Say hello, SaaS model observability 

Oren Razon

March 1st, 2022 min read

March 1st, 2022

min read

from Superwise gift image

I’m thrilled to announce that as of today, the Superwise model observability platform has gone fully SaaS. The platform is open for all practitioners regardless of industry and use case and supports any type of deployment to keep your data secure. Everyone gets 3 models for free under our community edition. No limited-time offers, no feature lockouts—real production-ready model observability. 

Head over to the platform now to sign up, integrate your models 

What drives us

Since the day that we started Superwise, we’ve worked closely with our customers to realize our mission of making model observability accessible to anyone. A SaaS platform that will end the need for years-long ML infrastructure and tooling integration projects without compromising an inch on self-service customization and security.

What guides us

There are four core values that resonate throughout the platform and everything we do for our customers.

Make it easy 

Easy to start. Easy to integrate. Easy to see value.

Model observability should be as easy and as obvious a choice as traditional software monitoring. That’s why Superwise is model and platform agnostic, comes with a host of plugins and an SDK, is API-first, and, last but not least, lets you sign up and start on your own. 

Make it customizable 

Custom metrics. Custom monitoring. Custom workflows.

You’re the ones that know your models and business the best. From issues you need to know about, such as bias, drift, and performance. To the workflows you need to build around issues, what domain knowledge and business KPIs need to be incorporated into ML decision-making processes, and how to best alert and empower your teams to resolve issues faster.

Make it secure 

Lightweight, secure, flexible deployments. Data doesn’t leave your organization.

We totally get it. Your data and models are sensitive, and data science and ML engineering teams shouldn’t need to install or manage complex infrastructure to support their observability needs. Whatever your deployment needs, be it pure SaaS or self-hosted, you have control to ensure that no raw data or plain values will ever leave your network. 

Make it scalable

Scalable technology. Scalable automation. Scalable pricing.

You scale, we scale. It’s that simple. Superwise is built for scale and works just as well on 1,000 models as it does on 1. To drive scale, automation is required, from embedded anomaly detection to reduce the tedious efforts of searching for anomalies. All the way up to an open platform approach that enables interaction with Superwise metrics and incidents via APIs. No less importantly, our pricing is flexible and gives you complete control over how and when you scale up or down. 

What’s next?

As awesome of a day today is for us, we’re just getting in gear. Obviously, we’re obsessed with creating a truly streamlined model observability experience that can be customized to any ML use case and that our users love. But for all our roadmap and plans, it’s not about us. How do you use Superwise? What do you love and wish to see? What’s not good enough, and what do you need to close the loop and streamline model observability?

How? Email me at oren.razon@superwise.ai, chat with us in-app, DM us. Whatever works for you, we’re here and would love to chat. 

Everything you need to know about AI direct to your inbox

Superwise Newsletter

Superwise needs the contact information you provide to us to contact you about our products and services. You may unsubscribe from these communications at any time. For information on how to unsubscribe, as well as our privacy practices and commitment to protecting your privacy, please review our privacy policy.


Featured Posts

Drift in machine learning
May 5, 2022

Everything you need to know about drift in machine learning

What keeps you up at night? If you’re an ML engineer or data scientist, then drift is most likely right up there on the top of the list. But drift in machine learning comes in many forms and variations. Concept drift, data drift, and model drift all pop up on this list, but even they

Read now >
Everything you need to know about drift in machine learning
July 12, 2022

Concept drift detection basics

This article will illustrate how you can use Layer and Amazon SageMaker to deploy a machine learning model and track it using Superwise.

Read now >
Concept drift detection basics
Data Drift
August 31, 2022

Data drift detection basics

Drift in machine learning comes in many shapes and sizes. Although concept drift is the most widely discussed, data drift is the most frequent, also known as covariate shift. This post covers the basics of understanding, measuring, and monitoring data drift in ML systems. Data drift occurs when the data your model is running on

Read now >
Data drift detection basics