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TRUSTED BY THE WORLD’S MOST ICONIC COMPANIES.
★ ★ ★ ★ ★ 4.9 Client Rated
We design and build custom AI models using PyTorch to meet your unique business needs. Whether you're tackling image recognition, natural language processing, or predictive analytics, our developers ensure your model is optimized for performance and scalability. With Coderio, you get models tailored to your data and goals.
We fine-tune and optimize existing PyTorch models for better accuracy, lower latency, and efficient resource usage. Through techniques like quantization, pruning, and model compression, we help businesses get faster, smarter models ready for production.
From data preprocessing to deployment, we build complete ML pipelines using PyTorch. Our team integrates tools and frameworks to automate training, validation, and inference, ensuring a smooth, scalable workflow. This allows your team to focus on innovation rather than infrastructure.
Switching from TensorFlow or another framework to PyTorch? Our experts handle smooth migration of your ML models, ensuring minimal disruption and preserving performance. We also offer refactoring and retraining services for legacy models.
We develop and deploy lightweight PyTorch models on edge devices. Ideal for IoT, robotics, and mobile applications, our services ensure fast inference and low power consumption while maintaining model accuracy.
We integrate PyTorch-powered models into your existing software infrastructure. Whether it’s web apps, mobile platforms, or internal systems, our developers ensure seamless integration with APIs, microservices, and cloud infrastructure.
Not sure where to begin with PyTorch? Our AI consultants provide strategic guidance, from assessing feasibility to choosing the right architecture. We help you make informed decisions, reduce risk, and accelerate time to market.
Ripley recognized the urgent need to modernize its Electronic Funds Transfer System (EFTS) to ensure seamless operations for its users in Chile and Peru. The existing system faced reliability issues, prompting Ripley to embark on a comprehensive overhaul. The objective was clear: to establish a robust and resilient EFTS that would consistently meet the evolving needs of customers in both countries.
Coca-Cola needed a solution to measure sentiment in comments, categorize themes, generate automated responses, and provide detailed reports by department. This approach would transform feedback data into a growth tool, promoting loyalty and continuous improvements in the business.
The project involved implementing a data Warehouse architecture with a specialized team experienced in the relevant tools.
Coca-Cola faced the challenge of accelerating and optimizing the creation of marketing promotions for its various products and campaigns. Coca-Cola was looking for a solution to improve efficiency, reduce design and copywriting time, and ensure consistency in brand voice. Additionally, the company sought a flexible, customizable platform that would allow the creation of high-quality content while maintaining consistency across campaigns.
Coca-Cola sought an intelligent customer segmentation system that could identify and analyze behavioral patterns across different market segments. The solution had to automatically adapt to new data, allowing for optimized marketing strategies and improved return on investment.
YellowPepper partnered with Coderio to bolster its development team across various projects associated with its FinTech solutions. This collaboration aimed to leverage our expertise and elite resources to enhance the efficiency and effectiveness of the YellowPepper team in evolving and developing their digital payments and transfer products.
Coca-Cola needed a predictive tool to anticipate customer churn and manage the risk of abandonment. The goal was to implement an early warning system to identify risk factors and proactively reduce churn rates, optimizing retention costs and maximizing customer lifetime value.
We are eager to learn about your business objectives, understand your tech requirements, and specific PyTorch needs.
We can assemble your team of experienced, timezone aligned, expert PyTorch developers within 7 days.
Our PyTorch developers can quickly onboard, integrate with your team, and add value from the first moment.
PyTorch is an open-source deep learning framework developed by Facebook’s AI Research lab. It is widely used for building neural networks and training models due to its flexibility, ease of use, and dynamic computational graph.
Unlike static frameworks like TensorFlow (in its early versions), PyTorch allows developers to define and modify models on the fly, making it particularly powerful for research and rapid prototyping.
PyTorch is favored for its intuitive syntax, strong GPU acceleration, and native support for dynamic computation. It simplifies the development of deep learning models, especially those requiring custom layers and architectures.
PyTorch integrates well with Python, making it easy to debug and iterate. With broad community support and robust documentation, PyTorch is ideal for both rapid experimentation and large-scale production deployments.
PyTorch offers dynamic graphing, allowing for easier debugging and greater model flexibility. Developers can modify model architecture on the fly, accelerating innovation and iteration.
