How Does GPU Technology Help In Machine Learning?

January 27, 2021

Be it a data scientist or any machine learning expert; no one will deny the importance of large datasets for any successful machine learning (or ML) models. As the size of datasets keeps increasing, the performance scaling of ML models tends to hit a peak – before experiencing a performance bottleneck or lag.

A growing technology within the ML fold is Deep Learning, which has both practical and research applications across multiple domains. Based on the use of artificial neural networks (or ANNs), deep learning is widely used to make accurate predictions from large volumes of data. In short, Deep learning requires a lot of computational power – or the best of hardware configuration.

One such hardware component is either a traditional and reliable CPU (or central processing unit) or the advanced GPU (or graphic processing unit) technology. Which of these two hardware technologies performs better – when it comes to machine or deep learning models with heavy datasets? First, let us discuss these technologies in detail.

CPU and GPU, an Introduction

Since the 1970s, CPU units – introduced by Intel – have been the brain of any computer or computing device. CPU units have performed these operations without any speed/performance bottlenecks, be it any logical, computational, or input-output operations. Essentially, CPU technology was designed with a single core – to perform a single operation at any given time.

Gradually, with technological advancement and higher computational demand, we began to see dual (or even multi-) core CPUs designed to perform more than one operation at the same time. Most CPUs today are built using a few cores – and their basic design is still prime for solving a few complex computations—for example, machine learning problems that require interpretation or parsing of complex code logic.

Like CPU, GPU is also used in any computer to process instructions. The significant difference being GPUs can work on multiple instructions at a given time, thanks to parallelization. Most GPUs are designed with many processing cores – with clock speeds that are much lower than those of CPUs. However, with its multiple processing, GPUs can parallelize calculations using many threads – thus increasing the rate of calculations that would take longer using the CPU.

GPUs have smaller sizes – but larger numbers of cores consisting of arithmetic logic units (or ALUs), control units, and in-memory cache. In other words, a GPU is a specialized processing unit with dedicated memory for performing floating-point operations – that were conventionally used in graphics processing. GPUs have also been in existence since the 1970s but were mostly restricted for gaming applications. It only gained mainstream popularity after NVIDIA released its GeForce line of GPU server products.

Initially used for graphical rendering, GPUs gradually advanced to perform advanced geometrical calculations (for example, for polygon transformations or vertical shapes rotation into 3-D coordinate systems). With the release of the CUDA technology in 2006, NVIDIA introduced parallel computing into GPUs, thus accelerating the speed of computing applications. CUDA use in GPU acceleration applications allows the sequential part of the application workload to run on CPU (for single-thread performance). In contrast, the compute-intensive part of the workload is run in parallel on thousands of GPU machine cores.

CPU or GPU – Which is Better for Machine Learning?

As processing units, both CPU and GPU are built for computations and calculations on neural networks. From a computational viewpoint, GPUs are better suited due to their parallel computation capability. ML frameworks like TensorFlow are built to leverage multiple CPU performances, reducing computing time on various threads.

For most data science experts, CPUs are easy to access Windows Cloud or Linux Cloud servers. As an example, E2E Networks offers cloud service for running CPU intensive workloads across industry verticals.

When it is a matter of advanced neural networks, training of deep learning models is the most intensive on hardware resources. During the training phase, neural networks receive inputs, which are processed using hidden layers – with weights – that are continually adjusted to derive a prediction from the data model. For accurate predictions, weights are adjusted to locate patterns in the input data. This type of operation is commonly known as matrix multiplications.

For neural networks handling around 1,000 to even 100,000 parameters, you can use any CPU-based computer to train the data model to handle this data volume in minutes (or at the most, hours). On the other hand, neural networks handling over 10 or 50 billion parameters would take years to train using the CPU approach. This is where GPU processors can have a significant impact – in faster processing and reduced training time.

How do GPUs achieve faster training of deep learning models? Only through parallel computing that runs all the operations simultaneously, instead of one operation after the other. As compared to CPUs, GPUs allocate a higher number of transistors to ALUs and fewer transistors to caching or flow control. As a result, GPUs are better oriented for faster machine learning or data science models – whose speed can be enhanced by parallel computations. Next, let us look at a few crucial parameters in deep learning models where GPUs can make a difference.

When to use GPU or CPU for Deep learning?

