The services and support you need for your data products.

Built from customer-proven patterns for analytics and machine-learning, NativeML is your end-to-end provider of cloud-native services and automation.

Databricks Logo
Snowflake Logo

We help you build, automate, and run your data products in 4 simple steps.

Data Product Architecture

Step 1: Design

We design a cloud-native architecture based on user needs, product requirements, and customer-proven patterns.

NativeML Data Product Architecture
NativeML Managed Pipelines

Managed Pipelines

Step 2: Ingest

Fast, reliable, inexpensive, and sustained data movement from sources into cloud-native storage.

NativeML Data Product Engineering

Data Product Engineering

Step 3: Transform

Transform unbounded, unordered, and messy data to help inform crucial business decisions.

NativeML MLOps


Step 4: Serve

Deploy, serve, and monitor your analytics and machine learning data products.

BREAkdown of Data Product Architecture

Step 1: Cloud-Native Design

NativeML Data Product Architecture

Automation planning and design

Sustainable data products require automation. NativeML helps you plan and design automation that enables engineering teams to iterate faster and build a data product that nails the business target.

Cloud-native focus

Unlike generic services companies that only bring people, we are dedicated to Databricks and Snowflake and come with cookbooks, expertise, standards, best practices, and proven strategies for cloud-native success.

Customer-proven, enterprise patterns

Unlike generic providers who are often “learning on the job” we utilize cloud-native design patterns driven from engagement with the world's largest enterprises, including fortune 500 companies in manufacturing and healthcare.

Supported and monitored, end-to-end

Uptime drives user-confidence and adoption. Our architectures include strategies for error handling, alerting, instrumentation, and monitoring.

Breakdown of Managed Pipelines

Step 2: Ingest Data to the Cloud

NativeML Managed Pipelines

Single source of truth

Once developed, pipelines are stored in source control and automatically rolled out across environments. This leads to increased reliability, lower maintenance costs, and lower ongoing technology debt.

Build and run your pipelines for a low, fixed price

NativeML builds and runs your data pipelines for a yearly fixed price. We take on implementation and support risk and insulate you from unexpected development complexities or issues.

Automation to add new data sources quickly

We use automation and best-in-class data ingestion technologies to centrally create data pipelines. Many new sources can be added in minutes or hours, instead of days. This leads to faster time to insight, faster development of data products, and delighted end-users.

24/7/365 Support

Pipelines are monitored and supported by NativeML engineers 24/7/365. Unlike most services companies, we're motivated to deliver on Service Level Agreements, not bill hours. Reliable data movement results in happy users!

Breakdown of Data Product Engineering

Step 3: Transform Your Data

NativeML Data Product Engineering

Operationalize machine learning

Deploy models into operationalized machine learning systems, integrated with your business processes. Allows you to move your data science out of the sandbox and into the enterprise.

Structured, clean, indexed data

NativeML engineering teams transform unbounded, unordered, and global scale data into structured, indexed storage that can help inform crucial business decisions or unlock new product features.

CI/CD enabled

Building data products require an automated development pipeline, including continuous integration and delivery. NativeML's cloud-native Software Development Life Cycle enables our data engineers to get test feedback on each incremental change resulting in more robust solutions in a shorter timeframe.

Engineering expertise

Data product engineering is delivered by highly trained and specialized solutions architects and data engineers who are experts on both Databricks and Snowflake. Using smaller teams of highly-experienced engineers, we outperform generic providers in costs, speed, and reliability.

Breakdown of MLOps

Step 4: Serve Your Users

NativeML MLOps

Best practices to lower risk

Serving machine learning products to customers requires operational transparency such as versioning, logging, error handling, and alerting. Many data scientists don't follow any of these practices. NativeML helps by providing standard application server infrastructure, KPIs for model performance, runbooks for customer errors, and versioning.

24/7/365 machine learning operations and support

MLOps includes 24/7/365 monitoring and administration of your integrated machine learning system.

Increased business confidence

How do you know your machine learning model is making the right decision? Monitoring application health and model accuracy provides business confidence in machine learning and deployed models.

Fixed-price support

You require predictable and consistent costs when operating your machine learning system. NativeML MLOps provides SLA-bound, 24/7/365 support for your machine learning system at a consistent, yearly price.

Take the next step.

We're ready to

We are focused 100% on cloud-native technologies and are able to use the elasticity, scale, agility of the cloud to lower costs, more quickly deliver data products, and increase revenue and profitability.

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