Machine Learning Operations (MLOps) Services for AWS
Streamline Your Training and Experimentation Pipelines
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.
Everything You Need for ML Success
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.
Tools, Best Practices, and AWS Services
Here’s some of the ways MLOps can streamline your machine learning lifecycle.
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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.
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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.
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Experiment Tracking
Easily compare different model runs, hyperparameters, and outcomes. SageMaker Experiments allows you to track these aspects and more.
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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
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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.
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Security and Compliance
Ensure that ML workflows are compliant with industry standards and organizational policies.
Driving Results with MLOps: Customer Success Stories
“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 and cutting 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.”
What is MLOps?
MLOps is a set of practices that combines Machine Learning, DevOps, and continuous delivery to automate the end-to-end machine learning lifecycle. Read more about MLOps here.
Why should I be concerned about MLOps?
Scale. MLOps is critical to scaling machine learning workflows. Similar to how DevOps can streamline and operationalize your development and deployment processes, MLOps helps you deploy models faster, achieve reproducibility between experiments, scale your solutions, and maintain consistency in your model quality. By helping your teams collaborate and automating the processes for these elements, your teams move faster.
What’s the difference between MLOps and DevOps? They seem related.
They are, but the distinction is the support of your infrastructure and its operations overall (DevOps) versus the support of your data science and engineering teams in their specific development of ML algorithms and solutions (MLOps). There are challenges specific to how ML models are developed, deployed, and operated at scale which are distinct from AWS best practices for your overall environment.
How should I handle model drift or changes in data distribution?
This is a common concern, and something best supported by model monitoring infrastructure. Setting appropriate alerts for performance metrics can identify drift, and you may want to institute periodic retraining and validation against new data.
How do I handle costs to ensure I’m not overspending on MLOps?
Having a robust cost analysis tool, like CloudHealth, which is included in both of our managed services, to help you track costs and attribute expenses can be critical to this work. You’ll also need to adopt best practices, like ensuring you’ve cleaned up unused resources, using spot instances for experimentation, and taking advantage of the elasticity of native services wherever possible.
Is MLOps always necessary? How do I know if I'm at the point where I need to think about adopting some of these practices?
Basic MLOps should always be in place for tracking the results of experiments and model tuning. However, the scale of your MLOps processes and tool sets really depends on the aim of your work, the size of your team, and the maturity of your solution. For small projects with a few ML models that rarely update and have limited impact, a full-fledged MLOps approach might be overkill. But if you find yourself having to update models frequently, coordinate a large number of them, or scale their application, it may be time to begin adopting MLOps. Challenges with collaboration, scaling, experimentation and reproducibility, and compliance and security can all indicate a need to adopt MLOps practices.
Get in touch
Schedule a Free Consultation With a Machine Learning Expert
Work with an MLOps partner who understands AWS, your goals and the agile processes required to bring value to your business faster.