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PyTorch vs TensorFlow: Which is Better in 2024?

Updated on 24 October, 2024

27.58K+ views
17 min read

As technology is evolving rapidly today, both Predictive Analytics and Machine Learning are imbibed in most business operations and have proved to be quite integral. Deep learning is a machine learning type based on ANN. For many applications, shallow machine learning models and traditional data analysis approaches fail to reach the performance of deep learning models. 

Deep learning (DL) frameworks offer the building blocks for designing, training, and validating deep neural networks through a high-level programming interface. These frameworks provide superior performance and better management of dependencies. 

Today, let's discuss the key differences between PyTorch vs TensorFlow. We have numerous frameworks at our disposal that allow us to develop compact and robust tools that can offer a better abstraction and simplify difficult programming challenges. 

PyTorch vs TensorFlow [Head-to-Head Comparison] 

Is PyTorch better than TensorFlow? Let us see the point of differences between the two.  

Parameters TensorFlow PyTorch
1. Programming Language Written in Python, C++ and CUDA Written in Python, C++, CUDA and is based on Torch (written in Lua)
2. Developers Google Facebook (now Meta AI)
3. Graphs Earlier TensorFlow 1.0 was based on the static graph. TensorFlow 2.0 with Keras integrated also supports dynamic graphs using eager execution Dynamic
4. API Level High and Low Low
5. Installation Complex GPU installation Simple GPU installation
6. Debugging Difficult to conduct debugging and requires the TensorFlow debugger tool Easy to debug as it uses dynamic computational process.
7. Architecture TensorFlow is difficult to use/implement but with Keras, it becomes bit easier. Complex and difficult to read and understand.
8. Learning Curve Steep and bit difficult to learn Easy to learn.
9. Distributed Training To allow distributed training, you must code manually and optimize every operation run on a specific device. By relying on native support for asynchronous execution through Python it gains optimal performance in the area of data parallelism  
10. APIs for Deployment/Serving Framework TensorFlow serving. TorchServe
11. Key Differentiator Easy-to-develop models Highly “Pythonic” and focuses on usability with careful performance considerations.
12. Eco System Widely used at the production level in Industry PyTorch is more popular in the research community.  
13. Tools TensorFlow Serving, TensorFlow Extended, TF Lite, TensorFlow.js, TensorFlow Cloud, Model Garden, MediaPipe and Coral TorchVision, TorchText, TorchAudio, PyTorch-XLA, PyTorch Hub, SpeechBrain, TorchX, TorchElastic and PyTorch Lightning
14. Application/Utilization Large-scale deployment Research-oriented and rapid prototype development
15. Popularity This library has garnered a lot of popularity among Deep Learning practitioners, developer community and is one of the widely used libraries It has been gaining popularity in recent years and interest in PyTorch is growing rapidly.  It has become the go-to tool for deep learning projects that rely on optimizing custom expressions, whether it’s academia projects or industries.
16. Projects DeepSpeech, Magenta, StellarGraph CycleGAN, FastAI, Netron

Sharpen your skills with these online Data Science courses and learn to tackle complex Data Science problems.  

What is PyTorch? 

From the definition as per the official website, PyTorch is an open-source machine learning framework that accelerates the path from research prototyping to production deployment. It is a development tool that removes cognitive overhead involved in building, training and deploying neural networks.  

The PyTorch framework runs on Python and is based on the Torch library (Lua-based deep learning framework). Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan authored PyTorch, and Meta AI primarily develops it. Given the PyTorch framework’s architectural style, one can tell the entire deep modeling process is far more transparent and straightforward when compared with Torch. 

What is TensorFlow? 

As per the definition from the official website, TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. TensorFlow is by far one of the most popular deep learning frameworks. It is developed by Google Brain and supports languages like Python, C++ and R.  

TensorFlow uses dataflow graphs to process data. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. As you build these neural networks, you can look at how the data flows through the neural network.   

Difference Between TensorFlow and PyTorch: Detailed Comparison 

TensorFlow and PyTorch are inarguably the two most popular Deep Learning frameworks today. Though both are open-source libraries, it might not be easy to figure out the difference between PyTorch and TensorFlow. Both frameworks are extensively used by data scientists, ML engineers, researchers and developers in commercial code and academic research.    

Both frameworks work on the fundamental data type called a tensor. A tensor is a multidimensional array, as shown in the below picture. 

Source: tensorflow.org 

There has always been a contentious debate over which framework is superior, with each camp having its share of ardent supporters. The debate landscape is ever evolving as PyTorch and TensorFlow have developed quickly over their relatively short lifetimes. It is important to note that since incomplete or outdated information is abundant, the conversation about which framework reigns premier is much more nuanced as of 2024 - let’s explore these differences in detail.  

Just to show you a broad picture of growth in usage and demand of TensorFlow and PyTorch deep learning frameworks, Google's worldwide trend graph for the search keywords TensorFlow vs. PyTorch across the last 5 years is as below:

 

Google search trends

1. PyTorch vs TensorFlow: Performance Comparison 

Even though both PyTorch and TensorFlow provide similar fast performance when it comes to speed, both frameworks have advantages and disadvantages in specific scenarios.   

The performance of Python is faster for PyTorch. Despite that, due to TensorFlow’s greater support for symbolic manipulation that allows users to perform higher-level operations, programming models can be less flexible in PyTorch as compared to TensorFlow. 

In general, for most cases, because of its ability to take advantage of any GPU(s) connected to your system, TensorFlow should ideally provide better performance than PyTorch. Training deep learning models using Autograd that require significantly less memory is one of the exceptions where PyTorch performs better than TensorFlow in terms of training times.   

