#Data centers

[scipunk]

Scanners (1981)

[veronikazemanovafans]

[datasciencewithmohsin]

Understanding Outliers in Machine Learning and Data Science

In machine learning and data science, an outlier is like a misfit in a dataset. It's a data point that stands out significantly from the rest of the data. Sometimes, these outliers are errors, while other times, they reveal something truly interesting about the data. Either way, handling outliers is a crucial step in the data preprocessing stage. If left unchecked, they can skew your analysis and even mess up your machine learning models.

In this article, we will dive into:

1. What outliers are and why they matter.

2. How to detect and remove outliers using the Interquartile Range (IQR) method.

3. Using the Z-score method for outlier detection and removal.

4. How the Percentile Method and Winsorization techniques can help handle outliers.

This guide will explain each method in simple terms with Python code examples so that even beginners can follow along.

1. What Are Outliers?

An outlier is a data point that lies far outside the range of most other values in your dataset. For example, in a list of incomes, most people might earn between $30,000 and $70,000, but someone earning $5,000,000 would be an outlier.

Why Are Outliers Important?

Outliers can be problematic or insightful:

Problematic Outliers: Errors in data entry, sensor faults, or sampling issues.

Insightful Outliers: They might indicate fraud, unusual trends, or new patterns.

Types of Outliers

1. Univariate Outliers: These are extreme values in a single variable.

Example: A temperature of 300°F in a dataset about room temperatures.

2. Multivariate Outliers: These involve unusual combinations of values in multiple variables.

Example: A person with an unusually high income but a very low age.

3. Contextual Outliers: These depend on the context.

Example: A high temperature in winter might be an outlier, but not in summer.

2. Outlier Detection and Removal Using the IQR Method

The Interquartile Range (IQR) method is one of the simplest ways to detect outliers. It works by identifying the middle 50% of your data and marking anything that falls far outside this range as an outlier.

Steps:

1. Calculate the 25th percentile (Q1) and 75th percentile (Q3) of your data.

2. Compute the IQR:

{IQR} = Q3 - Q1

Q1 - 1.5 \times \text{IQR}

Q3 + 1.5 \times \text{IQR} ] 4. Anything below the lower bound or above the upper bound is an outlier.

Python Example:

import pandas as pd

# Sample dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate Q1, Q3, and IQR

Q1 = df['Values'].quantile(0.25)

Q3 = df['Values'].quantile(0.75)

IQR = Q3 - Q1

# Define the bounds

lower_bound = Q1 - 1.5 * IQR

upper_bound = Q3 + 1.5 * IQR

# Identify and remove outliers

outliers = df[(df['Values'] < lower_bound) | (df['Values'] > upper_bound)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Values'] >= lower_bound) & (df['Values'] <= upper_bound)]

print("Filtered Data:\n", filtered_data)

Key Points:

The IQR method is great for univariate datasets.

It works well when the data isn’t skewed or heavily distributed.

3. Outlier Detection and Removal Using the Z-Score Method

The Z-score method measures how far a data point is from the mean, in terms of standard deviations. If a Z-score is greater than a certain threshold (commonly 3 or -3), it is considered an outlier.

Formula:

Z = \frac{(X - \mu)}{\sigma}

 is the data point,

 is the mean of the dataset,

 is the standard deviation.

Python Example:

import numpy as np

# Sample dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate mean and standard deviation

mean = df['Values'].mean()

std_dev = df['Values'].std()

# Compute Z-scores

df['Z-Score'] = (df['Values'] - mean) / std_dev

# Identify and remove outliers

threshold = 3

outliers = df[(df['Z-Score'] > threshold) | (df['Z-Score'] < -threshold)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Z-Score'] <= threshold) & (df['Z-Score'] >= -threshold)]

print("Filtered Data:\n", filtered_data)

Key Points:

The Z-score method assumes the data follows a normal distribution.

It may not work well with skewed datasets.

4. Outlier Detection Using the Percentile Method and Winsorization

Percentile Method:

In the percentile method, we define a lower percentile (e.g., 1st percentile) and an upper percentile (e.g., 99th percentile). Any value outside this range is treated as an outlier.

Winsorization:

Winsorization is a technique where outliers are not removed but replaced with the nearest acceptable value.

