions. A high IQ provides leaders with the cognitive abilities necessary to understand the intricacies of their industry and adapt to changing market conditions. It also helps leaders identify opportunities and risks, leading to better business outcomes.

SQ plays a critical role in effective leadership by fostering collaboration and teamwork. Leaders with high SQ are skilled at building trust, resolving conflicts, and creating a harmonious work environment. They create a sense of belonging and motivate their team members to work together towards a common goal. This leads to improved team performance, innovation, and overall organizational success.

Lastly, AQ is essential in today’s rapidly changing world. Leaders with high AQ are quick to adapt to new technologies, market trends, and industry disruptions. The companies who have survived in COVID with leaders who had great AQs! They embrace change and encourage their teams to do the same, creating a culture of continuous learning and growth. High AQ enables leaders to navigate uncertainty, seize opportunities, and stay ahead of the competition.

Conclusion:

In conclusion, the modern workplace requires a combination of Emotional Intelligence (EQ), Social Quotient (SQ), and Adaptability Quotient (AQ) that are the key ingredient for effective leadership along with IQ.

Basically, “IQ represents your knowledge, EQ represents your character, SQ represents your charisma and AQ represents your resilience”.

These intelligences, when balanced with Intelligence Quotient (IQ), contribute to psychological stability and drive success.

By understanding and developing these intelligences, leaders can create a positive and productive work culture, inspire their team members, and achieve organizational goals. Aspiring leaders should strive to enhance their EQ, SQ, and AQ alongside their IQ, as these qualities are the key ingredients for effective leadership in the modern world.

Edge Data Center in Digital India

Why does Digital India need Edge Data Centers? This was a question asked by one of my old friend who is senior IT Professional in the Industry. I thought of capturing the same in this blog.

The internet as we know is continuously evolving. India has 560 Million Internet users with 310 Million Active social media users as per the recent analyst report and these numbers are growing rapidly. As the Internet Infrastructure is growing consistently, we are moving towards more and more connected and inclusive digital economy, with large amounts of data (any type of data – Text, Image, Audio, Video, or any unstructured data.) being generated either by humans and connected devices, accessible from Internet by many people or an Intelligent device using any device from any location.

This data typically needs to be stored, managed and shared through cloud from data centers that aid Digital transformation, as the flow of data has moved from on-premise to the cloud model of consumption as of today. The new paradigm shift is from centralized to distributed computing that leans towards edge computing and Edge Data Center. Around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, Gartner predicts this figure will reach 75%. As demand grows due to the growth of many new business use cases forcing a greater percentage of data to be residing within the country and that too closer to the end-users than ever before. This promises India to become one of the biggest global destinations for data center business based on the present Indian business and consumer landscape. By accepting this need, we are steadily moving towards the next wave of growth for Hyper-scale Data center including edge computing and edge data centers.

What is Edge Data Center?

An edge data center is a smaller data center that provides cloud computing support and processing closer to the place of operation instead of a centralized data center. It can provide services independently, with a centralized data center to back them up with services and analytics, effectively expanding the edge of the network along with compute and storage capability closer to the end-user. This reduces the need for multiple Hyper scale data centers for a single enterprise or large DC in each edge location.

Since they are physically closer to the end-users, edge data centers improve the speed of data processing, response times and reduce latency. Not only is the architecture faster and more efficient for transferring and processing data, but edge computing is also cost-effective, scalable and agile. It is no wonder why many industry leaders are using edge data centers to reach out to many locations by investing on it to improve user experience and reduce operational costs. Despite being at the edge of the network, edge data centers may provide as much as 75% of local internet content to the surrounding market/region.

Why do we need Edge Data Center?

Certain type of Application needs to process data in real-time with ultra-low network latency to quickly respond to a data point or business requirements. The new business use cases based on technologies like 5G, IoT, Blockchain, Content Delivery Network(CDN), real-time video streaming & real-time Video Analytics, Interactive online gaming, Connected factories, smart cities, Smart Home, driverless car, remote online health care, online shopping, Multi-party Video conferencing, etc. definitely need Edge Data Center and. Edge computing. These next Generation Technologies/ business use cases demand higher performance need of 1 to 5 milliseconds (ms) latency across the network.