The following benchmark shows that TensorFlow exhibits better training performance on CNN models, while PyTorch is better on BERT and RNN models (except for GNMT). Looking at the difference % column, it is noticeable that the performance between TensorFlow and PyTorch is very close. 

2. PyTorch vs TensorFlow: Training Time and Memory Usage 

For PyTorch and TensorFlow, time taken for training and memory usage vary based on the dataset used for training, device type and neural network architecture.    

We can observe from the diagram below that the training time for PyTorch is significantly higher than TensorFlow on the CPU.

Source

From the below diagram, we can see that for CNN architecture training time for PyTorch is significantly higher than TensorFlow on GPU. But, for LSTM architecture, except for “Many things” dataset, training time for PyTorch is significantly lower than TensorFlow on GPU.

As we can see from the following diagram, memory consumption is slightly higher for PyTorch on CPU compared to that of TensorFlow. 

And as we can see from the following diagram, memory consumption is significantly higher for TensorFlow on GPU compared to that of PyTorch.
 

 3. PyTorch vs TensorFlow: Accuracy 

For a good number of models, the best possible accuracy attained during training can be the same for PyTorch and TensorFlow for a given model. But hyperparameters used could be different between these frameworks including parameters such as number of epochs, training time, etc. From the below diagram, we can see that the validation accuracy of the models in both frameworks averaged about 78% after 20 epochs. 

 

In Spite of all sorts of hyperparameter tuning, the best possible accuracy achieved could differ between PyTorch and TensorFlow, and one might beat another one in accuracy - for a given dataset (CIFAR, MNIST, etc.), device (CPU, GPU, TPU etc.), type of neural network (CNN, RNN, LSTM, etc.), type of CNN (Faster R-CNN, Efficientnet, etc.). These differences arise due to various reasons including optimization methods, backend libraries used, computation methods used, etc.

From the below diagram, we can see that for MNIST, both TensorFlow and PyTorch achieve an accuracy of ~98%. While for CIFAR-10, TensorFlow achieved an accuracy of ~80%, but PyTorch could get ~72% only. For CIFAR-100, PyTorch archives ~48% but TensorFlow could score ~42% only, whereas Keras gets ~54%. 

For the below diagram, we can observe that PyTorch experiences a significant performance jump after the 30th epochs to reach a peak accuracy of 51.4% at the 48th epochs, while TensorFlow achieves peak accuracy of 63% at the 40th epochs. 

4. PyTorch vs TensorFlow: Debugging 

As PyTorch uses a standard python debugger, the user does not need to learn another debugger. Since PyTorch uses immediate execution (i.e., eager mode), it is said to be easier to use than TensorFlow when it comes to debugging. Hence in the case of PyTorch, you can use Python debugging tools such as PDB, ipdb, and PyCharm debugger.  

For TensorFlow, there are two ways to go about debugging: you must request the variables from the session or learn the TF debugger. Either way, TensorFlow requires you to execute your code before you can debug it explicitly. You must write code for the nodes in your graph to be able to run your program in debug mode. To find the problems related to memory allocation or errors at runtime that require more advanced debugging features such as stack traces and watches, you’ll have to use TF debugger). 

5. PyTorch versus TensorFlow: Mechanism: Graph Definition 

As TensorFlow works on a static graph concept, the user must first define the computation graph and then run the machine learning model. So basically, TensorFlow has its graphs pre-constructed at the beginning of training. Next, the graph must go through compilation, executing computations against these graphs.   

PyTorch gives an edge with its dynamic computational graph construction, which means the graph is constructed as the operations are executed. The main advantage of this approach is that - graphs can be less complex than those in other frameworks since graphs are built on demand (i.e., graphs are built by interpreting the line of code corresponding to that particular aspect of the graph). Since data doesn't need to be passed around to intermediate nodes when it's not required, complexity can be reduced here. 

Advantages and Disadvantages of TensorFlow 

Advantages 

  1. Data Visualization: TensorFlow provides a tool called TensorBoard that helps with the graphical visualization of data. By reducing the effort of looking at the whole code, the tool facilitates easy node debugging and effectively helps with an easy resolution of the neural network. The tool lets you see and observe multiple aspects of the machine learning model, such as the model graph and loss curve. 
  2. Compatibility: TensorFlow is compatible with many programming languages. It provides a stable Python API and APIs without a backward compatibility guarantee for languages such as Javascript, C++, and Java. It provides third-party language binding packages for C#, Haskell, Julia, MATLAB, R, Scala, Rust, OCaml, and Crystal.  
  3. Scalability: The scalability offered by TensorFlow is high as it was built to be production-ready and can easily handle large datasets.  
  4. Architectural Support: The TensorFlow architecture uses an application-specific AI accelerator called TPU (Tensor Processing Unit), which offers faster computation than that of GPUs and CPUs. Deep learning models built on top of TPUs can be easily deployed over clouds, and they work faster than the other two.  
  5. Model Building: Using intuitive high-level APIs such as Keras, the TensorFlow library allows us to build and train machine learning models with quick model iteration and easy debugging.   
  6. Deployment: Since its inception, it has been the go-to framework for deployment-oriented applications. TensorFlow, equipped with the arsenal of associated tools, makes the end-to-end Deep Learning process easy and efficient. For deployment specifically, robust tools such as TensorFlow Serving and TensorFlow Lite allow you to painlessly deploy on clouds, servers, mobile, and IoT devices.  
  7. ML Production: We can train and deploy the models in the cloud, on-premises, in the browser, or on a device, irrespective of the language the user makes use of.  
  8. Open Source: Any user can employ the TensorFlow module whenever and wherever required, as it is free of cost to anyone who wants to work with it or utilize it.  
  9. Integration and EcoSystem: TensorFlow can easily integrate with Google’s services if you use Google Cloud. For example, saving a TF Lite model onto its Firestore account and delivering the model to a mobile application. Another example is the ability to use TFLite for local AI in conjunction with Google’s Coral devices, a must-have for many industries.  