Python Example:

from scipy.stats.mstats import winsorize

import numpy as np

Sample data

data = [12, 14, 18, 22, 25, 28, 32, 95, 100]

Calculate percentiles

lower_percentile = np.percentile(data, 1)

upper_percentile = np.percentile(data, 99)

Identify outliers

outliers = [x for x in data if x < lower_percentile or x > upper_percentile]

print("Outliers:", outliers)

# Apply Winsorization

winsorized_data = winsorize(data, limits=[0.01, 0.01])

print("Winsorized Data:", list(winsorized_data))

Key Points:

Percentile and Winsorization methods are useful for skewed data.

Winsorization is preferred when data integrity must be preserved.

Final Thoughts

Outliers can be tricky, but understanding how to detect and handle them is a key skill in machine learning and data science. Whether you use the IQR method, Z-score, or Wins

orization, always tailor your approach to the specific dataset you’re working with.

By mastering these techniques, you’ll be able to clean your data effectively and improve the accuracy of your models.

[ainewsmonitor]

DeepSeek Affair May End Up Settling This Looming US Crisis

The DeepSeek-R1 model required far less energy than was previously thought possible. It may be good news for the US energy sector. DeepSeek’s latest breakthrough has caused US experts to take a closer look at the actual energy demands of AI models. The Chinese company announced that it used far less electricity and computational power than was previously thought possible. It shows that the…

[pilog-group]

How Dr. Imad Syed Transformed PiLog Group into a Digital Transformation Leader?

The digital age demands leaders who don’t just adapt but drive transformation. One such visionary is Dr. Imad Syed, who recently shared his incredible journey and PiLog Group’s path to success in an exclusive interview on Times Now.

In this inspiring conversation, Dr. Syed reflects on the milestones, challenges, and innovative strategies that have positioned PiLog Group as a global leader in data management and digital transformation.

The Journey of a Visionary:

From humble beginnings to spearheading PiLog’s global expansion, Dr. Syed’s story is a testament to resilience and innovation. His leadership has not only redefined PiLog but has also influenced industries worldwide, especially in domains like data governance, SaaS solutions, and AI-driven analytics.

PiLog’s Success: A Benchmark in Digital Transformation:

Under Dr. Syed’s guidance, PiLog has become synonymous with pioneering Lean Data Governance SaaS solutions. Their focus on data integrity and process automation has helped businesses achieve operational excellence. PiLog’s services are trusted by industries such as oil and gas, manufacturing, energy, utilities & nuclear and many more.

Key Insights from the Interview:

In the interview, Dr. Syed touches upon:

The importance of data governance in digital transformation.

How PiLog’s solutions empower organizations to streamline operations.

His philosophy of continuous learning and innovation.

A Must-Watch for Industry Leaders:

If you’re a business leader or tech enthusiast, this interview is packed with actionable insights that can transform your understanding of digital innovation.

👉 Watch the full interview here:

The Global Impact of PiLog Group:

PiLog’s success story resonates globally, serving clients across Africa, the USA, EU, Gulf countries, and beyond. Their ability to adapt and innovate makes them a case study in leveraging digital transformation for competitive advantage.

Join the Conversation:

What’s your take on the future of data governance and digital transformation? Share your thoughts and experiences in the comments below.

[exeton]

Data Centers in High Demand: The AI Industry’s Unending Quest for More Capacity

The demand for data centers to support the booming AI industry is at an all-time high. Companies are scrambling to build the necessary infrastructure, but they’re running into significant hurdles. From parts shortages to power constraints, the AI industry’s rapid growth is stretching resources thin and driving innovation in data center construction.

The Parts Shortage Crisis

Data center executives report that the lead time to obtain custom cooling systems has quintupled compared to a few years ago. Additionally, backup generators, which used to be delivered in a month, now take up to two years. This delay is a major bottleneck in the expansion of data centers.

The Hunt for Suitable Real Estate

Finding affordable real estate with adequate power and connectivity is a growing challenge. Builders are scouring the globe and employing creative solutions. For instance, new data centers are planned next to a volcano in El Salvador to harness geothermal energy and inside shipping containers in West Texas and Africa for portability and access to remote power sources.

Case Study: Hydra Host’s Struggle

Earlier this year, data-center operator Hydra Host faced a significant hurdle. They needed 15 megawatts of power for a planned facility with 10,000 AI chips. The search for the right location took them from Phoenix to Houston, Kansas City, New York, and North Carolina. Each potential site had its drawbacks — some had power but lacked adequate cooling systems, while others had cooling but no transformers for additional power. New cooling systems would take six to eight months to arrive, while transformers would take up to a year.

