End-to-End MLOPs Services

Your managed machine learning development infrastructure, Reinforced by a remote MLOps team. Today, everyone wants to be a machine learning hero, but only a handful of tech leaders know how to move an AI project from concept to production without night sweats (and awkward conversations with the CEO). You can be one of them. Without growing the headcount of your engineering team.
MLOps
img Before Neuromation:
Every machine learning model deployment is a painful struggle. Data science team is stressed because models crash. Infrastructure setup is long, version control is non-existent, and you cannot scale your AI capabilities. It all feels like one big chasm.
img With Neuromation:
Wait a minute, where's the chaos? All the cloud and on-premises infrastructure is fully set up and ready-for-use. Deployment pipelines are semi-automated. Dev neatly integrated. Data is cleansed, validated, and prepared for a new training round. All that is missing — a new idea for your data science team to explore.
mlops platform
Bringing ML and DL projects into production is as challenging as one-color puzzles. We know it because we are trying to drive break-through AI products to the market every day at Neuromation.

If you are looped into the best practices of machine learning, you know you need repeatable, continuous, and automated processes to scale. But it’s hard to get there when 95% of your ML teams’ productive time is tied up by infrastructure setup, management, and maintenance.

It’s time to turn the odds in your favor. With Neuromation — a machine learning development platform, set up and managed by us, ready for a spin from you.

See Our Managed MLOps Service in Action

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Mlops service in Action

Tell Me More, What is MLOps?

Machine Learning Operations (MLOps) solves problems, unique to every aspect of the machine learning model lifecycle by melding tested DevOps approaches with data management best practices into a repeatable framework for model development, testing, and deployment. So that you could deliver new models just like any other type of application.

You already know how the standard ML project goes. Pipelines get hacked together with some brittle scripts. Continuous testing is a foreign concept. Collaboration goes messy with data and code snippets being lost due to poor model control. And that's just the development part.

Without any health monitoring, the fearless surviving models still burn in production. And your team needs to get back to the dreaded first step of preparing production ready-data and stitching together yet another bevy of tools that may (or may not) help to get that thing finally running.

Then, in the other room, sits the happy software development team. Their development is fast and flawless thanks to CI/CD with automation testing deployed at crucial checkpoints. Apps fit in their containers just fine and fly through the deployment pipeline without a hitch.

MLOps attempts to bring the same level of experience to data science teams by:

Ensuring a greater level of collaboration during planning and development.
Adding reproducibility to model training, tuning, and deployment.
Enhancing scalability with hotkey access to the necessary tools and resources.
Infusing continuity into the entire production flow.
Mlops services
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Machine Learning Operations

The Main Principles of MLOps

Automated data preparation and management
Seamless data integration and validation
Collaborative workspaces with unified management
One-click libraries, notebook, and frameworks installation
Fast model replication and re-training
Tested and automated deployment pipelines
Scalable resources provisioning
Rapid model set up, training, and testing.
The goal of MLOps is to help you create a collaborative environment for continuously developing machine learning models and deploying them, hassle-free.

MLOPs Benefits

Faster time-to-market for new models
Faster time-to-market for new models
With most of the pre-development out of the way and effectively automated, your team can fully focus on building viable ML models.
Full visibility and reproducibility
Full visibility and reproducibility
Know what's working and when to re-train with a version environment and tools for building, evaluating, and comparing models' performance.
Lower risk of production failure
Lower risk of production failure
Bridge the communication gap between the research and production environments with a model registry, detailing all the model metadata.
Accelerate experimentation rate
Accelerate experimentation rate
When viable models can be replicated in a matter of clicks and deployed semi-automatically, you finally get the time to pursue new projects.

How MLOps Adds Value to Every Step of the Machine Learning Model Lifecycle

ML model lifecycle spans over data preparation, management, model training, evaluation, serving, and monitoring. At each step, you spin multiple plates (and pray that none breaks). The goal of MLOps is to make you stop juggling things and instead, roll them in one continuous flow.
Machine Learning Model Lifecycle Machine Learning Model Lifecycle
MLOps

Data Preparation & Management

Manual data prep and wrangling can eat up to 80% of your team’s time. While MLOps can’t make your data better, it can help you get better at handling it.

