MLOPS MANAGED SERVICE

Turbocharge AI Innovation with Diamanti’s MLOps Managed Service

Deliver high-performance production ML models quickly at scale. Powerful Kubernetes-native GPU platform that simplifies machine learning life cycle, from data collection to deployment and monitoring. Includes all the tools you need.

The AI/ML Challenge

From finance to healthcare to smart manufacturing, artificial intelligence and machine learning (Al/ML) models are being deployed to solve an array of challenges. These undertakings, while they all may be very different, all require a large commitment of resources to go from prototype to model training to full deployment. To address this challenge, Diamanti has developed MLOps Managed Service, a completely turnkey solution specifically designed for the full implementation of ML models-training, optimization, deployment-that are best suited to meet your needs.

Many Apps

Each data service has its own operational practices, but hiring specialists or buying support agreements for each is prohibitively expensive.

Many Environments

Containers solve the infrastructure differences between clouds and on-prem data centers for compute, but don't address the challenges of running stateful apps in different environments.

Uncontrolled Self-service

Developers want self-service, but you can't risk giving up control of corporate policies like security, data retention, backups, and more.

The Diamanti Solution

Diamanti MLOps Managed Service seamlessly addresses the entire ML lifecycle. Unlike platforms that cater to specific operations (e.g., data cleaning, deployment), Diamanti’s MLOps Managed Service gives you everything you need-hardware, software, support-to take over the entire process, from data ingestion and cleaning to analysis, visualization, model deployment and monitoring. A high performance architecture, automated optimization techniques, and extensive collaboration tools all support the primary goal of simplifying and accelerating ML model development, enabling teams to focus on driving innovation and delivering business value.

Why Choose Diamanti MLOps Managed Service

Flexible Model Management

The Diamanti MLOps Managed Service offers the flexibility to work on different types of operations and models simultaneously, performing quickly and effectively in a distributed environment.

ML Model Distribution

Once a model is undergoing training, the Diamanti solution anayzes and compares results, selects the most suitable model and makes it available to the widest possible user base, including both those who require or do not require coding.

Enhanced Reliability

Robust data management capabilities, including data lineage tracking, quality management, and governance, ensure compliance and reliable ML workflows.

No Vendor Lock-in

A modular architecture that supports integration with a wide range of machine learning frameworks eliminates the cost and inflexibility of vendor lock-in.

High Performance

Complete support for Kubernetes containerization as well as the use of innovative acceleration and off-loading technologies - including support for NVIDIA GPUs-enhances performance on the largest datasets while significantly reducing operating costs and data center footprint. Take full advantage of automated resource management and planning on clusters with GPU options as well as automatic data sharding and distribution.

The Diamanti Solution

The modular architecture of the Diamanti solution includes components for addressing every phase of the MLOPs topology:

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Dataset Module

Ingesting, cleaning and preparing datasets of any size without writing any code. Includes (as needed) category operations, dataset loading, dataset preprocessing and dataset versioning. Take advantage of a range of code-free data cleaning and transformation tools.

Cluster Module

Complete management of the distribution and parallelization of workloads. Compute, storage and networking infrastructure are all included as well as the latest Kubernetes distribution and a monitoring and management console designed for large, multi-cluster implementations.

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Experiment Module

Model training and evaluation. Take advantage of hyprparameter optimization through AutoTune (or manual tuning) as well as Integration with popular tools like PyCaret, TPOT, and H20. Users can create projects according to their own filtering and perform as many experiments as needed, including subexperiments under each experiment.

Deployment Module

View your model's results after training and deploy it for use taking advantage of the user interface and pipeline log.

Designed for ML Performance

Deploy processor-optimized workloads consistently and reliably across environments — on-premises, in the cloud and on both GPU and non-GPU-based resources - delivering the essential capabilities needed to accelerate Al model training while keeping costs under control.
Automated model optimization techniques like hyperparameter tuning and neural architecture search streamline model development. Scalable resource management with distributed training and efficient cluster utilization enable handling large scale workloads
  • On-Prem Parallel and Distributed Processing
  • Auto Scaling
  • Distributed GPU Utilization
  • High I/O Throughput via HCI

Resource Utilization/Lower TCO

The ability to share clusters and parallelize various Al/ML workflows allows for efficient utilization of computing resources and faster experimentation. Just as important. Diamanti's support for NVIDIA GPUs enables allocation of GPU resources to specific containers, addressing the competing demands of large application sizes. Diamanti further optimizes resource allocation by scheduling workloads onto nodes based on their availability and capacity. All of this helps control infrastructure footprint, software licensing costs and other expenses, Diamanti regularly delivers total cost of ownership savings.

Consistency & Portability

Using Kubernetes to encapsulate and manage Al/ML projects, with all their related data and dependencies, provides a structured and reproducible approach to these projects. This eliminates the classic challenge of inconsistent results due to varying environments, library versions, and system variables. Now you can develop a single ML pipeline and deploy it across multiple environments without worrying about compatibility issues and vendor lock-in. Your teams now have a central platform to monitor experiments and collaborate.

Pipeline Acceleration, Collaboration & Automation

Diamanti provides full support for Kubeflow, the framework for developing, managing. and running Al/ML workloads. By providing tools and APls that simplify the process of training and deploying models at scale. Kubeflow solves many of the challenges involved in orchestrating Al/ML pipelines. In addition, Kubeflow is able to accommodate the needs of multiple teams in one project and allows those teams to work from any infrastructure.
Acceleration via Automation: The DiamantiML platforms come with APls for connecting to virtually any automation platform including Ansible, GitLab, Jenkins, Terraform and many others.

Data Protection

Mechanisms such as replication, erasure coding, and snapshots are built into the platform. By replicating data across multiple storage nodes or regions and creating point-in-time snapshots, Diamanti's intelligent storage protects against data loss, corruption, and downtime. If one system faces an outage, the distributed nature of the containers ensures that deployments can quickly restart on other operational systems-zero RPO and RTO.

Enterprise Support & Services

By taking advantage of a fully managed service, any organization can gain all the benefits of Al model development and deployment more easily and without tying up expensive in-house resources. Support and services for installation, migration, upgrades, performance tuning and other Day-2 operations help ensure the transition is faster, smoother and provides an ongoing contribution to the business goals of today’s enterprises.

Enabling ML at Enterprise Scale

Our MLOps managed service offers unparalleled flexibility, scalability, and efficiency, positioning organizations at the forefront of the rapidly evolving ML landscape. Invest in our cutting-edge solution today and unleash the full potential of your ML initiatives.