Disadvantages 

  1. Backward Compatibility: The life of researchers is difficult with TensorFlow as there are backward compatibility issues between old research in TensorFlow 1 and new research in TensorFlow 2.  
  2. Training Loops: In TensorFlow, the procedure to create training loops is slightly complex and not very intuitive.  
  3. Frequent Updates: As TensorFlow gets updates very often, it becomes overhead for a user to maintain the project as it involves uninstallation and reinstallation from time to time so that it can bind and be blended with its latest updates.  
  4. Symbolic Loops: TensorFlow lags at providing symbolic loops for indefinite sequences. Its support for definite sequences makes it a useful resource.   
  5. Inconsistency: TensorFlow’s contents include some homonyms as names, making it difficult for users to remember to use them. Since the same name gets used for various purposes, it can get confusing more often.  
  6. Computation Speed: Benchmark tests show that TensorFlow lags in computation speed compared to its competitors. Also, it has less usability in comparison to other frameworks.  

Advantages and Disadvantages of PyTorch 

Advantages 

  1. Pythonic in Nature: Most of the code deployed in PyTorch is pythonic, which means the procedural coding is similar to most of the elements of Python. PyTorch smoothly integrates with the python data science stack. PyTorch functionalities can easily be implemented with other libraries such as Numpy, Scipy, and Cython.  
  2. Ease of Use and Flexibility: PyTorch is very simple and provides easy-to-use APIs. PyTorch is constructed in a way that is intuitive to understand and easy to develop machine learning projects.  
  3. Easier to Learn: PyTorch is relatively easier to learn than other deep learning frameworks, as its syntax is similar to conventional programming languages like Python.  
  4. Dynamic Computation Graph: PyTorch supports Dynamic Graphs. This feature is especially useful for changing the network behavior programmatically at runtime. When you cannot pre-determine the allocation of memory or other details for the particular computation, dynamically created graphs are most useful.  
  5. Documentation: PyTorch’s documentation is very organized and helpful for beginners, and it is kept up to date with the PyTorch releases. PyTorch has one of the best documentations that is helpful to get a hold of a majority of the essential concepts. They have a detailed description where one can understand most of the core topics such as torch.Tensor, torch.autograd, Tensor Attributes, Tensor Views, and so much more. 
  6. Model Availability: Since PyTorch currently dominates the research landscape and the community has widely adopted it, most publications/available models use PyTorch.  
  7. Community Support: PyTorch has a very active community and forums (discuss.PyTorch.org). Apart from the default documentation, the entire community highly supports PyTorch and related projects. Working, sharing, and developing PyTorch projects is easier while working on a research project. 

Disadvantages 

  1. Visualization Techniques: PyTorch does not have as great an option for visualization, and developers can connect externally to TensorBoard or use one of the existing Python data visualization tools.  
  2. Model Serving in Production: For PyTorch serving, even though we have TorchServe, which is easy to use and flexible, it does not have the same compactness as its TensorFlow counterpart. In terms of serving in production, PyTorch has a long way to go before it can compete with the superior deployment tool. While this will change in the future, other frameworks have been more widely used for real production work.  
  3. Not as extensive as TensorFlow: The development of actual applications might involve converting the PyTorch code or model into another framework, as PyTorch is not an end-to-end machine learning development tool. 

Which is Better in 2024: PyTorch or TensorFlow? 

The debate on PyTorch vs. TensorFlow doesn't have a definitive answer. Each framework is superior for specific use cases. Both are state-of-the-art, but they have key distinctions. PyTorch supports dynamic computation graphs and is generally easier to use. TensorFlow is more mature with extensive libraries but may require more learning time.

Decide based on your project needs. For quick learning and ease of use, PyTorch is preferable. For production-ready frameworks supporting heavy calculations, TensorFlow may be ideal.

1. For a Researcher 

PyTorch is the de facto research framework with most SOTA models. It offers features essential for research, like GPU capabilities, an easy API, scalability, and excellent debugging tools. However, in Reinforcement Learning (RL), TensorFlow might be better due to its native agents' library and DeepMind’s Acme.

2. For an Industry Professional 

For deep learning engineering in industry, TensorFlow’s robust deployment framework and end-to-end platform are invaluable, though it requires more learning. If accessing SOTA models in PyTorch, consider using TorchServe. For deploying PyTorch models within TensorFlow workflows, ONNX might be needed. For IoT or embedded systems, use TensorFlow with the TFLite + Coral pipeline. For mobile applications, prefer PyTorch unless you need video or audio input, then use TensorFlow.

3. For a Beginner 

Beginners should start with Keras (part of TensorFlow) or FastAI (for PyTorch) to quickly learn Deep Learning basics. As you advance, choose based on the discussed points. 

Conclusion

As both PyTorch and TensorFlow have their merits, declaring one framework as a clear winner is always a tough choice. Picking TensorFlow or PyTorch will come down to one’s skill and needs. Overall, both frameworks offer great speed and come equipped with strong Python APIs. 

As of 2024, both TensorFlow and PyTorch are very mature and stable frameworks, and there is a significant and visible overlap with their core Deep Learning features. Today, the practical considerations of each framework supersede their technical differences. These considerations include time to deploy, model availability, associated ecosystems, etc.  