Edge Data Center Market trends:

• The number of connected devices will be 38.6 billion worldwide by 2025 and 50 billion by 2030 according to the latest research from Strategy Analytics.

• The total edge computing market size is expected to grow from US$ 2.8 billion in 2019 to US$ 9.0 billion by 2024, at a CAGR of 26.5% as per analytics insight report.

• Microsoft CEO Satya Nadella said that the future of computing could actually be at the edge, where computing is done locally before data is then transferred to the cloud for AI and Machine Learning purposes. What goes around, comes around. 

• By 2022, more than half of enterprise-generated data will be created and processed outside of data centers, and outside of the cloud as per Gartner.

• 60% of servers will be located in an Edge Data Center by 2025 as per Bell Labs.

Use Cases

There are many use cases across Industry sectors that are available and few of the examples are given below:

• Connected Car and Autopilot: The next wave of massive growth and big investment is happening in Driverless car. Assume that the internet connection works at 150 to 250 milliseconds in order to talk to a far-away data center and Artificial Intelligence is driving a car. Can AI-driven car need to respond to microseconds or wait for 150+ ms? This will convert into approximately 20 to 21 feet and the difference could be car might dodge pedestrian versus halting just in time.

• Augmented reality (AR) and virtual reality (VR): Creating entirely virtual worlds or overlaying digital images and graphics on top of the real world requires a lot of processing power. One of the options is to use handheld devices like Mobile phones to deliver that horsepower. But the tradeoff is extremely short battery life. Edge Data Center addresses those obstacles by moving the processing power to the local cloud in a seamless way.

• Retail Industry: Creating more real in-store environments with technologies like AR in the near future.

• Smart Home, Smart office automation & Smart Factories: Managing the entire home or office or factories using multiple sensors for energy savings by turning on lights on command or changing the temperature and auto-notifications on security issues, Image recognition and allowing only approved guest/ visitor to home or office, car number plate recognition based actions and many more. With edge computing, it will be possible for them to happen in near real-time.

• Remote online Health care: The next biggest change 5G, IoT and Edge computing can bring is in the health care industry. The expert doctor can even perform surgery remotely for the patient in a small nursing home or robotic surgeries. Ultra-low latency, need for high image & real-time video processing along with real-time video analytics play a crucial role in saving a patient life or curing the diseases just in time.

• Predictive Maintenance: Help to detect machines that are in danger of breaking and find the right fix before they do.

• Video Survillence: Handle data at the edge rather than sending to the cloud that takes a longer time to respond. The in-built memory for storage & process power in such an end device (IoT device) or edge computing would play tremendous value in a real-life example for Video Security Survillence.

• Other User cases: Devices such as robots, drones and wearables using AI will require considerable computing power. Since centralized data centers would introduce too much latency, industrial edge data centers are ideally suited to these technologies as compared to a distant central data center. Moreover, the availability of such devices and technologies even in hard-to-reach remote or rural locations can drastically better the quality of service.

Securing the Edge

It is not all smooth sailing as, no matter how innovative the technology is. The collection of data and network expansion with so many devices in edge location opens up additional security risks. Present in many different locations, edge data centers might be remotely located or not as well monitored like centralized data centers. With the increasing point of storage and processing, Security vulnerabilities are bound to increase.

Complicating things further, diverse uses of IoT and more do not always follow traditional IT hardware protocol. This means that configuration and software updates are needed throughout the device’s life cycle but, given their small sizes, they might not have been built for security or timely updates. Edge data centers need to be designed with a lot of consideration such as securing security holes apart from designing it for better performance. That way, you can guarantee long-term support and security fixes in a timely manner rather than going there physically. Edge nodes need to be built with intelligence decision-making capabilities. Many of the OEMs / ISVs are started building an intelligent device that plays a vital role at the Edge not only providing compute & Storage power and also provides basic security features. 

