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Machine Learning’s Impact on Cloud Computing

When coupled with the power of cloud computing, machine learning could be even more beneficial. This amalgamation is termed ‘the intelligent cloud.’

The current usage of cloud involves computing, storage, and networking. But with the feature of machine learning infused in the cloud, the capabilities of the cloud will increase vastly. The intelligent cloud becomes capable of learning from the vast amount of data stored in the cloud, to build up predictions and analyze situations. This will serve as an intelligent platform to perform tasks much efficiently.

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Impact of machine learning on cloud computing

Cloud computing provides two basic prerequisites for running an AI system efficiently and cost-effectively – scalable and low-cost resources (computing and storage mainly) and processing power to crunch huge amounts of data. First, it provides scalable, low-cost computing and secondly, it is a great way to store and process large volumes of data. Therefore, the amalgamation of cloud with machine learning benefits both these disciplines. The impact of machine learning on the cloud is greatest in the following aspects:

 

Cognitive computing

Machine learning in the cloud does exactly this. The large amounts of data stored in the cloud provide a source of information for the machine learning process. With millions of people using the cloud for computing, storage and networking, the already existing data, the millions of processes that happen every day, all provide a source of information for the machine to learn from. The whole process will provide applications in the cloud with sensory capabilities. The applications will be able to perform cognitive functions and make decisions.

Some examples of cognitive computing in the current market have made remarkable progress in the field of artificial intelligence. IBM Watson, AWS IA, and Microsoft Cognitive APIs have been notable cases in the industry.

Cognitive computing systems in existence today are more at an experimental stage and are given tasks of minimal importance. Over time, we can expect these systems to take over healthcare and hospitality, business and personal lives even.

 

Personal assistance and chatbots

Personal assistants have made life easier for individuals. Products like Apple Siri, Google Allo, or Microsoft Cortana are pre-coded voice recognition systems that give a feel of human touch to machines. But these personal digital assistants have limited capabilities.

With the mass data on the cloud, the learning capabilities of machine learning, and its cognitive computing feature as mentioned above, personal assistance can almost replace any form of human interaction. Fantasies of owning computer systems like those in science fiction or super-hero movies can become a reality.

Implementing machine learning will increase the cognitive capabilities of these chatbots, giving them a human touch. These chatbots can learn from past conversations and provide better assistance. Not just that, instead of a plain, question-answer session between the customer and the chatbot, a real conversation can take place with the chatbot. The chatbot can initiate queries about previous problems or additional suggestions for the problem at hand. The main aim is to make these chatbots as human and personal as possible to make customers feel important.

Increased demand for cloud

Standing alone, the cloud is on its way to becoming an essential computing commodity in many fields. But the integration of machine learning will increase the need for intelligent clouds in the market. With all the capabilities provided by the intelligent cloud, it is definitely the most disruptive technological change in the market. With ever-increasing competition, the intelligent cloud will become a core necessity in managing big companies and help them stay on top of the competition.

The need for an intelligent cloud in fields like healthcare cannot be over-estimated. It would not act as a replacement for doctors or their procedures. Rather, it can act as a virtual assistant to decide the right methods to be used in the treatment of the patients. The machine can gather years of information on a particular case, make comparisons and recommend new approaches to treatment to make the process easier on the doctors.

Fields like banking, investments, education, etc. could also make use of the intelligent cloud capabilities and make human lives simpler and more efficient.

 

Business intelligence

Business Intelligence can become smarter with the introduction of machine learning. Figuring out real-time anomalies, identifying and rectifying faults as they are happening and predicting future are some ways that machine learning could help.

The need for proactive analytics and real-time dashboards is currently high. The demand for advanced, predictive analytics that processes previously collected data, makes real-time suggestions or even future predictions is the kind of business intelligence systems the market needs. The integration of Machine learning into cloud computing will help business intelligence systems get better at what they do.

Businesses need their BI to be proactive and not just crunch up numbers. Predictions from current trends and suggestions for actions should be generated by the BI to make things easier on leaders. Machine learning helps Business intelligence reach that goal.

 

IoT

The opportunities for IoT are endless. From self-driven cars to smart homes to real-time accident predictions, IoT is working towards connecting everything in one web. As the connections and interconnectivity grow, a massive amount of data will be produced. Stored in the cloud, IoT will work better with machine learning.