Both frameworks have good documentation, active communities, and many learning resources, so you’re not making a mistake choosing either framework. While TensorFlow remains the go-to industry framework, and after its explosive adoption by the research community, PyTorch has become the go-to research framework, there are certainly use cases for each in both domains. 

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Frequently Asked Questions (FAQs)

1. Is PyTorch Faster than TensorFlow?

Both are comparable for small and medium-sized datasets. PyTorch is faster than TensorFlow as it allows quicker prototyping than TensorFlow.

2. What is PyTorch Used for?

PyTorch, an open-source deep learning framework, is used in computer vision and natural language processing tasks.

3. Should I Learn PyTorch or TensorFlow First?

It depends. Learning Keras is a better choice for deep learning beginners due to its high-level API. However, if you already have some basic understanding of deep learning and have worked with Keras before, you can choose either of the two frameworks based on your project requirements. TensorFlow is good at deploying models in production to build AI products, while PyTorch is preferred in academia for research tasks. Thus, both TensorFlow and PyTorch are good frameworks to learn.

4. Is TensorFlow Easier than PyTorch?

With the use of PyTorch, a lot of the complexities can be avoided, which are required for Neural Networks and Deep Learning technologies. You need much more experience to achieve the same functionality in TensorFlow. Many people generally opt for Keras over TensorFlow as an additional layer.

5. Is PyTorch worth Learning?

Yes, learning PyTorch is an excellent decision to improve one's deep learning skills. PyTorch is quite popular in the research community. It is also a part of the Python package ecosystem and hence, fully compatible with other popular Python libraries such as SciPy and NumPy.

Did you find this article helpful?

Devesh Kamboj

I’m passionate about Transforming Data into Actionable Insights through Analytics, with over 5+ years of experience working in Data Analytics, Data Visualization & Database Management. Comprehensive experience in administering analytical solutions with Big Data Architecture and Agile Project Development. Skill Set: SQL (MySQL/SQL Server), Python, Tableau, NoSQL (MongoDB), Advanced Excel, Hadoop, Hive, HDFS, Data Warehousing, Business Analytics/Reporting and Project Management.

