AI/ML

PyTorch is an open-source deep learning framework that offers flexibility and speed, enabling developers and researchers to design, train, and deploy AI models with ease. It is backed by the Linux Foundation and supported by AWS, Meta Platforms, Microsoft Corp, and Nvidia. PyTorch has become the go-to framework for cutting-edge AI research and enterprise-scale applications. Its popularity stems from a Pythonic, intuitive design, dynamic computation graphs that make experimentation seamless, and strong ecosystem support. What sets PyTorch apart from other popular neural network-based deep learning frameworks, such as TensorFlow, is that it uses static computation graphs. On the contrary, PyTorch uses dynamic computation graphs, allowing greater flexibility when building complex architectures. So, let’s dive deep into the nitty-gritty of PyTorch, understand its features, benefits, and major applications, where it enables faster experimentation and prototyping. What is PyTorch? PyTorch is an open-source deep learning framework that supports Python, C++, and Java. It is often used for building machine learning models for computer vision, Natural Language Processing (NLP), and other neural network tasks. It was developed by Facebook AI Research (now Meta), but since 2022, it has been under the stewardship of the PyTorch Foundation (part of the Linux Foundation). Despite being a relatively young deep learning framework, PyTorch has become a developer favourite for its ease of use, dynamic computational graph, and efficient memory usage. Understanding How PyTorch Works Two core components of PyTorch are tensors and graphs. Let’s understand them: Tensors Tensors are an essential PyTorch data type that are similar to multidimensional arrays. They are used to store and manipulate a model’s inputs, outputs, and other parameters. In addition, tensors resemble NumPy’s ndarrays, but unlike them, tensors can run on GPUs to accelerate computing for large workloads. Graphs Graphs are data structures consisting of connected nodes (vertices) and edges that help the framework track computations and calculate gradients during training. With two processes: forward propagation, where the neural network carries the input and delivers a confidence score to the nodes in the next layer, until the output layer, where the ‘error’ of the score is calculated. Whereas, in the backpropagation process, gradients of the loss function are computed and sent backward to update parameters, PyTorch keeps a record of tensors and executed operations in a directed acyclic graph (DAG) consisting of Function objects. In this DAG, the leaves are the input tensors, and the roots are the output tensors. Unlike static frameworks like TensorFlow, PyTorch builds its graph at runtime (i.e., dynamically as the code runs). This makes the framework more flexible, easier to debug, and an ideal choice for accelerating innovation while supporting enterprise-grade deployment. PyTorch can use debugging tools of Python. Since PyTorch creates a computational graph at runtime, developers can use PyCharm, the IDE from Python, for debugging. Why Do We Need PyTorch? PyTorch works very well with Python, and uses its core concepts like classes, structures, and loops, and is therefore more intuitive to understand. The PyTorch framework is seen as the future of deep learning. There are many deep learning frameworks available to developers today, with TensorFlow and PyTorch among the most preferred. PyTorch, however, offers more flexibility and computing power. For machine learning and AI developers, PyTorch is easier to learn and work with. 5 Key Features of PyTorch Here are some capabilities that make PyTorch suitable for research, prototyping, and dynamic projects. 1. Easy to Learn PyTorch follows a traditional programming structure, making it more accessible to developers and enthusiasts. It has been well documented, and the developer community is continuously improving the documentation and support. This makes it easy for programmers and non-programmers alike to learn. 2. Developer Productivity It works seamlessly with Python, and with many powerful APIs, can be easily deployed on Windows or Linux. Most PyTorch tasks can be automated. Which means with just some basic programming skills, developers can easily boost their productivity. 3. Easy to Debug PyTorch can use Python’s debugging tools. Since PyTorch creates a computational graph at runtime, developers can use PyCharm, the IDE for Python, for debugging. 4. Data Parallelism PyTorch can assign computational tasks amongst multiple CPUs or GPUs. This is made possible by its data-parallelism feature, which wraps any module and enables parallel processing. 