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TRUSTED BY THE WORLD’S MOST ICONIC COMPANIES.
★ ★ ★ ★ ★ 4.9 Client Rated
We specialize in designing and deploying custom machine learning models tailored to your specific business needs. Using AWS SageMaker, our team ensures your models are efficiently trained, tested, and deployed at scale, enabling you to make data-driven decisions with confidence.
Our data scientists help you prepare and process your data, using advanced feature engineering techniques to ensure optimal model performance. We leverage SageMaker’s built-in tools to clean, transform, and structure your data, creating a solid foundation for high-quality machine learning models.
We utilize AWS SageMaker Autopilot to automate the model training process, reducing the time and complexity associated with manual model selection and tuning. This service enables faster deployment of high-performing models, empowering you to achieve impactful results efficiently.
Coderio offers real-time monitoring and performance tracking for your models, using SageMaker’s tools to identify any drift or degradation. Our team ensures ongoing optimization of your models, so they stay accurate and relevant over time, adapting to new data and business changes.
We streamline the machine learning lifecycle by implementing MLOps best practices with AWS SageMaker. From version control and testing to continuous integration and deployment (CI/CD), our MLOps solutions ensure efficient and reliable management of your machine learning models.
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.
Smooth. Swift. Simple.
We are eager to learn about your business objectives, understand your tech requirements, and specific AWS SageMaker needs.
We can assemble your team of experienced, timezone aligned, expert AWS SageMaker developers within 7 days.
Our AWS SageMaker developers can quickly onboard, integrate with your team, and add value from the first moment.
AWS SageMaker is a fully managed machine learning (ML) service from Amazon Web Services (AWS) designed to enable data scientists and developers to build, train, and deploy machine learning models at scale. SageMaker simplifies the entire ML lifecycle by providing an integrated environment that includes tools for data preparation, feature engineering, model training, tuning, deployment, and monitoring.
One of SageMaker’s core strengths is its flexibility and support for popular ML frameworks like TensorFlow, PyTorch, and Scikit-Learn, allowing developers to work with familiar tools while harnessing the power of the AWS cloud. With features like SageMaker Autopilot for automated model selection, SageMaker Studio for collaborative development, and MLOps capabilities for continuous integration and deployment (CI/CD), SageMaker makes machine learning accessible, efficient, and highly scalable. By eliminating the heavy lifting of infrastructure management, SageMaker empowers businesses to accelerate their AI projects, optimize costs, and bring machine learning solutions to market faster and more effectively.
AWS SageMaker is a powerful choice for businesses seeking to harness the capabilities of machine learning without the complexity of managing underlying infrastructure. SageMaker’s fully managed environment provides end-to-end support for the ML lifecycle, covering everything from data preparation to model deployment and monitoring, allowing teams to focus on building effective solutions rather than on operational overhead.
With SageMaker, users gain access to high-performance computing resources and scalability on demand, making it ideal for projects of all sizes. SageMaker Autopilot simplifies model selection and tuning through automation, and SageMaker Studio offers a collaborative, integrated workspace for data scientists and engineers to develop and refine models efficiently. Additionally, SageMaker’s MLOps capabilities streamline model management, enabling continuous integration and deployment (CI/CD) for seamless updates. This comprehensive suite of tools and features makes AWS SageMaker an invaluable platform for accelerating AI initiatives, improving model accuracy, and reducing costs in machine learning workflows.
AWS SageMaker supports the entire machine learning process—from data preparation and model training to deployment and monitoring. By providing an integrated environment, SageMaker simplifies complex workflows, allowing teams to manage every phase of development in a single platform and reducing time to deployment.
SageMaker Autopilot automates crucial steps in model building, including feature engineering and model tuning. This automation accelerates the creation of high-performing models and allows teams to focus on refining outcomes and generating insights rather than manual adjustments, making ML more accessible to teams of varying expertise.