The Internet of Things will only get better. The system, through machine learning, will be able to identify and rectify problems with systems even before the users will. Warnings about any malfunctioning device can be given out before the defective pieces affect the entire system.

The need for such technology is fast growing and the developments made in the IoT, machine learning, and cloud computing are looking positive in this light.

 

AI as a service

AI is being provided as a Platform (AIaaS) by cloud providers via open source platforms. This provides users with a chunk of AI tools for necessary functions. AIaaS is said to have potential to be a delivery model which provides fast and cost-effective AI solutions rather than consulting many AI experts to complete a task.

AI as a platform service makes the process of intelligent automation easier on users who do not want to be involved in the complexities of the process. This will further increase the capabilities of cloud computing, in return increasing demand for the cloud.

A future of a symbiotic relationship

An intelligent cloud is a future. The interdependency of cloud computing and artificial intelligence (and humans!) will be the essence of any systems or applications developed in the future.

The risk of computers taking over and the fear of robot apocalypse will keep the human involved and in charge of the machines, hence hindering full automation of machines. After the Facebook AI incident (where two AI systems started communicating with each other in a language unknown to the programmers), the need for control over the interaction between the machines has increased.

But the interdependency will exist as long as humans need technology to make their lives easier. The cloud can help provide AI with the information which they need to learn, while the AI can provide more information, automate and make the cloud better – an intelligent one.

 

Conclusion

With great strides happening in the development of both machine learning and the cloud, their future seems increasingly tied together. Cloud computing becomes much easier to handle, scale and protect with machine learning. Not just that, the wider the business initiatives get on the cloud, the more the cloud will need machine learning to be integrated, to make it more efficient. There will be a point in time where no cloud will exist without machine learning.

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Build a High-Performance Computing Infrastructure the right way

High-Performance Computing (HPC) brings a powerful set of tools to a broad range of industries, helping to drive innovation and boost revenue in finance, genomics, oil and gas extraction, and other fields. For many smaller organizations, on-premises HPC infrastructure is too expensive to procure and maintain. They have been forced to make do with renting time on others’ supercomputers, outsourcing design and engineering tasks, or running their applications on whatever computing hardware they can afford. Even within larger organizations that can afford to host their own HPC infrastructure, engineers and researchers must compete for scarce computing resources. But cloud-based HPC solutions are putting vast computational capabilities within reach of more and more organizations—and offering greater flexibility as well.

Using cloud-based HPC lets organizations get started quickly and start to realize benefits almost immediately. Many see faster innovation thanks to shorter turnaround times and improved flexibility, and collaboration is greatly enhanced between teams that might not be able to work together otherwise due to geographical or other logistical considerations. Cost optimization is also a key factor when considering cloud-based HPC—it is much simpler to predict and manage budget and resource use in the cloud.

Many startups and independent researchers who had not even considered buying and setting up their own HPC infrastructure because of perceived up-front costs are finding that it’s now easier than ever—and much less expensive—to dive into cloud-based HPC. The ability to configure massive parallel computing clusters on-demand in the cloud changes the rules—any team with a need for compute resources to solve a problem can start working on it in hours or days. As more organizations adopt cloud-based HPC, more applications, ISVs, and systems integrators are creating better and better solutions for a wider range of users.

BARRIERS TO ENTRY ARE ERODING QUICKLY

Many organizations, especially smaller ones, are held back by outdated beliefs regarding the cost and effort required to get started with HPC applications. Most of these are true enough for large on-premises HPC setups but are no longer true for nimble cloud-based HPC solutions. As cloud-based HPC solutions have matured rapidly, they have become much easier to start using. Even small teams with limited resources are finding that they can test whether HPC can help them innovate faster, or get products to market faster, without taking huge risks with their budgets.

TRANSITIONING AND ONBOARDING

Until recently, organizations that switched from on-premises HPC to cloud-based solutions had to deal with transition issues like license management, or the need to revisit their systems to manage the use of elastic compute resources.

As cloud-based HPC has matured, support ecosystems have developed around it to help make the transition simpler and less expensive. Of course, smaller organizations new to HPC won’t have to deal with these issues and can take advantage of the support structures to jump-start their HPC efforts with cloud-born or cloud-native HPC applications.