5. Useful Libraries PyTorch is supported by a large community of developers and researchers who have built tools and libraries to expand PyTorch’s accessibility. This developer community actively contributes to developing computer vision, reinforcement learning, and NLP for research and production. GPyTorch, BoTorch, and AllenNLP are some of the libraries used with PyTorch. This provides access to a robust set of APIs that further extend the PyTorch framework. Top Benefits of PyTorch Python-friendly: PyTorch was created with Python in mind (hence the prefix), unlike other deep learning frameworks that were ported to Python. PyTorch provides a hybrid front end that enables programmers to easily move most of the code from research to prototyping to production. Optimized for GPUs: PyTorch is optimized for GPUs to accelerate training cycles. PyTorch is supported by the largest cloud service providers, including AWS, which currently supports the latest version. AWS includes its Deep Learning AMI (Amazon Machine Image), which is optimized for GPUs. Microsoft also plans to support PyTorch on Azure, its cloud platform. PyTorch includes built-in data parallelism, enabling developers to leverage multiple GPUs on leading cloud platforms. Plethora of tools and libraries: PyTorch comes with a rich ecosystem of tools and libraries that extend its capabilities and availability. For instance, Torchvision, PyTorch’s built-in set of tools, allows developers to work on large and complex image datasets. Large Network & Community Support: The PyTorch community, comprising researchers across academia and industry, programmers, and ML developers, has created a rich ecosystem of tools, models, and libraries to extend PyTorch. The objective of this community is to support programmers, engineers, and data scientists to further the application of deep learning with PyTorch. Production & Deployment Challenges with PyTorch Scaling Models Efficiently: Sometimes, scaling applications at production requires optimization to support a large user base. System Integration: PyTorch models don’t usually integrate with the overall enterprise workflow, so APIs or conversion tools are frequently required. Performance Optimization: To reduce latency and boost throughput in real-time applications requires tuning and, in some instances, specialized hardware. Lifecycle Management: Model drift may occur, so PyTorch models need monitoring, retraining, and version control to ensure reliability. 5 Ways in Which AI Apps Can Use PyTorch With PyTorch, engineering teams can build deep learning predictive algorithms from datasets. For instance, developers can leverage historical housing data to predict future housing prices or use a manufacturing unit’s past production data to predict the success rates of new parts. Other common uses of PyTorch include: Image Classification PyTorch can be used to build complex neural network architectures, such as Convolutional Neural Networks (CNNs). These multilayer CNNs are fed thousands of images of a specific object, say, a tree, and, much like how our brains work, once trained on a data set of tree images, they can identify a new picture of a tree they have never seen before. This application can be particularly useful in healthcare for detecting illnesses or spotting patterns much faster than the human eye can. Recently, a CNN was used in a study to detect skin cancer. Handwriting Recognition PyTorch makes it easier to build systems that can recognize human writing across people and regions. These systems can even account for inconsistencies in human handwriting across people and languages by leveraging flexible tools for image processing and neural networks. Developers can train these models to learn and understand the shapes and strokes of handwritten characters, enabling apps to convert notes or forms into digital text automatically. It is a useful capability for digitized entries, smart pens, or educational tools. Forecast Time Series Another type of neural network is a Recurrent Neural Network (RNN). They are designed for sequence modelling and are particularly useful for training an algorithm on past data. It can make predictions based on historical data, allowing it to make decisions based on the past. For instance, an airline operator can forecast the number of passengers it will have in 3 months, based on data from previous months. Text Generation RNNs and PyTorch are also used to generate human-like text, from simple word structures to advanced chatbot responses. In text generation, by training RNNS and AI models on large datasets, developers can build AI applications that produce content, answer questions, or provide customized communication. For instance, AI assistants such as Siri, Google Assistant, and other AI-powered chatbots rely on similar systems to enhance and personalize user interaction. Style Transfer One of the most exciting and popular applications of PyTorch. It uses a set of deep learning algorithms to manipulate images and transfer their visual styles onto other images, creating new images that combine the data of one with the style of another. For example, you can use your vacation album images, apply a style-transfer app, and make them look like paintings by a famous artist. And as you would expect, it can do the reverse as well. Convert paintings into contemporary photos. Closing Statement Undoubtedly, PyTorch has made deep learning more accessible than ever before. Since its architecture is uniquely suited to support both rapid research experimentation and the scalability required for production development, it’s becoming a trusted framework in the research and development industry. Therefore, its growing influence reflects how advances built on PyTorch are moving beyond research to shape enterprise platforms. Salesforce, the world’s leading CRM platform, is one such example. The CRM platform embeds trusted AI across all its product offerings, from predictive analytics and SMS apps to Voice Agents that enhance customer engagement. As a Gold Salesforce Partner, Girikon helps organizations leverage these advances in Salesforce CRM and bring AI innovation and tangible business outcomes. Get in touch today to know how we can help enterprises embed trusted AI into CRM workflows and transform how you interact with customers.