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5.81K+

How Big Data and Machine Learning are Uniting Against Cancer

Cancer is not one disease. It is many diseases. Let us understand the cause of cancer by a simple example. If you take a photocopy of a document, due to some issues, other dots or smears appear on it even though they are not present in the original copy. In the same way, in gene replication processes, errors occur inadvertently. Most of the time the genes with errors will not be able to sustain and will ultimately perish. In some rare cases, the mutated gene with mistakes will survive and get further replicated uncontrollably. Uncontrollable replication of mutated genes is the primary cause of cancer. This mutation can happen in any of the twenty thousand genes in our body. Variation in any one or a combination of genes makes cancer a severe disease to conquer. To eradicate cancer, we need methods to destroy the rogue cells without harming the functional cells of the body; which makes it doubly hard to defeat. Cancer and its complexity Cancer is a disease with a long tail distribution. Long tail distribution means there are various reasons for this condition to occur and there is no single solution for eradicating it. There are diseases which affect a large percentage of the population but have a sole cause of occurrence. For example, let us consider Cholera. Eating food or drinking water contaminated by the bacterium Vibrio Cholerae is the cause of cholera. Cholera can occur only because of Vibrio Cholerae, and there is no another reason. Once we find out the only cause of a disease, then it is relatively easy to conquer it. What if a condition occurs because of multiple reasons? A mutation can occur in any of the twenty thousand genes in our body. Not only that, but we also need to consider their combinations. Cancer may not just happen because of a random mutation in a gene but also because of a combination of gene mutations. The number of causes for cancer becomes exponential, and there is no single mechanism to cure it. For example, a mutation of any of these genes ALK, BRAF, DDR2, EGFR, ERBB2, KRAS, MAP2K1, NRAS, PIK3CA, PTEN, RET, and RIT1 can cause lung cancer. There are many ways for cancer to occur and that’s why it is a disease with long tail distribution. In our arsenal for waging this war on cancer and conquering it, big data and machine learning are critical tools. How can big data help in fighting this war? What does machine learning have to do with cancer? How are they going to help in fighting a disease with many causes, a condition with a long tail distribution? Firstly, how and where is this big data generated? Let us find answers to these questions. Gene Sequencing and explosion in data Gene sequencing is one area which is producing humongous amounts of data. Exactly how much data? According to the Washington Post, the human data generated through gene sequencing (approximately 2.5 lakh sequences) takes up about a fourth of the size of YouTube’s yearly data production. If all this data were combined with all the extra information that comes with sequencing genomes and recorded on 4GB DVDs, it would be a stack about half a mile high. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript The methods for gene sequencing have improved over the years, and the cost for the same has plummeted exponentially. In the year 2008, the cost of gene sequencing was 10 million dollars. As of today, it is only a 1000 dollars. In the future, it is expected to reduce further. It is estimated that one billion people will have their genes sequenced by 2025. So, within the next decade, the genomics data generated will be somewhere between 2 – 40 exabytes in a year. An exabyte is ten followed by 17 zeros. Before coming to how data will help in curing cancer, let us take one concrete example and see how data can help in conquering a disease. Data and its analysis helped in finding out the cause of one infectious disease and fight it, not now but in nineteenth-century itself! Yes, in the nineteenth century! The name of that disease is Cholera. Clustering in the Nineteenth Century – the Cholera breakthrough John Snow was an anesthesiologist and cholera broke out in September 1854 near Snow’s house. To know the reason for cholera, Snow decided to note the spatial dimensions of the patients on the city map. He marked the location of the home address of patients on London’s city map. With this exercise, John Snow understood that people suffering from cholera were clustered around some specific water wells. He firmly believed that a contaminated pump was responsible for the epidemic and against the will of the local authorities replaced the pump. This replacement drastically reduced the spread of cholera. Snow subsequently published a map of the outbreak to support his theory, showing the locations of the 13 public wells in the area, and the 578 cholera deaths mapped by home address. This map ultimately led to the understanding that cholera was an infectious disease and quickly spread through the medium of water. John Snow’s experiment is the earliest example of applying the clustering algorithm to know the cause of illness and help eradicate it. In the nineteenth century, John Snow could apply clustering algorithm on a London city map with a pencil. With cancer as the target disease, this level of analysis is not possible with the same ease as John Snow’s Analysis. We need sophisticated tools and technologies to mine this data. That is where we leverage the capabilities of modern technologies like Machine Learning and Big Data. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Big data and Machine learning – tools to fight cancer Vast amounts of data along with machine learning algorithms will help us in our fight with cancer in many ways. It can help us with diagnosis, treatment, and prognosis. Mainly, it will help customise the therapy according to the patient, which is not possible otherwise. It will also help deal with the long tail of the distribution. Given the enormous amounts of Electronic Medical Records (EMR), data generated and recorded by various hospitals; it is possible to use ‘labelled’ data in diagnosing cancer. Techniques like Natural Language Programming (NLP) are utilised for making sense of doctor’s prescriptions and Deep Learning Neural Networks are deployed to analyse CT and MRI scans. The different types of machine learning algorithms search the EMR databases and find hidden patterns. These hidden patterns will help in diagnosing cancers. A college student was able to design an Artificial Neural Network from the comfort of her home and developed a model that can diagnose breast cancer with a high degree of accuracy. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Diagnosis with Big Data and Machine Learning Brittanny Wenger was 16 years old when her older cousin was diagnosed with breast cancer. This inspired her to make the process better by improving the diagnostics. Fine Needle Aspiration (FNA) was a less invasive method of biopsy and the quickest method of diagnosis. The doctors were reluctant to use FNA because the results are not reliable. Brittanny thought of using her programming skills to do something about it. She decided to improve the reliability of FNA which would enable the women to choose less invasive and comfortable diagnostic methods. Brittanny found public domain data from the University of Wisconsin that included Fine Needle Aspiration. She coded an Artificial Neural Network (ANN) which is inspired by the design of human brain architecture. She used cloud technologies to process the data and train the ANN to find the similarities. After many attempts and errors finally, her network was able to detect breast cancer from an FNA test data with 99.1% sensitivity to malignancy. This method is applicable for diagnosing other cancers as well. The accuracy of diagnosis is dependent upon the amount and quality of the data available. The more the data available, the more the algorithms will be able to query the database, find similarities and come out with valuable models. Treatment with Big Data and Machine Learning Big data and Machine learning will be helpful not only for diagnosis but treatment as well. John and Kathy were married for three decades. At the age of 49, Kathy was diagnosed with stage III breast cancer. John, CIO of a Boston hospital helped plan her treatment with the help of big data tools that he designed and brought into existence. In 2008, five Harvard affiliated hospitals shared their databases and created a powerful search tool known as ‘Shared Health Research Information Network’ (SHRINE). By the time of Kathy’s diagnosis, her doctors could sift through a database of 6.1 million records to find insightful information. Doctors queried ‘SHRINE’ with questions like “50-year-old Asian women, diagnosed with stage III breast cancer and their treatments”. Armed with this information doctors were able to treat her with chemotherapy drugs by targeting the estrogen-sensitive tumour cells by avoiding surgery. By the time Kathy completed her chemotherapy regimen the radiologists could no longer find any tumour cells. This is one example of how big data tools can help in customising the treatment plan according to the requirement of each. As cancer is a long tail distribution a ‘one size fits all’ philosophy will not work. For customising treatments depending on the patient’s history, their gene sequence, results of diagnostic tests, a mutation found in their genes or a combination of their genes and environment, big data and machine learning tools are indispensable. upGrad’s Exclusive Software Development Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4   Drug Discovery with Big Data and Machine Learning Big data and Machine learning will not only help in diagnosis and treatment but also will revolutionise drug discovery. Researchers can use open data and computational resources to discover new uses for the drugs which are already approved by agencies like FDA for other purposes. For example, scientists at University of California at San Francisco found by number crunching that a drug called ‘pyrvinium pamoate’ which is used to treat pinworms – could shrink hepatocellular carcinoma, a type of liver cancer, in mice. This disease which is associated with the liver is the second highest contributor to cancer deaths in the world. Not only is big data used for discovering new uses for old drugs but can also be used for detecting new drugs. By crunching data related to different drugs, chemicals, and their properties, symptoms of various diseases, the chemical composition of the drugs used for those conditions and side effects of these medications collected from different media; new drugs can be devised for various types of cancer. This will significantly reduce the time taken to come up with new medicines without wasting millions of dollars in the process. Using big data and machine learning will no doubt improve the process of diagnosis, treatment and drug discovery in treating cancer, but it is not without challenges. There are many stumbling blocks and problems on the road ahead. If these blocks are not removed, and these challenges are not faced, then our enemy will get the upper hand and will defeat us in the future battle. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Challenges in using Big Data and Machine Learning to fight Cancer Digitisation Except for a few large and technically advanced hospitals, most of them are yet to be digitised. They are still following the old methods of capturing and recording data in massive stacks of files. Due to lack of technical expertise, affordability, economies of scale and various other reasons, digitisation has not taken place. Provision of open source EMR software, teaching how helpful these digital records could be in treating the patients and how profitable it is to the hospitals are some steps in the right direction. Data locked in enterprise warehouses As of today, only a few hospitals can digitally capture patient records. This apparatus too is locked away in enterprise warehouses and inaccessible to the world at large. Hospitals are reluctant to share their databases with other hospitals. Even if they are willing, they are plagued by the different database schemas and architectures. Critical thinking is required on this front about how hospitals can share their databases among themselves for their mutual benefit without being suspicious of each other. A consensus needs to be reached about the schema in which this data should be shared as well, for the benefit of all hospitals. This patient data should be democratised and utilised for the betterment of the future of mankind.   Patient data should not be allowed to be employed for the growth of a single organisation. Utmost care should be taken to anonymise the individual to whom the data belongs. If a person’s lipstick preference is leaked, then there is not much harm. If a person’s medical history is leaked, then it will have a significant impact on his life and prospects. The government should take positive steps in this direction and should help create a big data infrastructure for storing medical records of patients from all hospitals. It should make it compulsory for all hospitals to share their database within this shared infrastructure. Access to this database should be made free for patient treatment and research. Improvement in efficiency of Machine Learning Algorithms Machine learning is not a magic pill for cancer diagnosis and treatments. It is a tool that if used well can help in our journey to conquer cancer. Machine learning is still in a nascent stage and has its disadvantages. For example, the data on which these algorithms are trained needs to be very close to the data on which they are utilised for producing results. If there is a huge difference in them, then the algorithm will not be able to provide meaningful results which can be employed. There are many machine learning algorithms which exist with their own peculiar assumptions, advantages, and disadvantages. If we can find a way to combine all these different algorithms for achieving the results required by us, i.e. curing cancer, needless to say, we would have found a hugely beneficial outcome. The famous machine learning scientist Pedro Domingos calls it “The Master Algorithm”, who also wrote a popular science book of the same name. According to Pedro, there are five different schools of thought in machine learning. The symbolist, connectionist, Bayesian, evolutionaries and analogisers. It is difficult to go into all these different types of machine learning systems in this article. I will cover all the five types of machine learning systems in one of my future blogs. For now, we need to understand that all these different methods have advantages and disadvantages of their own. If we can combine them, then we can derive highly impactful insights from our data. This will be immensely useful not only for all kinds of predictions and forecasts but also for our fight against a vengeful enemy – cancer. To summarise, cancer is a formidable enemy which keeps changing its form frequently. We do possess new weapons in our arsenal now in the form of big data and machine learning, however, to face it competently. But to demolish it entirely we need a more powerful weapon than what we presently possess. The name of that weapon is ‘The Master Algorithm’. We also need to make some changes in the strategies and methods with which we are fighting this enemy. These changes are creating a big data infrastructure, making it compulsory for hospitals to share anonymised patient records, maintaining the security of the database and allowing free access to the database for patient treatment and research to cure cancer. Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Wrapping up If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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Piyush Kumar of MakeMyTrip explains Big Data Operations

