Advanced Computing in the Age of AI|Monday, October 26, 2020
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Leveraging MLOps to operationalize ML at Scale 
Sponsored Content by HPE

Most organizations recognize the transformational benefits of machine learning (ML) and have already taken steps to implement it.

However, they still face several challenges when it comes to deploying ML models in production and operating them at scale.

These challenges stem from the fact that most enterprise ML workflows lack the standardized processes typically associated with software engineering. The answer is a set of standard practices collectively known as MLOps (machine learning operations). MLOps brings standardization to the ML lifecycle, helping enterprises move beyond experimentation to large-scale deployments of ML.

In a recent study, Forrester found that 98% of IT leaders believe that MLOps will give their company a competitive edge and increased profitability. But only 6% feel that their MLOps capabilities are mature or very mature.

So, why the disparity?

Very few firms have a robust, operationalized process around ML model development  and deployment. It’s not necessarily through lack of trying or recognition—it’s not an easy undertaking.

Organizations looking to continually use ML to improve their business processes or deliver new customer experiences face consistent, significant challenges:

  • IT operations teams are not up to speed on ML
  • Lack of competency in key MLOps capabilities
  • Low collaboration between ML development and operations teams
  • Lack of a cohesive, efficient technology toolchain
  • Security and control of data spread across teams’ locations (cloud and on-prem deployments)

How do enterprises overcome these challenges and reap the benefits of artificial intelligence (AI) and machine learning? What are the key action steps to operationalize ML and deploy more ML use cases at enterprise scale?

Based on the findings from the HPE/Forrester paper, operationalization is a four-step process.

  1. Discover and execute high-priority, high-ROI ML use cases that can quickly shine a light on the efforts. That said, making sure the use cases are technically feasible and impactful is crucial to setting the stage for ML operationalization.
  2. Build the right AI team. Data scientists operating in a vacuum won’t give any organization the traction necessary for success. While data scientists are without a doubt the go-to experts for building the ML models, including IT, business analysts, project managers, designers, and developers in the AI team will provide a broader perspective and help mitigate last minute deployment issues.
  3. Analyze existing hardware, software, security, data access, and controls in place that impact the entire ML lifecycle. Identify where there are gaps, inefficiencies, deficiencies, and potential areas that can derail ML progress.
  4. Invest in the tools, technologies, and processes that both resolve issues identified in the analysis and simplify deployment, maintenance, and control.

HPE has the solutions to help enterprises succeed with ML. HPE Ezmeral ML Ops is a software solution that brings DevOps-like speed and agility to ML workflows with support for every stage of the machine learning lifecycle.

HPE Ezmeral ML Ops leverages containers and Kubernetes to support the entire ML lifecycle. It offers containerized data science environments with the ability to use any open-source or third-party data science tool for model development and the ease of one-click model deployment to scalable containerized endpoints on-premise, in the cloud—or hybrid. Data scientists benefit from a single pane of glass to monitor and deploy all of their data science applications across any infrastructure platform. More importantly, enterprises can rapidly operationalize ML models and speed the time to value of their ML initiatives to gain a competitive advantage.

To learn more about how to operationalize machine learning by leveraging MLOps at scale, read the whitepaper “Operationalize Machine Learning”.

 

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