Deploying Machine Learning with Wallaroo

‘Machine Learning is Hard. Deploying models doesn’t have to be.’ This is the tagline Wallaroo brings you. ‘Wallaroo is the purpose-built platform for last mile Machine learning.’ This machine learning platform facilitates the last mile of your ML journey, bringing machine learning to your production environment, with an incredible level of speed and efficiency.

Only a few businesses have the capacity to properly exploit their data in order to create better products or streamline their operations. Wallaroo changes the game by making it simple, quick, and low-cost for any organisation to put their boldest data and machine learning ideas into action and produce results. Models had to be meticulously re-engineered on a regular basis, the deployment software couldn’t process data rapidly enough even when operating on an alarming number of computer resources, and it was excessively difficult to evaluate how models were doing in order to monitor their accuracy. This is where Wallaroo enters the picture.
In addition to providing easy-to-use tools for data scientists, Wallaroo streamlines the deploy/run/observe steps of the ML life cycle, allowing them to deploy models in seconds, analyze data up to 12.5X faster, and significantly reduce compute costs, and iterate faster.

How do they do that? It’s simple, the process.

• DEPLOY- ML models in seconds with a single line of Python code and/or a flexible API
• RUN- ML with up to 80% lower cost and easily scale to more data, more models, and more complex models.
• OBSERVE – and identify sources of model under performance and production bottlenecks in real-time.
• Optimize- Live models based on the latest data and insights with no downtime.

This platform has 3 core components to offer

• With a Self-service toolkit, the user is provided with an Easy-to-use SDK, UI, and API for data scientists and ML engineers to deploy, manage, and collaborate. The Wallaroo platform can be installed in any type of environment (cloud, edge, hybrid and on-prem). Additionally, the Wallaroo platform supports ML pipelines across different model training frameworks (TensorFlow, sklearn, PyTorch, XGBoost, etc.). The Wallaroo platform also offers data connectors to process various types of data modalities. Data Scientists can leverage the Wallaroo platform’s self-service SDK, UI and API to collaborate, manage and deploy their ML models and pipelines in a production environment.
• Wallaroo’s purpose-built compute engine allows running models on vast amounts of data with optimized computational resource utilization, based on the size of data and complexity of ML pipelines to run. Blazing fast compute engine brings Distributed computing core written in Rust-Lang for breakthrough scalability and performance
• Advanced observability: Data Scientists can generate actionable insights at scale and help identify new business trends by analyzing model performance in real-time within the Wallaroo platform. It gives a Stream of comprehensive audit logs + advanced model insights to drive results.

The figure shows Before vs. after using Wallaroo for production AI.

Enterprises will often look to all-in-one MLOps platforms such as SageMaker, Databricks, or DataRobot to simplify deployment. However, these platforms force data teams to standardize on proprietary tools, processes, and formats. These tools will then lead to complexity as different business units within the same company might use different data platforms. One of our customers, for example, is all-in-one on a certain cloud, but because of mergers & acquisitions, its data engineering teams are supporting different deployment processes for multiple clouds. 

In response, companies will spend countless resources building their platform in-house, cobbling together open-source technologies such as Spark and MLflow, which might work within the current ecosystem but at the expense of performance and model observability. Wallaroo is designed to click into your ecosystem and seamlessly connect with everything around it. We provide a standardized process that ML engineering 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. You can also rest assured that all your data will remain yours. Everything that goes in and out of Wallaroo is private, secure, and only visible to those with permission to see it.

• Quickly connect with popular data sources and sinks, like Apache Kafka and Amazon S3. 
• Plug in custom integrations and even your own in-house solutions. 
• Rely on rapid support if you need to integrate something that isn’t available out of the box.

Wallaroo was specifically engineered for modern machine learning deployments, unlike Apache Spark, or heavy-weight containers. The core distributed computing engine is written in Rust language, not Java – so it runs at C-speeds and is Python-friendly. Our SDK was designed with data scientists in mind, and has incorporated direct feedback from our customers.

Wallaroo is a platform that enables the future of AI and analytics we always wished we had: one where cutting-edge AI and ML can be deployed in seconds, and data teams can deliver a higher value at a lower cost. We built it so your team can spend less time making your data work with your software, and more time making your data work for your business. 

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