MLOps on the Cloud

Maximize machine learning effectiveness by implementing the latest tools and best practices on the cloud

Automate ML Algorithms

Machine learning (ML) is essential in modern app development. Yet, many companies struggle to deploy ML models to production, and a vast majority of projects ultimately fail. Complicated model migrations, long deployment cycles, and other issues prevent organizations from reaching their full AI/ML potential. That’s why MLOps – the fusion of machine learning, DevOps, and data engineering – is critical.

Through MLOps, teams can automate the various lifecycle stages of ML algorithms and increase the likelihood of leveraging AI successfully at scale. InfraNext helps companies optimize their MLOps capabilities through Amazon Web Services (AWS), enabling them to generate more value from their data.

Our MLOps Services

Data Collection and Preparation

Gather, prepare, and analyze reliable training data using AWS tools like Data Wrangler, SageMaker Processing, and Ground Truth. Implement robust security and privacy from the ground up, and process large volumes of data quickly for future model development.

ML Model Development

Use AWS’ one-stop IDE, SageMaker Studio, to access managed built-in algorithms, open-source models, and pre-built Docker images that speed up the development process. Or, take advantage of SageMaker Autopilot to have AWS create ML models for you based on your data.

ML Model Training and Tuning

Manage ML experiments, profile training jobs, and optimize costs with SageMaker and other cloud solutions. Let AWS do the hard work of tuning your models and distributing training with GPUs so that you can focus data science and engineering resources elsewhere.

ML Model Deployment

Configure CI/CD pipelines and set up continuous monitoring to allow your ML engineers to stay on top of usage, consumption, and results. Accelerate your deployment process with serverless orchestration and execute batch transformations to make large-scale predictions.

Achieve Your Business Goals with InfraNext and AWS

Automate ML Workflows

Streamline your overall ML development process with automated workflows, and accelerate time to market for ML models that constantly improve over time.

Improve User Experiences

Implement key MLOps practices, such as continuous training and monitoring, to incorporate new insights about customers into your models in order to enhance their overall experiences.

Facilitate Collaboration

Empower your organization’s data science and IT operations teams to work together to innovate, solve hard problems, and simplify ownership over model versioning, governance, access, security, and usage.

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