The Enterprise should extend their security perimeter by deploying proper physical security protection, data encryption at the source level and in motion, using advanced Layer 7 and bot protection, DDOS protection. The next-generation security appliances that can have an ability to autonomous protection strategies, end-point protection services (e.g., antivirus), malware protections along with SIEM tool – will secure the edge faster in response to real-time attacks. Artificial Intelligence (AI) and Machine learning (ML) platforms will be playing a critical role in securing the Edge and provides many other benefits as given below.

AI/ML role in Edge Data Center

The Edge Data Center is smaller and typically Un-manned or only unskilled persons would be available in such a data center. With the advance of Artificial Intelligence (AI) & Machine Learning (ML) technologies along with IoT devices, we can secure the complete Edge Data center & associated devices. i.e. Remotely monitor, control & manage each every component, functionalities and associated Key Performance Indicators (KPIs) for both Passive and active infrastructure including physical and logical security, Temperature, Humidity, Vandalism, Airflow, Air pressure, Smoke, Water leaks, Access, Motion, Active power, Apparent power, Supplied energy, Energy savings, fuel utilization, server Hardening with respect to security vulnerabilities, IT availability & performance threshold monitoring- associated actions for scaling up and scaling down with the complete log management, real-time data analysis, doing predictive and prescriptive analysis as part of remote IT Operational tasks (refer my previous article on Machine learning in IT Operations https://www.linkedin.com/posts/msampath_cio-artificialintelligence-ai-activity-6622472413009534976-YCSs ) and thereby control edge data center end to end. This would ensure security and data privacy, on a massively scaled and distributed edge infrastructure using AI/ML platform. Not only that, dev-ops teams can do provisioning automation and push all new features & upgrades promptly.

Way Forward

The last decade has seen enormous strides in the industry with the introduction of 3G and 4G services boosting adoption. The roll-out of 5G services and other use cases for IoT are expected to garner huge demand in near future. As per the Economic Survey 2018-19, India’s projected data growth over the next few years is forecast to be USD 5 trillion by 2024.

With a framework in place, companies can extend their network services into areas that were previously beyond the reach of traditional architecture. The development of 5G data, the burgeoning population and improved facilities all point to a future in which edge data centers play a big part in the technological landscape of Digital transformation. It would be wise to keep an eye out on the years to come so that we are ready with practical solutions to industrial and commercial applications for edge data centers. Edge Data Center will become a lifeline and one of the key revenue generating engine for Telco’s, cloud, IoT, CDN and content providers will be consumed across Industry verticals.

TOP 17 Project Management Methodologies

https://www.cio.com/article/2950579/how-to-pick-a-project-management-methodology.html?fbclid=IwAR3rQxGk_IN2ytMe3FxxSL11zBuxZkwlJRB-9O5wRyeK0tU7Av0ZChdjQRQ

Machine Learning in IT Operations

Machine learning is a branch of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. It has a capability to play a significant role in improving IT operations in terms of incident management, root cause analysis, run-book automation and avoidance of future problems and to maintain the highest IT service availability to the end customers.

Many enterprises have begun introducing machine-learning and artificial intelligence platforms and automation as part of their IT Operation journey. 83% of businesses say AI is a strategic priority for their businesses today, as per a study by the Boston Consulting Group and MIT Sloan Management Review. Additionally, 63% of businesses say pressure to reduce costs will require them to use AI. While humans currently hold significant responsibility for critical operations at present, an AI-enabled future is possible with machines playing a more critical role and humans supporting them. Humans will be empowered to use a system at scale, leaving the autonomous system to handle routine IT operations.

