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. ClearScale helps companies optimize their MLOps capabilities through Amazon Web Services (AWS), enabling them to generate more value from their data.
“ClearScale demonstrated both its technical cloud expertise and creativity through our recent project. It was clear from the get-go that ClearScale had done this type of data management revamp time and time again. Their expertise in machine learning was a valuable complement to our own application experts. We're now well-positioned to create even greater value for everyone in the SRM Ecosystem from manufacturers, to our dealers, fleets and partners.”
- Satish Joshi, Chief Technology Officer, Decisiv
Read Case StudyOur MLOps Services
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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.
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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.
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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.
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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 ClearScale 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.
