Our solutions leverage cloud services crafted for optimized machine learning operations, ensuring an end-to-end, efficient, and automated machine learning lifecycle.
While the initial phase of our MLOps approach is about construction and development, the subsequent phase delves deep into maintenance, adaptation, and evolution. We recognize that as businesses grow and evolve, so do their data dynamics and computational needs. Hence, we place an equal emphasis on monitoring model performance, iterative enhancements, and ensuring longevity of deployed solutions. Through AWS’s vast toolset, we also provide an environment where models can be updated, retrained, and redeployed swiftly, ensuring businesses stay ahead of their less AI-savvy competition.
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What We Do
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Advantages of Semantive’s AWS-Powered MLOps
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Automation: AWS Step Functions at Work
AWS Step Functions provide a way to coordinate multiple AWS services into serverless workflows. We utilize this service to automate our machine learning workflows, ensuring smooth transitions between different stages of the model lifecycle, from data processing to deployment.
Collaboration: Real-time Model Sharing with SageMaker Studio
With SageMaker Studio, a fully integrated development environment for machine learning, our teams collaboratively create, train, and deploy your models. This promotes a synergistic workflow, allowing real-time sharing and rapid iterative development within teams.
Tracking: Comprehensive Model Evolution Insights
The lifecycle of an ML model involves multiple experiments, hyperparameter tunings, and algorithm adjustments. Our MLOps approach emphasizes the importance of tracking each model's metrics, configurations, and outcomes, providing a clear lineage and aiding in iterative enhancements.
MLOps Solutions
Our MLOps architecture leans heavily on AWS SageMaker, treating it as more than just a model training tool—it’s an integral platform that supports all MLOps stages.
SageMaker's built-in capabilities are essential for model tuning, versioning, and deployment automation.
Further, our MLOps pipelines incorporate AWS CodePipeline for continuous integration and CodeBuild for code compilation and testing.
By integrating these services, we ensure robust management of model versioning, testing, and validation.
The combined use of AWS Lambda for event-driven computations and AWS Fargate for serverless container management serves to enhance the deployment phase, while AWS CloudWatch offers indispensable model performance and health insights.
Extend Your Team with Semantive's ML Engineers
If you're looking to augment your team with expertise in Machine Learning operations on AWS, Semantive offers a flexible staffing model that allows you to extend your team with our experienced engineers.
Our specialists work as part of your team, seamlessly integrating with your existing processes and infrastructure to provide additional capacity, skills, and knowledge. You can scale up or down the number of engineers as needed, depending on your project requirements and budget. This model allows you to leverage the expertise of our specialists to help you achieve your business goals faster while maintaining full control over your project.
With Semantive's extended team model, you can reduce the costs and risks associated with hiring, training, and managing additional in-house staff while benefiting from the experience and skills of our cloud developers. Our team members are AWS-certified professionals with extensive experience in AI/ML technologies, and they are fully committed to delivering high-quality solutions that meet your business needs.
Contact us today to learn more about how our extended team model can help you achieve your business goals faster and more efficiently with the expertise of our cloud engineers.
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