Data
PROFESSIONAL SERVICES
MLOps Machine Learning Operations
Streamline Your Training and Experimentation Pipelines
OVERVIEW
Bring Your Machine Learning Models to Life
Bringing ML models to production is difficult and time-consuming, requiring the discipline of delivering ML models through repeatable and efficient workflows known as machine learning operations (MLOps).
When you work with Mission’s data experts for AWS MLOps, you will streamline operations, save time, improve your model quality, and avoid the risks typically associated with self-managing MLOps.
BENEFITS
Everything You Need for ML Success
Engage all the capabilities and technologies required to deploy, manage, govern, and secure your ML models in production. We’ll work with you to identify your ML infrastructure requirements, develop a solution, and then execute a plan of action to meet your ML model development, operations, and compliance needs.
Boost Innovation and Growth
Mission’s MLOps support provides you with repeatable and scalable processes to deploy faster, speed up your iteration time, and put your models into production rapidly.
Reduce Costs and Time to ROI
Operating without an MLOps strategy is a fragmented and costly endeavor, but we can develop an architecture that lets you monitor and adjust your models on the fly.
Improved Adaptability and Agility
Optimize your overall ML costs and time-to-market with automated CI/CD pipelines and workflows that eliminate the need for manual maintenance.
Modern ML Tooling
Work with us to learn and take advantage of the latest industry-leading ML tools and technologies.
Secure Company Assets
Protect your business’s data with a tailored MLOps solution that's aligned to your industry’s security, auditing and regulatory compliance requirements.
Collaboration and Scalability
Empower your data science and engineering teams to collaborate on models and pipelines that support any ML framework.
We talked to a few external companies and Mission was our clear preference. They understood our problem, and portrayed very clearly how they could use existing andcutting edge technology to solve it. It gave us the confidence that if we needed something changed or explained, Mission would be able to do it in a way that we’d be able to understand.
FEATURES
Tools, Best Practices, and AWS Services
Here’s some of the ways MLOps can streamline your machine learning lifecycle.
End-to-End Automation
Automate your ML pipeline from data preparation, training, validation, to deployment. Tools like AWS Step Functions can coordinate various stages of your ML workflow.
Versioning and Reproducibility
With services like Amazon SageMaker, you can maintain different versions of ML models, allowing for easy rollbacks and comparisons. Ensure every experiment can be recreated for debugging, auditing, and collaborating.
Experiment Tracking
Easily compare different model runs, hyperparameters, and outcomes. SageMaker Experiments allows you to track these aspects and more.
Scalable Training, Inference, and Testing
Train models on a cluster of machines and deploy models to endpoints that scale depending on the demand. Before deploying, validate performance and ensure that it meets the desired criteria
Integration with Data Lakes and Data Warehouses
Seamlessly access data stored in your data lakes or data warehouses using services like Amazon Redshift and Amazon Athena, simplifying the data preparation phase.
Security and Compliance
Ensure that ML workflows are compliant with industry standards and organizational policies.