Building on Top of Your Data Ecosystem Rather Than Rip and Replace

The Scenario

Most enterprises do not have a unified data ecosystem. Generally, it is several different systems composed of different platforms and applications built up over years and decades as business needs have evolved. MLOps teams are asked to scale the use of Machine Learning throughout the business in this balkanized data ecosystem. All-in-one vendors say if you migrate everything over to them and get rid of all your technical debt you’ll have a perfect, streamlined process; however, that’s not realistic. Different parts of the company will continue to use their own data platforms (e.g., Snowflake, Databricks, SageMaker, etc.), or Mergers & Acquisitions will often introduce new systems to support. But what if you didn’t have to scrap your current ecosystem to put ML into production at scale, across the enterprise, across different data environments within the enterprise?

Disruption vs. Integration

Do we have to change, or can we find a way to coexist? As a decision-maker, the main challenges revolve around estimating and managing the amount of time, effort, resources, and costs that go into integrating new technologies or making changes, within an already established set of products and operations to deliver on critical business initiatives.

Change can be beneficial, as we can:

• Conduct new ways of running a business
• Take advantage of the ever-growing advancements in technology
• Easily and continuously innovate

However, change also comes with disadvantages, as it can lead to:

• Disruption of business operations
• Loss of revenue
• Employee retention

Legacy technology still sits at the forefront of a lot of industries; consequently, if rash decisions are made without taking the time to vet the best fit options for business continuity, the work streams currently keeping the business at bay can crash and burn. COBOL is a great example of legacy tech still in use today and is running on older infrastructure to not interrupt all the systems it is connected to.

So, no disruption?

While disruption can shake things up and make headlines, integration provides the ability to connect all the dots without causing too much of a stir; moreover, in the case of your data science team, they will be able to continue what they do best: build models and generate insights to provide value to the business instead of having to disrupt their daily duties to learn how to use a brand-new tool.

Okay, this sounds like a better alternative, but who can I talk to that provides the integration option, all without vendor lock-in?

Incoming! Meet Wallaroo

Fortunately, there’s no need to sign over your data science practice to a comprehensive platform. With Wallaroo, you can take advantage of many benefits, including avoiding vendor lock-in.

Fit in, don‘t stand out.

Wallaroo is designed to blend into your ecosystem and seamlessly connect with everything around it. We provide a standardized process that ML engineering and data science teams can use to deploy, run and observe models across platforms, clouds, and environments (in the cloud, on-premises, or at the edge). Our Connector Framework neatly plugs our platform with your incoming and outgoing data points and takes care of the integration to get you up and running in no time. We can integrate with your existing authorization/security mechanisms, log analysis systems, alerting systems, deployment, and release management workflow, as well as assist with QA and governance.

Meet you (and your data) where you are.

Any cloud, anywhere you can deploy a Kubernetes cluster, even at the edge. We teamed up Wallaroo’s signature lightning-speed ML processing with the robust and highly available Kubernetes, so you can bring advanced edge ML to any device — even those with minimal RAM. We put everything you need into a seamless product experience leveraging the Wallaroo self-service toolkit UI, SDK, and API.

With our lightweight Kubernetes-stack, you can set up ML standalone or with edge connectivity. You can also run the standalone machine learning in environments that seldom have an internet connection. These can be environments deep underground, in international waters, or with such high security that they can’t be directly connected to the internet.

Augment your existing data flows.

Make simple HTTP API calls to Wallaroo from your existing code/infrastructure to get more value out of the existing data and gain new insights. Wallaroo’s observability and model insights allow for real-time metrics to measure business impact. Our simple, easy-to-use interface allows anyone on your team to explore powerful metrics and detailed analytics so they can effectively track, measure, and help improve your ML’s performance.

Reduce the human cost of going from trained model to production.

Because Wallaroo fits into your ecosystem, a data scientist can move models to production with minimal effort; additionally, your data scientists can use the tools they already know (Jupyter/python/etc.). With an intuitive SDK, UI, and API and support for common data workflows, Wallaroo takes care of the details to let data teams focus on the bigger picture. Our SDK allows data scientists to deploy their ML models against live data in two clicks of a button — whether it’s to a test, staging, or production environment.

Efficiently use your computational resources and your budget.

This highly performant, easily-scalable engine can analyze up to 100K events per second on a single server (beating the industry average of 5,000 events per second), making Wallaroo the fastest platform on the market for production ML. Wallaroo has outperformed some of the top competitors in this field, which has allowed our customers to realize the low costs to deploy and run their ML models for an overall low total cost of ownership (TCO).

About Wallaroo.

Wallaroo is a breakthrough platform for the last mile of ML, providing a simple, secure, and scalable deployment capability that fits into your end-to-end workflow. Our platform provides powerful self-service tools, a purpose-built ultrafast engine for ML workflows, observability, and an experimentation framework. Wallaroo runs in cloud, on-prem, and edge environments while reducing infrastructure costs by 80 percent. Our unique approach to production AI gives any organization the desired fast time-to-market, audited visibility, scalability — and ultimately measurable business value — from their AI-driven initiatives, and allows Data Scientists to focus on value creation, not low-level “plumbing.”

If you want to explore this further with us, such as access to the full SDK, giving us specific feedback about functionality/semantics/integration, or discussing how it can help with your use case, email us at deployML@wallaroo.ai.

You can find more information about Wallaroo at https://www.wallaroo.ai/blog.