Today, there are many options to help ease organizations through onboarding to first-time HPC use or transition from traditional on-premises HPC to the cloud. While internal change management is still up to the organization, most of the heavy lifting involved in getting started can be handled by the cloud provider or a third-party system integrator Transitioning from on-premises HPC solutions is relatively simple now. Many on-premises applications are adding cloud-friendly licensing models, and new cloud-oriented ISVs are developing cloud-first applications to challenge industry leaders. In most cases, the cost of transitioning and decommissioning old hardware is more than offset by

gains in productivity, innovation, and accelerated time-to-market.

And it’s easier than ever to skip past traditional solutions and get started directly in the cloud. Small organizations can explore options with minimal investment and can get assistance from third-party vendors like Ronin, who develop portals that help small teams start doing their research without having to dive into the details of setting up HPC clusters. 

NEEDS ANALYSIS

Understanding needs is one of the oldest business problems, but it’s also fairly straightforward once the initial trial-and-error period has led to solid results. Any organization considering HPC applications as part of their research or engineering programs needs to ask two big questions:

  1. What are our infrastructure requirements? For on-premises HPC, infrastructure size is often dictated by budget, but the pricing and flexibility of cloud-based HPC mean that a precise awareness of specific needs will be rewarded with reduced costs and less downtime for researchers.
  2. How much capacity will we need over time? Correctly predicting need is a major driver of ROI in HPC, whether on-premises or in the cloud. Big capital expenditure items, like on-premises HPC infrastructure, have a 3-5-year procurement cycle. Organizations of all sizes usually struggle with predicting the capacity needed for the next 3-5 years. Buying based on an inflated expectation of growth leads to expensive, unutilized capacity. Pessimistic forecasts lead to oversubscribed resources and lower productivity. This can be especially challenging for smaller organizations or those new to HPC. Cloud-based HPC eliminates the need for long term forecasting, thanks to near-instant access to any required capacity and the latest technologies.

HPC Solutions for Tyrone Systems

Tyrone has a long history in the HPC market and is the fastest-growing major HPC solutions provider, providing solutions to some of the largest end-users in ASEAN. This experience has placed us in a strong position to help end-users accomplish their workloads in an efficient environment. We offer exceptional price-performance, security, and control for today’s most demanding high-performance computing (HPC) workloads.

Explore the unlimited possibilities of HPC. Talk to our expert today at [email protected]

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3 Considerations To Keep In Mind When Building A Private Cloud

With a private cloud, the key categories of consideration are infrastructure, security, procedures, management and administration, and cost.

The specific cloud technology you choose is also a key consideration, however when building your private cloud the decision isn’t as simple as choosing the fastest hardware or the vendor with the most advanced technology; the more important thing is which vendor can work with and adapt to your existing business needs, direction and policies.

 

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How Tyrone Cloud Suite can Overcome Data Center Management Challenges

The emergence of digital transformation has made organizations realize the importance of data in making strategies, analyzing market trends, creating better experiences for customers, and finding out ways to stay ahead from their competitors. Data is becoming the key to competitive advantage.

However, the data that is being generated after technological advancements is way more different and evolved from the earlier one in terms of transactions, structure, availability, methods of collection, and value derived from the ability to aggregate and analyze it. The data is extensive and can easily dominate any aspect of business decision making. To make it usable in a cohesive environment, it can be divided into two categories – big data and fast data.

Big data is large collected data sets used for batch analytics, whereas, fast data is collected from many sources and is used to drive immediate decision making. Despite having advanced ways of storing and using this data, there are some challenges that data centers face and needs to be addressed for the effective utilization of data. Let’s check them out.

Monitoring and Reporting in Real-time

The data centers have a lot of applications, connecting cables, network connectivity, cooling systems, power distribution, storage units, and much more things running at the same time. This heavy load could lead to unexpected failures. Therefore, constant monitoring and reporting different metrics becomes a must-do for data center operators and managers.

Planning and Management in Terms of Capacity

Data center managers tend to overprovision to avoid downtimes. As a result, resources, space, power and energy is wasted. The increase in data has always questioned the capacity of a data center making it a challenge for data center managers. However, this was only till data center infrastructure management solution came into being.