6.08K+

Piyush Kumar of MakeMyTrip explains Big Data Operations

Piyush Kumar is the Head of Data Platform Engineering at MakeMyTrip. He heads the Data team (Data platform, Data Science, and Business Intelligence functions) at MakeMyTrip to support various Lines of Business such as Flights, Hotels, Holidays, and Ground. Along with defining Big Data strategy, he looks after designing and building a scalable and distributed machine learning platform for Big Data systems with real-time streaming and batch processing for Clickstream, Mobile, Transactional, CRM (Customer relationship management) & user feedback or reviews data. In an exclusive interview, Piyush provides valuable insights to UpGrad about how MakeMyTrip has leveraged Big Data, in line with current trends, to upgrade and enhance its product offerings. In this first video, Piyush talks about how MakeMyTrip uses Big Data to solve critical business problems in the area of customer segmentation, personalisation, building data pipelines, etc. He also explains the architecture of the Big Data system at MakeMyTrip. In the second video, Piyush shares insights on career planning for Big Data enthusiasts highlighting different career paths available in Big Data and the necessary skill sets required. So, Piyush spoke about how MakeMyTrip uses Big Data in their operations. He provided valuable insights to UpGrad about how MakeMyTrip is leveraging Big Data, in line with current trends, to upgrade and enhance its product offerings. He shared insights on career planning for big data enthusiasts highlighting the necessary skill sets required. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Are you planning a big data career? If you want us to cover other topics and interview other industry experts please let us know your thoughts in the comments section. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know?
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by Mohit Soni

17 Jan'18
The Business of Data Security is Booming!

5.25K+

The Business of Data Security is Booming!