Surge in Demand for Computational Power

The demand for computational power has skyrocketed since late 2022, following the success of OpenAI’s ChatGPT. The surge has overwhelmed existing data centers, particularly those equipped with the latest AI chips, like Nvidia’s GPUs. The need for vast numbers of these chips to create complex AI systems has put enormous strain on data center infrastructure.

Rapid Expansion and Rising Costs

The amount of data center space in the U.S. grew by 26% last year, with a record number of facilities under construction. However, this rapid expansion is not enough to keep up with demand. Prices for available space are rising, and vacancy rates are negligible.

Building Data Centers: A Lengthy Process

Jon Lin, the general manager of data-center services at Equinix, explains that constructing a large data facility typically takes one and a half to two years. The planning and supply-chain management involved make it challenging to quickly scale up capacity in response to sudden demand spikes.

Major Investments by Tech Giants

Supply Chain and Labor Challenges

The rush to build data centers has extended the time required to acquire essential components. Transceivers and cables now take months longer to arrive, and there’s a shortage of construction workers skilled in building these specialized facilities. AI chips, particularly Nvidia GPUs, are also in short supply, with lead times extending to several months at the height of demand.

Innovative Solutions to Power Needs

Portable Data Centers and Geothermal Energy

Startups like Armada are building data centers inside shipping containers, which can be deployed near cheap power sources like gas wells in remote Texas or Africa. In El Salvador, AI data centers may soon be powered by geothermal energy from volcanoes, thanks to the country’s efforts to create a more business-friendly environment.

Conclusion: Meeting the Unending Demand

The AI industry’s insatiable demand for data centers shows no signs of slowing down. While the challenges are significant — ranging from parts shortages to power constraints — companies are responding with creativity and innovation. As the industry continues to grow, the quest to build the necessary infrastructure will likely become even more intense and resourceful.

FAQs

1. Why is there such a high demand for data centers in the AI industry?

The rapid growth of AI technologies, which require significant computational power, has driven the demand for data centers.

2. What are the main challenges in building new data centers?

The primary challenges include shortages of critical components, suitable real estate, and sufficient power supply.

3. How long does it take to build a new data center?

It typically takes one and a half to two years to construct a large data facility due to the extensive planning and supply-chain management required.

4. What innovative solutions are companies using to meet power needs for data centers?

Companies are exploring options like modular nuclear reactors, geothermal energy, and portable data centers inside shipping containers.

5. How are tech giants like Amazon, Microsoft, and Google responding to the demand for data centers?

They are investing billions of dollars in new data centers to expand their capacity and meet the growing demand for AI computational power.

Muhammad Hussnain Facebook | Instagram | Twitter | Linkedin | Youtube

[el-ffej]

BTW, Ars Technica published a (IMO) much more informative -- and nuanced -- article re: predicted power usage due to AI.

The article is data-driven, and asks more questions than it answers; but I they're questions we should be considering re: the amount of energy used by data centers (not just AI):

After reading it, I feel like I have a much clearer perspective on the problem. Highly recommend giving it a look.

I don't know, how about switching it off?

[scipunk]

Scanners (1981)

[dan-nanni]

NTP (Network Time Protocol) and PTP (Precision Time Protocol) are network-based clock synchronization protocols, with NTP providing millisecond accuracy and PTP achieving sub-microsecond precision

Here is a quick comparison between the two 😎👆

Find high-res pdf books with all my Linux and networking related infographics at https://study-notes.org

[robpegoraro]

Weekly output: 5G platforms, AI in financial services, AI and supply chains, Kamala Harris on AI, AI infrastructure, Gmail's AI calendar integration, Android 16, AI and information security

[youthchronical]

Solar Energy, Criticized by Trump, Claims Big U.S. Gain in 2024

The U.S. power grid added more capacity from solar energy in 2024 than from any other source in a single year in more than two decades, according to a new industry report released on Tuesday. The data was released a day after the new U.S. energy secretary, Chris Wright, strongly criticized solar and wind energy on two fronts. He said on Monday at the start of CERAWeek by S&P Global, an annual…

[climavenetam]

Data Center Cooling With Thermal Storage System

Data Center Cooling With Thermal Storage System

Data centres maintain several IT equipments that store, organize, process and disseminate significant amounts of data. These data centres require an uninterrupted power supply for the equipment to function accurately. This also means that it is important for data centres to have reliable cooling systems to prevent the temperature from rising which can lead to the failure of critical electronic equipment. So, if and when there is a power outage, stored chilled water in large buffer tanks provides continuous operation of the cooling system whilst the generator backup power supply cuts in.