From academic research to enterprise-grade applications, PyTorch supports a wide range of AI/ML needs, making it a flexible choice across industries.
With backing from Meta and an active developer community, PyTorch has vast resources, tutorials, and third-party tools that help teams solve problems faster.
PyTorch works natively with Python, enabling smooth integration with other libraries like NumPy, SciPy, and Pandas. This enhances developer productivity and ecosystem compatibility.
PyTorch offers TorchScript for model serialization and deployment, and works well with ONNX for cross-platform compatibility—making it suitable for production use.
PyTorch provides efficient GPU acceleration, which leads to faster model training and inference. This lowers development time and operational costs.
PyTorch is widely used for tasks like image classification, object detection, and segmentation. Its support for convolutional neural networks (CNNs) and integration with OpenCV makes it a go-to framework for computer vision applications.
Thanks to its support for RNNs, LSTMs, and transformer models, PyTorch is heavily used in NLP applications like chatbots, language translation, and sentiment analysis. Libraries like Hugging Face Transformers are built on top of PyTorch.
PyTorch supports the rapid prototyping of reinforcement learning environments and agents. It's frequently used in robotics, gaming, and algorithmic trading to train agents that learn from interaction.
From GANs to VAEs, PyTorch makes building generative models straightforward. These models are used in everything from image generation to synthetic data creation.
Healthcare companies use PyTorch for analyzing medical images such as MRIs, CT scans, and X-rays. Its flexibility and high accuracy are essential for diagnostics and anomaly detection.
Businesses use PyTorch to build predictive models for sales forecasting, customer churn analysis, and demand planning. Its compatibility with big data platforms makes it a strong choice for analytics teams.
Streamline model deployment, monitoring, and scaling using robust cloud solutions.
Ensure clean, structured, and usable data for training and inference with scalable ETL solutions.
Track model performance and reliability in real time with dedicated monitoring tools.
Build user interfaces that interact with PyTorch models seamlessly.
While TensorFlow has improved its usability over time, PyTorch is still preferred for its intuitive syntax and debugging capabilities. PyTorch's dynamic graph allows for real-time model adjustments, whereas TensorFlow traditionally required a static graph.
Scikit-learn is ideal for classical ML models and structured data tasks, but PyTorch is designed for deep learning. It supports complex architectures like CNNs and RNNs that Scikit-learn does not handle.
Keras is simpler but less flexible than PyTorch. While great for beginners, Keras can be limiting when it comes to building highly customized models. PyTorch offers deeper control over each component of the neural network.
We build high-performance software engineering teams better than everyone else.
Coderio specializes in Pytorch technology, delivering scalable and secure solutions for businesses of all sizes. Our skilled Pytorch developers have extensive experience in building modern applications, integrating complex systems, and migrating legacy platforms. We stay up to date with the latest Pytorch advancements to ensure your project is a success.
We have a dedicated team of PyTorch developers with deep expertise in creating custom, scalable applications across a range of industries. Our team is experienced in both backend and frontend development, enabling us to build solutions that are not only functional but also visually appealing and user-friendly.
No matter what you want to build with PyTorch, our tailored services provide the expertise to elevate your projects. We customize our approach to meet your needs, ensuring better collaboration and a higher-quality final product.
Our engineering practices were forged in the highest standards of our many Fortune 500 clients.
We can assemble your PyTorch development team within 7 days from the 10k pre-vetted engineers in our community. Our experienced, on-demand, ready talent will significantly accelerate your time to value.
We are big enough to solve your problems but small enough to really care for your success.
Our Guilds and Chapters ensure a shared knowledge base and systemic cross-pollination of ideas amongst all our engineers. Beyond their specific expertise, the knowledge and experience of the whole engineering team is always available to any individual developer.
We believe in transparency and close collaboration with our clients. From the initial planning stages through development and deployment, we keep you informed at every step. Your feedback is always welcome, and we ensure that the final product meets your specific business needs.
Beyond the specific software developers working on your project, our COO, CTO, Subject Matter Expert, and the Service Delivery Manager will also actively participate in adding expertise, oversight, ingenuity, and value.
Accelerate your software development with our on-demand nearshore engineering teams.