TensorFlow is an open-source library created by the Google Brain team to build enterprise-grade machine learning algorithms. TensorFlow bundles together a host of machine learning models and algorithms and with the use of common programming frameworks, makes them useful. TensorFlow uses Python and JavaScript to build user friendly APIs for connecting with apps, and uses core C++ to execute the app functionalities. While it is still early days for machine learning technology, it continues to evolve rapidly, introducing us to a new world of advanced algorithms and deep learning. Deep learning uses algorithms commonly referred to as Neural Networks. As the name suggests, they draw inspiration from our biological nervous systems, led by the brain, to process information. Deep learning algorithms enable computing devices to identify every single bit of data, establish what it means and learn patterns. TensorFlow is a tool to develop deep learning models. It is an open-source AI library that uses data flow graphs to build learning models. With TensorFlow, programmers can build large-scale, multi-layered neural networks. TensorFlow is primarily used to perceive, understand, classify data and create predictive models. Main Use Cases of TensorFlow While TensorFlow can be used for many applications, here are 5 commonly used applications in the world of artificial intelligence. Voice/Sound Recognition One of the most popular use cases of TensorFlow is audio signal based applications. When fed appropriate data, neural networks can perceive and understand audio signals. These can be: Voice recognition — primarily used in Internet of Things (IoT) applications, Automotive applications (Voice command based actions), Security (Authentication) Voice search — Commonly used in Telecom and by mobile phone manufacturers Sentiment Analysis — used in CRM applications Flaw Detection (noise analysis) —Automotive and Aviation applications The world is familiar with the common use case of voice-search and voice-activated assistants. This use case has been widely popularised by smartphone manufacturers and Mobile OS developers such as Apple’s Siri, Google Assistant and Microsoft Cortana. Understanding and analyzing language is another widely used use case for Voice Recognition. Speech-to-text applications are used to extract and understand sound bites in larger audio files, and convert it into text. CRM is another area were voice/sound based applications can be implemented to deliver a better and smarter customer service experience. Imagine a scenario where TensorFlow algorithms fill in for customer service reps, and guide customers to the right set of information much faster than an agent. Text Based Applications This is another commonly used application of TensorFlow. Text based applications for instance sentiment analysis can be used in CRM apps and Social Media for improving the customer or prospect experience, Threat Detection, used in Social Media and Government applications and Fraud Detection, used by Insurance, Finance companies are some common examples. Language Detection is another popular use case of TensorFlow for text based applications. We are quite familiar with Google Translate. More than 130 languages can be translated into each other using this service. An AI powered version of a translate engine can be used in common real world situations like translating heath diagnosis technical terminology or legal jargon in contracts into plain language. Text Summarization Google also came up with sequence-to-sequence learning, a technique for shorter text summarization. This can then be used to build headlines for news and articles. Another use case for TensorFlow popularised by Google is SmartReply. It automatically generates e-mail responses based on text recognition and contextual understanding. Image Recognition This use case is primarily used by Social Media and Smartphone Manufacturers. Facial Recognition, Image Search, Motion Detection, Computer Vision and Image Clustering are nowadays being deployed in Warehousing, Healthcare, Automotive, and Aviation Industries. Google Lens is another example where Image Recognition is being used to understand the content and context to help identify people and objects within images. TensorFlow object recognition algorithms have the ability to identify and classify random objects within larger images. This has found use in engineering modelling applications such as creating 3D spaces from 2D images. Facebook’s Deep face is another example of photo tagging using image recognition technology. Deep learning technology can identify an object in an image never seen before by analyzing thousands of images with similar objects. Healthcare Industry is also at the cusp of using Image Recognition for faster diagnosis.  TensorFlow algorithms can process information and recognize patterns much faster than the human eye to spot illnesses and detect health problems faster than ever. Time Series TensorFlow Time Series algorithms is another method used today to establish patterns and forecasting of time series data. Meaningful statistics can be derived by these algos along with recommended actions. TensorFlow Time Series algorithms allow forecasting of generic time periods apart from generating alternative versions of the predicted time series. A popular use case for Time Series algorithms is Recommendations. Time Series Recommendations has seen widespread usage amongst leading organizations such as Netflix, Google, Amazon, where they analyze and compare activity of millions of users to determine what a customer might wish to view or purchase. And with every interaction, while recording the activity of every action, these recommendations get even smarter. For instance, they throw up content what your family members or friends like or offer you a gift they might like. Finance, Insurance, Government, Security and Threat detection, Predictive Analysis, Resource Planning and forecasting are some of the other use case scenarios of TensorFlow Time Series algorithms. Video Detection TensorFlow deep learning algorithms can also be used on video data. This is used in Motion Detection in Automotive and Aviation, Role based Gaming, Security and Threat Detection. Today, universities are doing deep research on Video Classification at a large scale to perceive, analyze, understand, classify video data. NASA is using TensorFlow algorithms to build a system for orbit classification and object clustering of asteroids. Consequently, they will be able to classify and predict near earth objects. TensorFlow is an open-source framework, allowing developers the freedom to work on innovative and disruptive use cases, which will contribute further to Machine Learning technology. Amongst many things, TensorFlow’s popularity is primarily due to the computational graph concept, automatic differentiation, and the adaptability of its python API structure. This makes TensorFlow more accessible to developers to solve real problems. Here are some advantages of TensorFlow. 