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Seer Real AI End-to-End MLops Services

Your managed machine learning development infrastructure, Reinforced by a remote MLOps team.

Meaningful, precise data, market analyses and reports, - based on high-quality data collection and comparisons, - including precise forecast simulations as trend indicators, - based on a variety of relevant resource market data

Professional decisions - for your competitive advantage and precise forecasting models based on the selection and analysis of comprehensive market data as well as relevant framework data.

Data the value of the future - Seer Real AI offers clients the latest technologies to evaluate different markets with various standard reports or develops customized solutions that integrate, analyse and combine data sources in real time.

Deploying ML and DL models in production is one of the toughest challenges facing the industry. We know it because we are continuously trying to build Data and AI driven products at Seer Real -AI-. 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 overcome the challenges. With our unique best of breed hybrid approach to build cloud native machine learning automation systems

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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.

Simple

Understandable

Highly available

The Main Principles of MLOps

Automated data preparation and management.
Seamless data integration and validation.
Collaborative workspaces with unified management.
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

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

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

Bridge the communication gap between the research and production environments with a model registry, detailing all the model metadata.

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.

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 Seer Real

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

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 Seer Real

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.

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 Seer Real

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

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 Seer Real

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, 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 Seer Real

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 how we can help you focus on your core business while we deliver you a scalable, predictable, optimized machine learning model lifecycle

Why Choose Sear Real 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

With infrastructure configured, workflows set up, data cleansed, and pipelines automated, your team can immediately get to action and stay productive.

Reproducible Experimentation

Securing all data transactions in - out in inside your cloud environment to achieve compliance and

Best of breed MLOps Toolkit

Utilise the best technology stacks from open-source to commercial offerings and managed services, combine the best-in-class tooling to achieve the tasks with the highest efficiency

Lower GTM time for your ML Projects

No vendor lock-in. with our cloud agnostic approach you can deploy your data and ML pipeline on any cloud

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

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.