Artificial Intelligence & MLOps

Our team of experts works with customers to perform feature engineering and selection, extracting the most relevant data features for building ML models. We use popular tools such as SageMaker, Tensorflow/Keras to build, train, test, and deploy ML models. Customers can choose to Bring Your Own Model to AWS, and Caylent can help tune, operationalize, and scale those models on AWS. To help customers quickly leverage machine learning we implement AWS managed machine learning services such as Textract, Comprehend, Personalize, Kendra etc. that are purpose-built for a variety of use cases and industries.

Caylent is uniquely positioned to help customers plan and implement their MLOps strategy with its expertise in DevOps, data engineering, machine learning, application development, and production operations. Our team provides the necessary multi-disciplinary skills to successfully implement all aspects of MLOps, including deploying, operationalizing, scaling, and monitoring ML models in production environments. We leverage modern ML tooling such as SageMaker, Step Functions, Airflow, MLFlow, EKS, and ECS to automate ML workflows and streamline the end-to-end process of building, deploying, and monitoring ML models. We also implement continuous integration, deployment, and monitoring practices to ensure that ML models are constantly optimized and up-to-date.

Caylent understands that successful data analysis requires not just technical expertise but also the ability to effectively visualize and communicate findings. Our team is experienced in utilizing descriptive and inferential statistics techniques such as summary statistics and regression analysis to analyze data. We also implement predictive analytics techniques such as time-series forecasting and logistic regression. Caylent can bring nifty visualizations to your business users and help them understand AWS’s self-service tools for exploratory data analysis.

To transform into a data-centric organization, it's important to have automated data governance in place. Caylent's Governed Data Platform™ can help you ensure that your data is accurate, explorable, and secured on a well-designed modern data platform. We'll work with you to establish data quality standards and implement data cataloging, enabling better data organization and secure access. This will give you greater visibility into your data and help ensure that it is being used appropriately. Our solutions on AWS can also help you comply with regulations such as GDPR, HIPAA, and PCI DSS.

Caylent's AI team can help you turn your generative AI vision into reality with strategy workshops, rapid prototyping, and production-ready projects. Caylent’s extensive experience in creating machine learning (ML) models, automating ML operations, enterprise data strategy, data governance, and data operations positions us as a go-to partner for all aspects of generative AI enablement and implementation. Our experts will work with you to build custom generative AI solutions leveraging AWS native technologies such as Amazon Bedrock and Amazon Sagemaker Studio.

Accelerate your adoption with our Generative AI Strategy Catalyst where we’ll lead you through ideation sessions for use case prioritization and foundational model selection, or quickly implement a custom AI powered chatbot leveraging retrieval augmented generation with our Generative AI Knowledge Base Catalyst.