As data center power and heat density increase, transient losses of electrical utility power potentially have more significant impacts and require special design considerations. Data centres these days use thermal storage tanks or chilled water buffer tanks in their cooling system to ensure that the equipment does not overheat the racks at the time when Grid power goes off and captive power kicks in.

There are several methods for increasing the resilience of Data Center cooling systems to counter power disturbances/switch from EB to Captive power & vice versa. Some data centers requiring very high power use standby generators for chillers. However, these add significantly to data center costs. Also, generators take several seconds to start up, after which it can take a few minutes to restart the chillers.

The other option is having a thermal storage tank and chillers with a quick start option. Thermal storage can extend the ability to cool data center IT equipment in the event of a power failure (till the captive power kicks in), by using thermal reserves to provide temporary cooling during a power outage.

During Power failure, Pump continues to circulate the water through chillers with the supply header motorized valve closed and the Thermal Storage Tank outlet valve kept open. Chilled water will be supplied from Thermal Storage Tank directly to the load/field equipment. The pump speed shall be modulated based on the field demand (from the DPT sensor).

An additional Chiller will be switched ON for charging the Thermal Storage Tank based on water temperature. Once the chiller starts, the supply header valve and the tank return water valve will open. This will allow chilled water from the chiller directly to the load/field equipment and simultaneously, the Thermal Storage Tank will start to get charged.

[sharinglaudatosi]

Can Artificial Intelligence solve the climate crisis?

     Pope Francis:  "We are the beneficiaries of two centuries of enormous waves of change....Technology has remedied countless evils which used to harm and limit human beings.  How can we not feel gratitude and appreciation for this progress, especially in the fields of medicine, engineering, and communications?  How can we not acknowledge the work of many scientists and engineers who have provided alternatives to make development sustainable?"  (LS 102)

   But he warns us to avoid the "technological paradigm" which leads us to believe that, in the words of priest and theologian Romano Guardini, "every increase in power means 'an increase of progress itself', an advance in "security, usefulness, welfare, and vigor..."  But "contemporary man has not been trained to use power well." (LS 105)

   Last year, the pope addressed the G7 conference with these remarks:

   For a deep dig into a recent (Jan. 2025) Church statement on artificial intelligence, you can read the document Antiqua et Nova:

  But to get more specific:  Maybe, just maybe, AI can help us make a transition to a cleaner, more sustainable future.

    Maybe. But consider this aspect--here are just a couple frames from a recent webinar from the Catholic Climate Covenant entitled AI’s Sustainability and Climate Challenges: A Catholic Response to Protect Our Common Home:

This entire webinar (1 hour) is fascinating. The presenter of this section is Dr. Ceire Kealty.  You will get the pros and cons.

 But then again, AI facilitates the creation of fun videos and images. Who could resist?

In the same webinar, Scott Hurd from Catholic Charities USA suggests we might do as follows. (He even has 10 suggestions as to how.)

 Watch the webinar and learn a bit more.  This issue is not going away.

  Blessings on your Ash Wednesday and Lenten journey.

  Sharing Laudato Si' comes to you from the St. Andrew the Apostle Care for Creation Ministry, Brooklyn, New York, affiliated with the Metro New York Catholic Climate Movement.

               Please share!

[visionaryvogues03]

The Role of Robotics in Data Centers: Automating Cloud Infrastructure

The digital economy is expanding at an unprecedented rate, and data centers have become the backbone of modern enterprises. As organizations migrate to cloud-based solutions, the demand for highly efficient, scalable, and secure data center operations continues to rise. Robotics is emerging as a game-changer, transforming cloud infrastructure by automating critical tasks such as hardware maintenance, cooling optimization, and security monitoring. For C-suite executives, startup entrepreneurs, and managers, understanding how robotics is reshaping data centers is crucial to staying ahead in the technology landscape.

The Need for Automation in Data Centers

Data centers handle an immense volume of information, and their operations require high levels of precision, efficiency, and security. Manual management of large-scale cloud infrastructure presents challenges such as:

High operational costs due to labor-intensive monitoring and maintenance.

Increased risk of human error, leading to downtime and inefficiencies.

Growing complexity of cloud environments, making traditional methods inadequate.

Security vulnerabilities, with cyber threats and unauthorized access becoming more sophisticated.