Listed below are a few parameters in deep learning that should determine when to use either CPU or GPU:

  • High memory bandwidth

Thanks to its higher memory bandwidth, GPU is a faster technology as compared to CPU. When you are using large datasets to train your ML model, you need high memory bandwidth from your processor. On the other end, CPUs consume more clock cycles to compute complex tasks because of sequential processing. GPUs are built with a dedicated Video RAM (or VRAM) memory for handling complex tasks – thus leaving the CPU memory for less-intensive tasks.

For instance, on average, CPUs can provide a bandwidth of around 60 GB/s, the GeForce 780 GPU processor offers a bandwidth of over 330 GB/s, while the NVIDIA Tesla GPU offers a bandwidth of close to 300 GB/s.

  • Size of the datasets

As mentioned before, data model training is resource-intensive and requires a large dataset. This, in turn, requires high computational power and memory allocation – which shifts the balance towards GPU processing. In short, the larger the dataset and computing power, the more advantageous is GPU as compared to CPU.

  • Optimization

Task optimization is much easier to perform in CPU cores than in GPU cores. Although they are much lesser in number, CPU cores are more potent than their GPU counterparts.

Based on its MIMD architecture, CPU cores can work on different instructions. On the other hand, GPU cores are organized within 32 core blocks – and can execute the same instruction in parallel using its SIMD architecture. Additionally, parallelization in extremely dense neural networks is complex in GPU computing.

  • Cost factor

On average, GPU-based compute instances cost around two to three times that of CPU-based compute instances. The higher cost is only justified if you are looking for 2-3 times more gains in the performance in GPU data models. For other applications, CPUs are always the better alternative thanks to their lower costs.

You do not need to run your ML instances on your hardware or server with cloud hosting services. Instead, you can rent an external server (for example, a virtual private server on Windows or VPS Windows). These services are charged at a low hourly rate (as low as INR 3.5). All you need to remember is to delete your cloud instance once you have completed the job.

Among India’s largest cloud providers, E2E Networks offers various cloud-based configurations – with hourly rates starting from INR 2.80 per hour.

Now that you are well-versed with the various parameters let us look at some real-life scenarios that you should know before going for the right GPU technology.

ScenarioGPU technologyWhen you need to work mainly on machine learning algorithmsTasks that are small or require complex sequential processing can be handled by CPU – and do not necessitate the use of GPU power.When you are working on data-intensive tasksThis can be implemented on any laptop with a low-end GPU processor. Example, a 2GB NVIDIA GT 740M or a medium-level NVIDIA GTX 1080 with 6GB VRAM.When you are working on complex ML problems that leverage deep learningBuild your own customized deep learning solution – or use a Windows cloud server from E2E cloudWhen you are managing larger and complex tasks with more scalabilityYou can opt for a GPU cluster or multi-GPU computing that are costly to implement. Alternatively, you can save costs by opting for a GPU cloud server (example, the E2E GPU Cloud from E2E Networks).

Next, let us look at some of the best GPU for machine learning applications.

Best GPUs for Deep learning

Be it any project, selecting the right GPU for machine learning is essential to support your data project in the long run. NVIDIA GPUs are among the best in the market for machine learning or integrating with other frameworks like TensorFlow or PyTorch.

Here are some of the best NVIDIA GPUs that can improve the overall performance of your data project:

  • NVIDIA A100 80 GB GPU RAM

The NVIDIA A100 GPU with 80 GB GPU RAM series is best recommended for large-scale AI and ML projects and data centres. Designed for GPU acceleration and tensor operations, the NVIDIA A100 is one GPU in this series that can be used for deep learning and high-performance computing.

Another popular offering from the GPU series is the NVIDIA A30, typically used for data analytics and scientific computing.

  • NVIDIA DGX

This is the top-of-the-level GPU series used for enterprise-level machine learning projects. Optimized for AI and multiple node scalability, the DGX series offers complete integration with deep learning libraries and NVIDIA solutions. If you are looking for an Ubuntu-hosted GPU, then the DGX-1 is the best choice as it is integrated with Red Hat solutions.

Conclusion When it is a matter of running high-level machine learning jobs, GPU technology is the best bet for optimum performance. With cloud applications designed for high memory tasks or running on the Windows Cloud platform, E2E Networks offers the best and most cost-effective GPU solutions that cater to different customer requirements.

Reach here to know more about GPU: https://bit.ly/3mFerJn

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.

 

The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.

 

In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.

 

Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:

Wiom

Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 

TechVantage

As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online

Manthan

As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 

NetraDyne

Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.

 

Helpshift

Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 

Facilio

An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.

 

In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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