This is an excerpt from the book ‘Breach: Remarkable Stories of Espionage and Data Theft and the Fight to Keep Secrets Safe’ by Nirmal John. Nirmal John has worked in advertising and journalism. He was earlier the assistant editor of Fortune. This book brings to light several incidents which till now were brushed under the carpet. It has instances of piracy, data theft, phishing, among many others. Even though he focuses on India, Nirmal John takes great pains to show links between underground international networks working to undermine data security. This excerpt has been taken from the chapter, ‘WHITE HAT Is GrEEnBACK’. This excerpt throws light on the normal routine of Saket Modi, a young CEO of a data security company, Lucideus. Fear. Urgency. Desperation. Panic. The themes that dominate that call for help are almost always the same. Pretty much everyone working in the cybersecurity business knows what it is to get that call, especially in the middle of the night. There used to be a time when break-ins were reported first to the police. But with the crime itself changing in nature, the way it is reported is changing too. The cops aren’t in control when it comes to new-age crime and theft of data. Dialling 100 may not get you far when it comes to data breaches. Saket Modi has been receiving these calls for a few years now. Modi is a baby-faced young man in his twenties who boasts an easy charm. His company is named Lucideus. It is a mash-up of two names from the ancient scriptures— Lucifer, the Latin word which came to be used to describe the devil, and Zeus, the supreme Greek deity who, among other things, dispensed justice. The mash-up is meant to be a reference to how the ‘bad’ and the ‘good’ come together online. Modi’s earlier office in Safdarjung Development Area market near IIT in Delhi was small and tastefully appointed in white (perhaps to accentuate the idea of the white hat hacker). He has since moved to a new, much larger space in Okhla, still tastefully appointed, still in white. He started out when he was in his teens, helping companies investigate breaches and shore up their cybersecurity. His carefully constructed reputation as a young white hat hacker brought him many projects over the years. These days he is among those advising the Government of India on matters of cybersecurity. Most of his projects for companies started with a call from a panic-laden voice. Modi particularly remembers one call from nearly five years back. It was the chief executive of one of India’s largest services companies at the other end of the line. The CEO introduced himself. He had met Modi on the sidelines of a conference; they’d exchanged visiting cards, and the chief executive had fished out Modi’s card to call him. ‘We think we are in major trouble. How quickly can you fly to Bengaluru?’ Modi was used to such requests from panic-stricken executives. He asked for a bit more context on what exactly had gone wrong. ‘The CEO of one of my top five clients, who is a huge name internationally, called me earlier today. He asked me to immediately stop all the operations I was doing for his company. He didn’t explain why. He just said that he will be calling me later to explain further.’ This was a client that contributed a very significant chunk to the Indian company’s top line. There were hundreds of employees from the Indian company working on the client’s projects. ‘I suspect there has been a breach, because of which all this could be happening. There are a few other things that would explain this reaction from the client. The truth is, I can’t afford to lose this client under any circumstances,’ the executive confessed. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Saket Modi took the next flight to Bengaluru. It was when he reached the office of the chief executive that Modi realized he wasn’t the only one who had got a call from him. There, sitting in the conference room and waiting to be briefed, were cyber-forensics experts from big accounting firms and other security researchers like himself. ” upGrad’s Exclusive Software Development Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4 ”   Even though this was par for the course when it came to how Indian companies reacted in such situations, Modi says he was taken aback. He says this has become a common practice when it comes to investigating breaches—the targeted company invites the names known to have cyber- forensics experience for a briefing post an incident and then gives the job to whoever bids the lowest. The question he asks is whether matters of security can be treated like other supplier relationships, especially in a crisis situation? This is probably how things work in many Indian corporations but, as he points out with evident displeasure, that is not how security and breach protocol should roll, particularly in a crisis situation. ‘security is not an L1 business.’ The chief executive briefed the gathering about the situation. There had indeed been a breach. He was looking for partners who could immediately deploy resources to find the vulnerabilities that had led to the breach and could help plug them. That was the only way he could convince the client not to terminate the contract. Modi ended up with the project even though his quoted fee was high. He flew in his team from New Delhi and, during the investigation, found several vulnerabilities in the organization that had resulted in the breach. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses The team started by pouring over the access logs which list the requests for individual files from a website. They then isolated the sectors which were compromised and sandboxed them. That meant that they used a separate machine, not connected to the company’s main network, to run programmes and test the behaviour of the malicious code. The idea behind doing this was to deduce if there were patterns in the type of data that was being compromised. If they could unearth a pattern, it could theoretically lead them to the hacker. Unfortunately, as in many such instances, Modi says, he couldn’t identify the source of the breach as its origins were from beyond Indian borders and hidden in a complex trail of IPs. His team couldn’t definitively pinpoint the location, but they pushed the chief executive and his company to shore up every single facet of its security protocol. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses The client continued the shutdown of the handling of his operations by the Indian company for a month, while Modi and his team worked on overhauling the Indian company’s security system. A month later, Modi had a call with the CEO of the company’s international client to detail the steps they had taken to make sure that breaches such as the one that had happened would not recur. Later, the client sent a team to audit the changes, and only when it was satisfied did the client allow the company to resume work on its projects. It cost the Indian company thousands of billable hours, not to mention damage to their standing in front of the client. If you like this excerpt and want to read real-life thriller stories full of hackers, police, and corporates, you can read the book; ‘Breach’ by Nirmal John. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Conclusion If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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by upGrad

01 Feb'18
Big Data: What is it and Why does it Matter?

5.6K+

Big Data: What is it and Why does it Matter?