In the context of this article, artificial intelligence can be defined as the use of Big Data analytics, Machine Learning and other artificial intelligence technologies to automate daily IT operations. Such autonomous system will require us to create safety nets in case of incidents and help to monitor, correlate and gain deep insights into data/ problem that the system has been tuned (machine-learned) over the period of time, helping to identify and resolve/prevent the issues that come up.

Machine Learning in IT Operations

Machine Learning is a subset of Artificial Intelligence, includes various analytics and algorithms to automate, based on sample data to make predictions or decisions without being explicitly programmed to perform various tasks in IT Operations, including event correlation to arrive at Root cause analysis, tickets, alerts, and Change execution analysis, planned change versus actual change validation and correlating with received logs, alerts, Present & past events and History from multiple sources within IT systems & Tools.

The concerted use in IT operations is still in the nascent stages and yet to mature a lot. However, many large enterprises or startups are taking steps towards this journey. Gartner predicts that large enterprise exclusive use of AIOPS and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023. It might be years in making end-to-end Automation, predict and take the corrective automated action as part of day-to-day IT operations and the methods will vary for each organization or industry.

AI and ML are only as good as the right data made available on that platform. Hence, one of the biggest challenges for enterprise is the data management, including what type of data to be collected, where to be collected, real-time or batch processing, where to be stored, how to establish basic relationships between collected data sources, how an engineer feed the right information at the initial stage to tune the system as part of machine learning exercise, etc. As we are dealing with various levels of unstructured data, the correlation is not that obvious. This is a perfect task for a Data Scientist / Data Engineering Team to create various rules between different data sources, determine how to correlate/group them and when it makes sense to do so. This requires enterprises put forth great effort into enterprise Data governance, maintaining and managing the complete platform, the huge amount of performance and data they produce and its overall management of the system.

Next comes choosing the right Machine Learning (ML) algorithms as part of the automation platform creation. These algorithms serve as the baseline for the ML behavior to achieve the desired business goals and to meet Objectives in an automated way. Once the Machine learning algorithms tuned based on sample data over the period of time,   it knows how to deliver results, we can come out what needs to be automated, i.e. the machine learns itself and performs as designed. ML makes use of all available data sources, aggregating and organizing output data. Each data set can be collected, formatted and cleaned for relevant information with noise and unnecessary data reduced to find trends, patterns and problems.

With ML, IT operations are more proactive than reactive, automatically anticipating, identifying and resolving issues in real-time which a human might not have detected from the multiple systems, dashboards and metrics.

AI & ML Capabilities

A more proactive approach helps to detect issues at an early stage and makes root cause analysis faster and easier. Even if the data set is vast, AI can get a speedy overview to detect the relation between events and issues which will allow for faster troubleshooting. This is especially useful in ensuring security as AI will monitor and detect unusual processes or activities and prioritize and address the possible malware. Not only will the algorithms flag unusual activity faster, but it will also help to detect system capacity issues, predict system failures, etc. When properly implemented, AI frees up the time and attention of IT operation staffs from focusing on routine tasks /processes and allowing them to focus on more complex tasks.

AI and ML can automate the management of IT infrastructure by scaling forecasted demand and anticipating requirements based on historical data for storage, memory and processing power. By mapping the workload, the AI is able to recommend the right configuration and improve agility, productivity and efficiency. An additional benefit is insights into the IT environment while streamlining communication between teams and business units.

Conclusion

As the world continues to evolve in Digital transformation, operation skills will continue to be needed but the team sizes will reduce with scale growing larger. Companies are adopting these techniques and technologies to stay competitive, cost-effective and efficient. Management of large distributed systems with smaller talent will make a big impact on the organization to be much more efficient. The organization can optimize its platforms with the right workload sizes and as little user intervention as possible. Instead of having to manage a crisis, humans can play a supervisory role and leave the AI to determine the course of action required based on the supporting data and metrics. With many such products in the industry, ever more innovation is taking place to integrate Artificial Intelligence – Machine learning platforms with the existing IT Operations tools, the whole IT industry is getting transformed towards an autonomous system in order to provide seamless IT operation.