Robotics is addressing these challenges by automating repetitive tasks, reducing reliance on human intervention, and enhancing the overall reliability of data center operations.

Key Applications of Machine Intelligence in Data Centers

1. Automated Hardware Maintenance

Data centers rely on thousands of interconnected servers that require frequent maintenance. Robotics can perform routine tasks such as:

Replacing faulty hard drives and network components.

Conducting automated diagnostics and predictive maintenance.

Physically relocating server racks for optimal efficiency.

Companies like Google and IBM are already integrating robotics to enhance server management, reducing downtime and improving service continuity.

2. Cooling and Energy Efficiency Optimization

Cooling is one of the most resource-intensive aspects of data center management. Smart mechatronics equipped with sensors can:

Monitor temperature fluctuations and adjust cooling systems in real time.

Optimize airflow within server rooms to prevent overheating.

Reduce energy consumption by fine-tuning cooling mechanisms.

By using robotics for intelligent climate control, data centers can significantly cut costs and improve sustainability.

3. Security and Surveillance Automation

With cyber threats on the rise, securing physical data center infrastructure is as crucial as protecting digital assets. Advanced automated systems is being used for:

AI-driven surveillance: Drones and robotic security guards patrol facilities, identifying unauthorized access and potential threats.

Biometric authentication: Robots can verify identities and grant access only to authorized personnel.

Threat detection and response: Autonomous systems can instantly flag and neutralize suspicious activities, reducing security breaches.

4. AI-Powered Data Management

Cloud providers generate and process vast amounts of data daily. AI-powered automated systems can automate data management by:

Identifying and resolving data bottlenecks.

Enhancing data backup and recovery processes.

Ensuring regulatory compliance by monitoring data flow and storage practices.

These advancements are making data centers smarter and more responsive to dynamic cloud computing needs.

The Business Impact of Robotics in Data Centers

1. Cost Reduction and Operational Efficiency

By integrating robotics, data centers can minimize labor costs, reduce energy consumption, and optimize infrastructure utilization. Automation leads to fewer disruptions, ensuring that cloud services remain consistently available and reliable.

2. Scalability for Growing Cloud Demands

As businesses expand their cloud operations, scalability becomes a key factor. Machine intelligence enables data centers to seamlessly scale resources up or down based on demand, ensuring agility and flexibility in cloud infrastructure.

3. Improved Security and Compliance

With stringent regulatory requirements in industries like finance and healthcare, data center security is non-negotiable. AI-driven robotics enhances security measures, ensuring compliance with industry standards and protecting sensitive information.

4. Faster Deployment of Cloud Services

Automation accelerates the deployment of new cloud services, reducing time-to-market for businesses. Organizations leveraging mechatronics can gain a competitive edge by offering faster, more efficient cloud solutions to their customers.

Challenges and Considerations

Despite its benefits, integrating mechatronics into data centers comes with challenges:

High initial investment: The cost of deploying robotics technology can be significant.

Skill gaps: Employees need specialized training to manage and maintain robotic systems.

Cybersecurity risks: Automated systems can become targets for cyberattacks if not properly secured.

Regulatory concerns: Compliance with data privacy laws must be carefully managed.

Businesses must weigh these factors and develop strategic plans to maximize the benefits of intelligent machinery while mitigating potential risks.

Future Outlook: The Evolution of Mechatronics in Cloud Infrastructure

The role of robotics in data centers will continue to expand, with emerging trends such as:

Autonomous AI-driven maintenance, where self-learning robots predict and fix issues without human intervention.

Edge computing integration, enabling faster data processing closer to the source.

Blockchain-based security, enhancing trust and transparency in automated operations.

Human-robot collaboration, where AI-driven assistants support IT teams in managing complex cloud environments.

As technology advances, autonomous systems will become an indispensable component of cloud infrastructure, revolutionizing how data centers operate.

Conclusion

The integration of robotics in data centers is transforming cloud infrastructure by automating maintenance, enhancing security, and optimizing energy efficiency. As businesses increasingly rely on cloud computing, leveraging automation technology is no longer an option but a necessity for scalability, cost efficiency, and innovation.

For tech executives, entrepreneurs, and decision-makers, investing in automated systems presents an opportunity to redefine data center operations and gain a competitive edge in the digital economy. The future of cloud computing is automated, and cybernetics is leading the way toward a smarter, more resilient infrastructure.

Uncover the latest trends and insights with our articles on Visionary Vogues