If you’re a complete newbie in the world of Big Data, the term itself might be slightly confusing. Before we move to the technicalities, let’s ask two essential questions: How big? What data? The answer to the first question isn’t fixed – it would’ve changed by the time you’d have completed reading this line. For all we know, by the time you’ve read through the article, the total amount of data in the world would have soared by quite a bit. According to IBM, we create roughly 2.5 quintillion bytes of data per day – To put things in perspective, that is the capacity you’ll need to hold around 530,000,000 MP3 songs. Look at that number again, there are quite a lot of zeros in there. Now, let’s talk about the “what”. What data is this? It’s almost like the famous song by The Police, which goes something like… “Every breath you take, every move you make, every bond you break, every step you take, I’ll be watching you.” And that’s what they’re doing. By they, we simply mean the ones who’re in charge of collecting this data. Everything you do on the internet is adding to this colossal mountain of data. Your Facebook posts, Tweets, Snapchat stories, and whatever the kids are using these days – are just bricks in the huge wall of Big Data. Watch Youtube video. So, to answer your second question – the data in question is the very data you’re producing every passing moment. Every time you book a cab, or order food online, or even do a very basic google search – It’s all going on top of the heap. Everything is being collected. That’s what is making this big data, bigger – every passing minute.   Now that you’re in control of the situation, let’s dive a little deeper into the ocean of Big Data. Further, we’ll look at why exactly does Big Data matter so much, and who’re the ones benefiting from it? Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses What is Big Data? By now, we’re clear that Big Data is just an extremely large volume of data – both structured and unstructured – collected through a variety of sources and in a variety of formats. For the sake of a formal definition, you can have a look at how IBM defines “Big Data”: According to the data scientists at IBM, Big Data can typically be characterized by 4 V’s – Volume, Variety, Velocity, and Veracity. Volume Very simply, volume means how “big” the Big Data is. Like we said earlier, there’s no specific number to it, it’s ever-increasing. Variety The data we’re talking about comes from a number of sources, hence it is in numerous formats. We’re talking about data in the form of audio, video, pdf, email, and more! Most of this data is unstructured – implying not much sense can be made out of it without a proper study.   Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Velocity The flow of Big Data from the variety of sources we discussed above is massive and un-ending. Like we said, by the time you’ve read this article, the amount of Big Data in the world would have increased drastically. If you don’t believe us, listen to the guys at IBM who claim that by 2020, there’ll be 5,200 GB of data for each and every person on Earth. Yeah, talk about velocity! Veracity Veracity in context of Big Data simply refers to the noises and anomalies present in the data. When dealing with Big Data, veracity is one of the biggest challenges that data analysts face. By now, it’s clear that there’s a lot of data around us, almost too much to even think about! Making sense of this data is quite a daunting task in itself. For this, we have data analysts – the heart and soul of any organization’s analytics team – but how exactly do businesses use data to power their operations? Let’s see. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Big Data matters – but why? The organizations which earlier had to rely on the data collected through archaic spreadsheets now have access to tonnes of data on their customers. Data that can be used to overhaul their business and make profits like never before. Watch Youtube video. Sherlock Holmes puts it right – “It’s a capital mistake to theorize before one has data!” And today, businesses HAVE data – a lot of it. But how exactly does it help them? By carefully examining the data at hand, organizations are performing the following kinds of intricate analytics to gather actionable insights and perform better in the market: Social listening It gives the organizations the power to know the real-time feedback of their consumers. The days of polls or surveys are long gone – sentiment analysis provides much more comprehensive and actionable feedback. Tools like HootSuite, TweetReach, Klout, and BuzzSumo are just a few examples of social listening tools that help the organizations stay a step ahead by knowing what the consumers have to say, their sentiments, and feedback. Comparative analysis Thanks to Big Data, organizations can now compare their products, services, and overall brand image with their competitors by examining user-behavior metrics in real-time. Marketing analytics This helps organizations in promoting new products and services to the target audience in a much more informed and innovative way. There are various sophisticated tools dedicated to Marketing Analytics which help organizations keep a close eye on how their product is being received in the market. Some common tools for this include – Marketing Evolution, Predictive Modeling, Lattice Engines – all of which aim to improve the organization’s ROI by leveraging Big Data. Targeting Using this stream of Big Data analytics, organizations can dive into social media activity on any subject, based on a variety of sources, all in real-time. For example, let’s say you want to target specific customer groups and provide them with exclusive special offers – you can do that now, using Big Data. It’s a win-win situation for both the organization as well as the customers. The same tools as the ones discussed in Social Listening can be used for this purpose as well. Customer satisfaction Organizations can boost customer engagement manifold by analyzing Big Data from a multitude of sources. Also, using these metrics, they’re able to figure out, and eventually iron out any potential customer issues that might go viral – preserving brand loyalty and improving customer service, at the same time. Who’s using Big Data – Real-world applications It’s safe to say that no domain of business today is untouched by the magic that is Big Data. From banking, to healthcare, to social-media, to education, to even government sectors – the list can go on – everyone is trying their best to make sense of the data at hand and outperform their competition. Let’s see some major industries that are affected by the giant that is Big Data: Healthcare Providers Asia’s largest healthcare group – Apollo hospitals – is using Big Data and analytics to control HAI (hospital-acquired infections). Education Big data is used quite extensively to improve higher education. Take the example of the University of Tasmania. It has deployed a management system that tracks things like the time at which a student logs on to the system, time spent on different pages of the system, and even the overall progress of the student. Government Operations Big Data has a wide range of applications in government operations and services. They include energy exploration, fraud detection, environmental protection, financial analysis, and health-related research. We can go on and on about each and every industry, but we think you get the gist. Big Data analytics is being used wherever it is possible. And frankly, there’s no domain that can’t use a little data analytics to improve their operations. Because at the end of the day, data is all that’s there, and all there will ever be. To wrap things up… It’s safe to say that Big Data is not just a fad – it’s a revolution. It’s always better to stay on your toes when you’re in the middle of a revolution, or you’ll be left behind before you know it. What makes one particular organization stand out from the rest is the way they deal with their data. Having said that, it’s only fair to conclude by saying that the demand for good data scientists is, and will keep on, increasing. So, buckle up while you can, and get started with exploring the mad but genius world of Big Data! If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know?
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by Mohit Soni

05 Feb'18