Does your startup or organization need to rapidly adopt DevOps infrastructure to power your AI practice? When DevOps is not your core competence, launching and training ML model routines are not optimized, and can lead to excess costs and poor project predictability for development workflows and production deployments.
ML Ops helps to standardize and accelerate AI deployment processes with all the necessary infrastructure to forecast, setup, and manage GPU usage limits.
Maven Solutions designs and implements custom DevOps environments that help automate AI infrastructure and processes with Kubernetes that help organizations use and pay for GPU nodes only when they use them.
Deploy the trained and validated model as a prediction service and publish access through API SDKs. Reuse the standardized, clearly defined launch, tune, and deploy processes with internal best practices, security built-in.
Experiment with different models until the best model version is ready for deployment without breaking the bank. Get model version deployments and data versioning under control with full knowledge of when and how you use GPU nodes, and launch them only when you need them.
See and manage ML model releases and seamlessly integrate them into your continuous integration and delivery (CI/CD) environment software asset pipeline. Deploy ML models alongside the applications and services they use and those that consume them as part of a unified release process.
Track changes in the machine learning assets, reproduce results and roll back to previous versions if necessary. Implement and easily conduct code reviews for every model. Version control and make the training of ML models reproducible and auditable at every phase, from data processing to model deployment.
Aggregate large amounts of digital data from local databases and via APIs into catalogs to compile and harness it into a more consumable and comprehensive medium.
Version control cleaned data artefacts and prepare them for easy intake for feature engineering and data training and validation for the ML model.
Launch and tune data models with a click of a button, and activate GPU node resource use only when the model needs to use it.
Take advantage of CI automation to continuously run tests and deploy code across your ML pipeline.
Extend validation and testing of code to data and models in the pipeline with continuous integration in a straightforward way and in sync with your development process.
Version control build artefacts with filterable lists of model versions, clusters, applications and associated cloud resources.
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Deploys the newly trained models or model prediction services with ease with full visualization of code dependencies and detailed information about each artefact and code component.
Get simplified analytics dashboard to monitor usage, errors, and set up important monitoring thresholds on usage and performance.
Effective Machine Learning Operations processes and tools are essential for powering innovation and experimentation. Yet sensitive data, budget limitations, skills shortages, and continuously evolving technologies challenge every ML project's success. Without control and guidance, GPU costs can easily balloon, while training and deployment timelines may fail to meet deadlines. ML Ops can help solve the most critical problems of ML projects and help position AI projects toward success by providing the necessary infrastructure and awareness.
With streamlined and transparent infrastructure provisioning, start projects faster and run them more smoothly. Model creation and deployment automation results in faster go-to-market times with lower operational costs. Data scientists deliver more business value more quickly and efficiently, and become more strategic and agile in model management.
Boost productivity and accelerate the development of ML models with standardized development and experiment environments. Launch new projects, rotate between projects, and reuse ML models across applications. Create repeatable processes for rapid experimentation and model training. Collaborate and coordinate throughout the ML software development lifecycle for greater efficiency.
Improves troubleshooting and model management in production. Monitor model performance and reproduce behavior for troubleshooting. Track and centrally manage model versions and pick and choose the right one for different business use cases. Integrate model workflows with continuous integration and continuous delivery (CI/CD) pipelines to limit performance degradation and maintain quality for your models, even after upgrades and model tuning.
Automate various stages in the machine learning pipeline with optimal repeatability, consistency, and scalability. Control all process stages from data ingestion, preprocessing, model training, and validation to deployment.
With your entire infrastructure codified into code, automate resource management and workload orchestration for machine learning infrastructure. Automatically provision and dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.
Set up guaranteed quotas of GPU resources to avoid bottlenecks and optimize billing to catch errors "early and often." Enable your data science teams to quickly test new models and give your operations team the confidence that they'll work properly and without end-of-month cloud cost invoice surprises.
Create an efficient pipeline of resource sharing with streamlined deployment process and reduced setup time. Get better understanding and utilization of available resources with Kubernetes with the complexity of dealing with containerization stripped off.
Manage all aspects of ML systems for efficiency by supporting many of the governance activities with the resources they need.
Collaborate with other teams and members via simple documentation markdown and user groups with standardized permissions
Automate tracking important service level metrics, analyze staff actions, detect and resolve incidents and anomalies faster.
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Auto-generate comprehensive documentation for all models based on code and implement automated workflow actions
Implement zero-trust approach for internal and external model data users by default and eliminate unauthorized access or non-compliance