1. Scalable The TensorFlow library is well defined and structured. This means it works just as efficiently on a mobile device as on a powerful computer. 2. Open Source The TensorFlow library is available free of cost. Anyone, anywhere can work on it and use it to solve problems. 3. Graphs Tensorflow has a very powerful, inbuilt data visualization capability. This makes it easier for developers to work on neural networks. 4. Debugging Tensorboard, which is a part of TensorFlow, allows easy debugging of code blocks. This reduces the need for combing through the whole code. 5. Parallelism TensorFlow uses Central Processing Unit (CPU) and Graphics Processing Unit (GPU) for its functioning. Developers can use the architecture freely based on the problem they are trying to solve. A system uses GPU by default, which is why  TensorFlow is sometimes referred to as a hardware acceleration library because it reduces memory usage. 6. Compatible TensorFlow is compatible with popular programming languages like Python, C++, JavaScript, etc. This allows developers the freedom to work in an environment they are most comfortable with. 7. Architectural Support The TensorFlow architecture uses Tensor Processing Unit (TPU). This makes computation faster than what one would get when using CPU and GPU. TPU models can be easily deployed on the cloud and work faster than CPU and GPU. 8. Library management With the Google backing, TensorFlow is updated regularly with enhanced capability and flexibility with every release. AI is already an intrinsic part of Salesforce, the world’s leading CRM platform. As a Gold Salesforce Partner, Girikon is the preferred choice for many Salesforce customers across the globe. To know more about how AI can work for your business, contact us today.

The retail industry is dealing with increasing costs, declining sales, disgruntled customers, and stiff competition. This has encouraged retailers to adopt technology tools and solutions to enhance operational efficiency and improve customer interactions. In fact, the retail industry offers a lot of scope for technological transformation in areas of supply chain management, inventory management, sales, marketing and more thereby empowering retailers to increase the overall efficiency and profitability of their business.  One such technology that has the potential to transform the retail sector is artificial intelligence, which has helped retail businesses boost their speed, efficiency, and precision. Let’s dive in a bit deeper to find out how: Cognitive Inventory Management: The thinking capability of AI can be leveraged to determine the inventory components basis of the current sales trend. Depending upon the data available, AI draws inferences and forecasts of probable scenarios, provides recommendations, and even takes necessary actions with human approval. Well trained algorithms can be used to make decisions, which improve efficiency. An AI-powered system can process huge amounts of data and help create an inventory that is tailored as per the current market trends and customer demand.  Customer Purchase History: With an AI-powered CRM, organizations can target the specific interest of buyers. By breaking down huge volumes of data, AI tools can be used to identify the purchasing pattern of customers and understand their preferences. The AI-powered systems can further recommend similar kinds of items to the customers for purchase. To integrate an AI-powered CRM, retailers should consider partnering with a Salesforce implementation partner. Interactive Interactions: Customers always look for a prompt response and with an AI-driven chatbot customer service can be improved significantly. These chatbots are trained to communicate with customers and provide answers to frequently asked questions and provide customers with the necessary support. These bots gather useful customer data and use it to determine their preferences and purchasing pattern. Streamlining Supply Chain: The retail industry can streamline their supply chain by leveraging AI-powered tools. This might involve categorizing in-demand products, transporting them, tracking the shipment, and inventory management. With an AI-powered CRM in place, the retail industry is poised to attain new levels of efficiency while maintaining a good customer relationship. Why Should Retail Businesses Consider Leveraging Salesforce Einstein?  Salesforce Einstein is an AI-powered CRM that integrates predictive analytics, and machine learning. Apart from this, there are other features such as Einstein discovery that allow users to figure out hidden data patterns in customer data. Other important features such as Einstein prediction builder provides for business predictions through custom AI models. Bots connected to CRM along with the integration of computer vision make use of deep learning models for identifying the make, model, or brand via product images. Another innovative feature is the Einstein voice assistant that helps in interacting with users in real-time. Quick Wrap-up:  Given the growing preference for online retailing by customers, organizations working in the retail sector are gradually adopting AI-powered solutions to enhance operational efficiency and business productivity. With a robust AI-powered platform like Salesforce Einstein in place, retailers can provide exclusive customer experience while taking business efficiency to the next level. If you are looking for implementing Salesforce Einstein within your business ecosystem, it’s in your best interest to get in touch with a reliable Salesforce consultant.