Performance Maintenance and Uptime

One of the major concerns for data center managers and operators is to measure the performance and ensure uptime. This includes maintaining power and cooling accuracy ensuring energy efficiency of the overall structure too. In such cases, any manual management is simply cost-prohibitive at the scale that they operate at.

Staff Productivity and Management

The activities of data center infrastructure involves tracking, analyzing and reporting performances which if done by non-automated or manual systems need facilities and IT staff to spend an extraordinary amount of time logging activities into spreadsheets. This can become a hindrance in the time that can be spent making strategic decisions for improving data center services.

Energy Efficiency and Cost Cutting

The data center is estimated to account for 1.4% of the global electricity consumption.
The industry is often accused of using massive amount of energy and rising temperature problem. There are times when energy is found to be wasted than used at a data center site. This is due to lack of proper energy monitoring tools and environmental sensors.

How to overcome these challenges?

Tyrone Cloud suite has been designed to meet the modern day challenges and unique needs of a data centre which it does by the following ways:

● It comes with a tailored-to-build approach. Sustainability, customer’s IT information flow and architecture is followed while designing a cloud machine.

● It allows the implementation and deployment of your cloud machines on a variety of technologies.

● The suite machines build the cloud architecture from the ground up with absolute ease.

● It can hand-hold any architectural changes with existing infrastructure that might require modifications, OS and technology upgrades, and deeper design revisions.

● It is flexible and can be tightly turned to meet unique needs.

 

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Introduction to HPC & Supercomputing in AI

Catch up with our live webinar on HPC & Supercomputing in AI! Learn about how it works and how it applies to you. We have provided all the information in our video recording you would not miss out on.
Watch the HPC & Supercomputing in AI webinar here!

You can view slides here! 

We will be launching more webinars soon so check out our social media pages for updates!

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Age of Language Models in NLP

For all that we’re unable to attend or would like to recap our live webinar Age of Language Models in NLP, all the resources you need are available to you!

We will be launching more webinars soon so check out our social media pages for updates!

Learn about how the Age of Language Models in NLP can be used and how it applies to you in the real world.

You can learn about Word embeddings, Sequence Modelling, Advanced Language Models, and NLP Attention Mechanism. All the resource is available for you to grow your knowledge and skills about Natural Language Processing webinar!

Watch the Language Models webinar here!

View this presentation:

Links to code:

https://huggingface.co/transformers/

https://nlp.stanford.edu/projects/glove/

https://www.tensorflow.org/tutorials/text/word_embeddings

https://github.com/stanfordnlp/GloVe

Download Resources

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An Introduction to Natural Language Processing

For all that we’re unable to attend or would like to recap our live webinar Natural Language Processing in AI, all the resources you need is available to you!

We will be launching more webinars in soon so check out our social media pages for updates!

Learn about how Natural Language Processing in AI can be used and how it applies to you in the real world.

You can learn about NLP concepts, Pre-processing steps, Vectorization Methods, Generative and Unsupervised methods. All the resource is available for you to grow your knowledge and skills about Natural Language Processing webinar!

Watch Natural Language Processing webinar here!

Watch the presentation here!

Links to codes:
https://www.dropbox.com/s/14lputzcjzi0r7g/codes-%20NLP.zip?dl=0

https://www.tensorflow.org/tutorials/text/word_embeddings

https://radimrehurek.com/gensim/auto_examples/core/run_corpora_and_vector_spaces.html#sphx-glr-auto-examples-core-run-corpora-and-vector-spaces-py

https://radimrehurek.com/gensim/auto_examples/core/run_topics_and_transformations.html#sphx-glr-auto-examples-core-run-topics-and-transformations-py

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Explore Deep Learning Architecture using Tensorflow 2.0 now! Part 2

For all that we’re unable to attend or would like to recap our live webinar Deep Learning for Tensorflow Series part 2, we have all the information for you so would not miss out!

We will be launching more webinars in soon so check out our social media pages for updates!

Learn about how Tensorflow 2.0 can be used in your deep learning architecture through our hands-on Tensorflow workshop.

You can learn about and how to use ConvNet(CNN), Sequence, Generative Models and Distribution Strategy. All the resource is available for you to grow your knowledge and skills about Tensorflow 2.0’s architecture!