With AWS’s powerful infrastructure, SageMaker provides on-demand scalability, enabling businesses to handle any level of data processing and model complexity. Users can scale resources up or down as needed, optimizing both performance and cost for projects of all sizes, from experimental models to large-scale deployments.
SageMaker integrates smoothly with other AWS services like Amazon S3, AWS Lambda, and Amazon Redshift, creating a cohesive ecosystem that enhances data storage, processing, and deployment. This interconnected environment enables seamless data transfer and supports comprehensive, end-to-end machine learning workflows within the AWS ecosystem.
Built within AWS’s secure infrastructure, SageMaker offers strong security features, including identity management, data encryption, and regulatory compliance support. With AWS’s robust security framework, businesses can trust that their machine learning models and data are protected, allowing them to meet industry standards and maintain compliance.
AWS SageMaker is highly effective for building predictive models that help businesses anticipate outcomes, such as sales trends, customer demand, or inventory needs. By analyzing historical data, SageMaker models provide valuable forecasts that inform strategic planning and decision-making.
SageMaker supports complex deep learning models for image recognition, video analysis, and object detection. This capability is valuable in industries like retail, healthcare, and security, where tasks such as quality inspection, facial recognition, and anomaly detection can enhance operational efficiency and safety.
AWS SageMaker is widely used for NLP applications, including sentiment analysis, customer feedback interpretation, and automated language translation. Businesses can improve customer service, automate text analysis, and generate actionable insights from large volumes of textual data.
SageMaker enables the development of models that detect unusual patterns, helping industries like finance and e-commerce mitigate fraud risks. By identifying anomalies in real time, businesses can enhance security, reduce financial loss, and build customer trust.
Many companies use AWS SageMaker to create recommendation engines that provide personalized suggestions based on user behavior. These systems are essential in e-commerce, streaming, and digital media, helping enhance customer experience, increase engagement, and drive conversions.
SageMaker’s MLOps capabilities allow businesses to streamline machine learning workflows, automating model training, testing, and deployment. This support for continuous integration and deployment (CI/CD) simplifies model management, enabling organizations to keep models updated and responsive to new data.
Technologies that facilitate data collection, processing, and storage for machine learning.
Popular libraries and frameworks compatible with SageMaker for building and training models.
Services for deploying, scaling, and managing machine learning models in production.
Tools that support data analysis, visualization, and business intelligence reporting.
Technologies that streamline the ML lifecycle with automation, version control, and continuous integration/deployment.
Services that ensure data protection, identity management, and regulatory compliance.
PHP offers a wealth of pre-built tools and features that streamline development, making it especially suitable for projects like content management systems (CMS), e-commerce sites, and blogs. PHP’s architecture also supports easy integration with third-party applications, making it a practical choice for projects that need to interact with various external systems.
Python is known for its advanced capabilities in machine learning (ML) and artificial intelligence (AI), making it ideal for data-intensive projects. If your application requires deep analytics, robotics, or AI-driven functionalities, Python’s robust libraries and frameworks provide the tools needed for handling complex data operations and predictive models.
Both PHP and JavaScript are essential tools in web development, yet they serve distinct roles. The primary distinction lies in their usage: JavaScript is typically employed for front-end, client-side interactions, providing dynamic elements within the browser itself, while PHP operates on the server side, handling the backend processes of a website or application. Additionally, JavaScript executes directly within the user's browser, whereas PHP runs on the server, delivering processed data to the client.
We build high-performance software engineering teams better than everyone else.
Coderio specializes in AWS SageMaker technology, delivering scalable and secure solutions for businesses of all sizes. Our skilled AWS SageMaker developers have extensive experience in building modern applications, integrating complex systems, and migrating legacy platforms. We stay up to date with the latest AWS SageMaker advancements to ensure your project is a success.
We have a dedicated team of AWS SageMaker 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 AWS SageMaker, 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 AWS SageMaker 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.
