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Get to Know the Redapt ML Accelerator

By Paul Welch | Posted on June 24, 2020 | Posted in AI/ML

Machine learning (ML) is an idea that has taken the business world by storm in recent years. But for many enterprises, it has remained just that—an idea. In fact, by some estimates, 90% of ML models never make it to production.

While some of this failure rate can be blamed on the relative newness of the technology, a lack of skills with the specialized hardware—and software—necessary to deploy ML is only part of the story.

The bigger problem, especially for those enterprises actively working on machine learning projects, is a disconnect between data science teams and IT teams.

What’s driving this disconnect? One of the major culprits is the fact that many—if not most—data scientist ML models are developed on dedicated workstations or cloud instances that IT teams don’t actively manage.

Download Now: The CIO's Guide to Leveraging AI to Leap Ahead of the Market

Because of this, when it comes time to actually move an ML model from the workstation and into production, IT teams are often left scratching their heads about how to deploy the model at scale in a datacenter.  

What your organization needs to find success with artificial intelligence (AI) depends largely on two things: where you start and what level of support you receive during implementation. Trying to do too much at the beginning, or attempting to adopt new AI techniques such as machine learning (ML) without the proper expertise, are recipes for failure.

The challenges of implementing ML at scale

The amount of data in the world is unprecedented. And organizations like yours need powerful tools to make sense of all the data.

That's where ML comes in. With the right tools to digest and analyze the stream of data, leaders can make data-driven decisions to propel their organization forward.

ML promises to transform a business by teasing apart the relationship between data points. This helps organizations uncover and act upon insights.

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While the promise is real, many ML models wind up never leaving the lab due to challenges getting them to production. These problems arise because the lab is much different than production. Production is operated by IT, who often rely on traditional processes that were built for legacy systems and aren't able to scale quickly or at an optimal price point.

Redapt’s ML Accelerator package removes hurdles with development, such as legacy hardware. It also offers powerful algorithms at an optimal price point. 

What is the ML accelerator?

Designed to help enterprises of all sizes bridge the gap between data science and IT, our ML accelerator program can take you from zero to production ready with ML models in an accelerated time frame.

Our goal with the program is to help you increase your R&D production. To that end, we’ve leveraged our strong partnership with hardware providers like Dell EMC to assemble a hardware and software package to get you up and running with ML.

The package’s deliverables include:

  • Ready-to-use infrastructure installed and made operational at your datacenter
  • Engineering assistance with building and deploying your first ML model
  • A workshop focused on getting started on the platform, as well as a workflow plan taking you from dev to deployment
  • Best practices and knowledge transfer so you can build out your own ML models successfully

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Under the hood

ML accelerator is designed to be adaptable to your enterprise’s unique needs. Included in the infrastructure are:

  • HA Kubernetes based on Rancher
  • Hardware to support ML and DL workloads, including a base model with 4xv100 GPUs
  • Workflow management with Kubeflow and ready built containers
  • Self-service Jupyter notebooks for data exploration
  • Integration with Nvidia RAPIDS and Spark
  • IT monitoring and alerting with Promethus and Grafana

Combined, these tools and other tools provide you with everything you need to get up and running with ML—all within a production-ready footprint that has been minimized to lower initial startup costs.

3 steps to machine learning success

Redapt's accelerator delivers ML that scales as needed to increase agility and enhance operational efficiency. It lays the critical groundwork for successful AI adoption in a matter of months.

It's a wiser choice than jumping in feet first, which is more likely to result in failure with your AI initiative. Here's how to use Redapt's ML Accelerator package to find success with AI.

handshake_large-iconStep 1: Partner with Redapt

Before organizations can find success with AI, they must modernize their applications and datacenters.

Our ML Accelerator lays the critical groundwork for successful AI adoption in a matter of months by modernizing an organization's resources. It also mitigates risk associated with ML because it's built with best practices in mind.

ai_large-iconStep 2: Accelerate AI adoption

While our consultants get everything up and running for your business, we are also here to help your organization adopt AI quickly.

We can talk about powerful use cases, help you identify a good first project, or provide training to team members. This service is tailored to the individual needs of each organization, so trust that we are here to help you have a successful launch.

magnifying-glass-app_large-iconStep 3: Launch and evaluate

Our infrastructure is ready to use when it arrives at your datacenter and experienced consultants are standing by to solve any challenges that arise. This way, your business can move from zero to production ready on a rapid timeline.

It's time to launch your first model to production and experience the benefits of ML. During this phase, evaluation is key. See what happens, measure results, and keep moving forward.

 

Whether your organization is just starting to think about AI, moving into production, or operating at scale, it can help to talk things out with a supportive partner.

Beyond helping you actually take ML models from workstations to production, the ML accelerator program mitigates risk by providing you with experienced consultants with well-versed, proven best practices. Data science workloads will be moved to IT owned infrastructure using industry best practices for IT and software development.

This opens the door for your enterprise to employ ML not just to make smarter decisions, but to drive the creation of entirely new products and capabilities.

To learn more about crafting an ML and AI strategy, download our free eBook, The CIO’s Guide to Leveraging AI to Leap Ahead of the Market.

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