Watch the Tensorflow for Deep Learning webinar here!

You can view slides here!

Links shown in demo session:

https://www.tensorflow.org/tutorials/images/cnn

https://www.tensorflow.org/tutorials/keras/text_classification_with_hub

https://www.tensorflow.org/tutorials/generative/dcgan

https://netwebtechnologiesindia-my.sharepoint.com/:u:/g/personal/samantha_netwebtech_com/EWyw6bRmsjRKr5aaQo4sX74BBmptRvCtEXbBB4s95d0MlA?e=mTfqQJ

by admin -

Learn about Tensorflow for Deep Learning now! Part 1

For all that we’re unable to attend our live webinar Tensorflow for Deep Learning Series part 1, we have all the information for you so you would not miss out!

We will be launching Tensorflow live webinar part 2 soon so check out our social media pages for updates!

In this comprehensive workshop, learn how to use TensorFlow, how to build data pipelines and implement a simple deep learning model using Tensorflow Keras. Enhance your knowledge and skills by having a better understanding of Tensorflow with all the resources we have available for you!

Watch the Tensorflow for Deep Learning webinar here!

You can view slides here!

Links shown in demo session:

https://www.tensorflow.org/guide/data

https://www.tensorflow.org/tutorials/keras/classification

https://www.tensorflow.org/hub

by admin -

How Tyrone Cloud Suite can Overcome Data Center Management Challenges

The emergence of digital transformation has made organizations realize the importance of data in making strategies, analyzing market trends, creating better experiences for customers, and finding out ways to stay ahead from their competitors. Data is becoming the key to competitive advantage.

However, the data that is being generated after technological advancements is way more different and evolved from the earlier one in terms of transactions, structure, availability, methods of collection, and value derived from the ability to aggregate and analyze it. The data is extensive and can easily dominate any aspect of business decision making. To make it usable in a cohesive environment, it can be divided into two categories – big data and fast data.

Big data is large collected data sets used for batch analytics, whereas, fast data is collected from many sources and is used to drive immediate decision making. Despite having advanced ways of storing and using this data, there are some challenges that data centers face and needs to be addressed for the effective utilization of data. Let’s check them out.

Monitoring and Reporting in Real-time

The data centers have a lot of applications, connecting cables, network connectivity, cooling systems, power distribution, storage units, and much more things running at the same time. This heavy load could lead to unexpected failures. Therefore, constant monitoring and reporting different metrics become a must-do for data center operators and managers.

Planning and Management in Terms of Capacity

Datacenter managers tend to overprovision to avoid downtimes. As a result, resources, space, power, and energy is wasted. The increase in data has always questioned the capacity of a data center making it a challenge for data center managers. However, this was only until the data center infrastructure management solution came into being.

Performance Maintenance and Uptime

One of the major concerns for data center managers and operators is to measure the performance and ensure uptime. This includes maintaining power and cooling accuracy ensuring energy efficiency of the overall structure too. In such cases, any manual management is simply cost-prohibitive at the scale that they operate at.

Staff Productivity and Management

The activities of data center infrastructure involve tracking, analyzing and reporting performances which if done by non-automated or manual systems need facilities and IT staff to spend an extraordinary amount of time logging activities into spreadsheets. This can become a hindrance in the time that can be spent making strategic decisions for improving data center services.

Energy Efficiency and Cost Cutting

The data center is estimated to account for 1.4% of the global electricity consumption.
The industry is often accused of using a massive amount of energy and rising temperature problems. There are times when energy is found to be wasted than used at a data center site. This is due to a lack of proper energy monitoring tools and environmental sensors.

How to overcome these challenges?

Tyrone Cloud suite has been designed to meet the modern-day challenges and unique needs of a data center which it does by the following ways:

● It comes with a tailored-to-build approach. Sustainability, customer’s IT information flow, and architecture is followed while designing a cloud machine.

● It allows the implementation and deployment of your cloud machines on a variety of technologies.

● The suite machines build the cloud architecture from the ground up with absolute ease.

● It can hand-hold any architectural changes with the existing infrastructure that might require modifications, OS and technology upgrades, and deeper design revisions.

● It is flexible and can be tightly turned to meet unique needs.