The MLOps way: Build an automated data preparation and management pipeline.
How we do it at Neuromation:
Program offline extraction or batch fetching form the target data source.
Automate data validation against a set schema to cleanse it.
Auto-distribute validated data into training/validation data sets.
Create a feature store — a catalog for organizing pre-made features.
Model Training mlops
Model Training
Model training can get messy when you can't properly attribute and manage the generated artifacts, along with crowing code branches. When none of these get logged, stored, and version-controlled, team productivity plunges.

The MLOps way: Automate version control and automate metadata management.
How we do it at Neuromation:
Select a line up of storage agnostic version control systems, adapted for ML workflows.
Integrate them into the platform and configure them.
Check that metadata from new training runs gets auto-committed to version control.
Build a metadata store to capture t relevant information for further analysis.
MLOPS model evalution
Model Evaluation
Manual model testing is menial work and a sure-fire way to miss some important performance metrics, especially as you test across different data segments. But it’s a crucial step for ensuring that you are pushing a working thing into production.

The MLOps way: Automate model evaluation and subsequent re-training.
How we do it at Neuromation:
Set up a framework for model monitoring and validation, using the selected toolkit
Ensure auto-capture of all the essential performance data from each model run
Record and store all the tidbits for easy reproducibility.
Create specific triggers for launching pre-training when the model didn’t perform well.
Model Serving MLOPS
Model Serving
Less than 10% of ML models make it past through to successful deployment, oftentimes because your research team cannot properly pass the model to production folks. You can put down that fire with MLOps.

The MLOps way: Set up “Model as a Service” cloud deployment.
How we do it at Neuromation:
Decide on the optimal framework for wrapping the model as an API service.
Or select and configure a container service for deployment.
Create a production-ready repository of models
And set up a model registry where all the relevant model metadata is stored.
Model Monitoring
Model Monitoring
Model monitoring, so that’s a thing too? If you don’t keep tabs on your model performance in a real-life setting, you are going to miss a huge concept drift heading your way sometime soon.

The MLOps way: Automated model monitoring and auto-triggers for retraining.
How we do it at Neuromation:
Pick the optimal agent for real-time model monitoring.
Configure it to capture anomalies, detect concept drift, and monitor model accuracy.
Add extra measures for estimating model resource consumption.
Specify re-training triggers and configure alerts.
...And that’s just one way how Neuromation can help you create a resilient machine learning model lifecycle — predictable, scalable, and continuous.
Why Choose Neuromation MLOps Platform

Our MLOps as a service covers two needs at once: a separate SaaS MLOps platform and in-house MLOps team. Without the double cost.

Short Track to Productivity
Short Track to Productivity
With infrastructure configured, workflows set up, data cleansed, and pipelines automated, your team can immediately get to action and stay productive.
Reproducible Experimentations
Reproducible Experimentations
We work on securing all data integrations and use air-tight encryption protocols for protecting all the data in, out, and on the cloud.
Flexible MLOps Toolkit
Flexible MLOps Toolkit
Mesh together best-in-class open source tools with commercial frameworks, favorites notebooks, and an array of libraries within one platform.
Lower TCO for ML Projects
Lower TCO for ML Projects
No vendor lock-in. With Neuromation you can run operations in the cloud, on-premises, or in hybrid environments. Switch between different options to optimize your infrastructure costs.
Efficient Collaboration
Efficient Collaboration
When routine tasks are automated and experiments run like the clock, your team can gather round and review shared data sets, models, and results, neatly stored and organized by us.
Top Security and Compliance
Top Security and Compliance
We work on securing all data integrations and use air-tight encryption protocols for protecting all the data in, out, and on the cloud.
We assemble a custom machine learning development platform just for you. Open sources or proprietary costs, on-premises storage, or any cloud provider — pick any stack you'd like and we'll stitch it together. Then we stay busy in the background, supporting your infrastructure and hopping in on a call whenever you need some hands-on help.
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