Organizations are investing more than ever in machine learning (ML) and AI. Unfortunately, deployment brings difficulties, and many models never make it to production.
Amazon SageMaker is a purpose-built ML service that simplifies the process of building, training, and deploying ML models at scale. Unifying these capabilities with your data and analytics, it has the power to change businesses.
Maximizing this potential requires expertise and experience. Mission's team of certified ML architects guide you through planning, implementation, and optimization of Amazon SageMaker, unlocking the full value of your analytics, ML, and AI initiatives.
Why Amazon Connect
Unified Environment
Experience a single platform for all your analytics and data science tasks with Amazon SageMaker’s Unified Studio. Discover insights, build and test models, and get production-ready in the same platform you use for data processing and SQL analytics. Simplify user permission management and collaborate easily with shared notebooks.
Managed Your ML Lifecycle
Amazon SageMaker provides a unified platform for managing complex ML projects with bespoke tools that span the entire ML process. From data preparation and training to packaging and deployment, Amazon SageMaker handles the infrastructure so your team can focus on building models that drive business value.
Production Ready
Seamlessly deploy your models into production. Package consistently using SageMaker’s Model Registry and deploy with SageMaker Endpoints or Batch Inference Jobs. Use Amazon SageMaker’s Inference Recommender to help find the right instance type for your model and get recommendations for auto-scaling.
How Mission Can Help
Strategic Implementation
Our AWS-certified architects guide you through your Amazon SageMaker journey from assessment to deployment. We evaluate your current ML processes, identify pain points, and create a tailored roadmap that maximizes Amazon SageMaker's capabilities. We ensure your implementation aligns with your business goals while adhering to best practices for security, performance, and cost optimization.
MLOps Excellence
Mission can help you integrate Amazon SageMaker with continuous integration and deployment tools like Github Actions or Jenkins, bringing DevOps best practices to your model deployment process. Our team implements Amazon SageMaker Pipelines to help your data scientists create repeatable end-to-end workflows. Monitoring systems are established to track model performance and alert you when thresholds are exceeded.
Cost Optimization Expertise
Our experts help you optimize costs without sacrificing performance. Our team has expertise in benchmarking models to find optimal inference hardware, implementing strategies like rightsizing notebook instances, and using Spot Instances to reduce training or asynchronous inference costs. We also configure endpoint auto-scaling based on demand, ensuring you only pay for what you need.
eBook
Build a Strong Foundation on Amazon SageMaker
Our AWS experts have crafted the perfect guide to setting up for success on Amazon SageMaker. Discover common pitfalls, key insights, best practices and more!
A web-based integrated development environment provides everything you need to build, train, and deploy models in one place. Teams can collaborate by sharing notebooks while maintaining access to all of Amazon SageMaker’s tools and capabilities.
Unified Lakehouse
Access and query all your data across Amazon S3 data lakes, Amazon Redshift data warehouses, and other sources in a single unified platform. Secure your data with fine-grained permissions and organize access by project.
Model Development
Build, train, and deploy machine learning models in AWS. Access pre-built algorithms for common use cases, or bring your own algorithms using popular frameworks like TensorFlow, PyTorch, and MXNet.
Generative AI in Unified Studio
Rapidly create generative AI applications tailored to your business needs using cutting-edge models and your proprietary data. Accelerate development with Amazon Q Developer, discover data, and build models through natural language.
Data Processing
Prepare your data for analysis using a wide variety of tools. Amazon SageMaker’s Data Wrangler helps businesses take data preparation time from weeks to minutes with visual tools for data cleaning and transformation.
“Using SageMaker in the process is key because it’s more scalable and easier to manage than a population of EC2 servers. This makes it possible for our analysis tool to tap into additional compute resources to handle spikes in customer activity."
Johann Levy
Chief Innovation Officer
Get in touch
Want to talk about using Amazon SageMaker for your business?
Schedule your free Amazon Connect consultation with a Mission Cloud Advisor today.
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