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Beat the Odds of AI Success: Best Practices to Deploy Models Faster

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AI developers and data scientists face common challenges from pilot to deployment that often results in difficulties and delays to operationalize AI. According to presenter Mo Nomeli from Accenture*, “Going from idea to experiment and results with the least possible delay is the key to finding good models for building an AI-powered system.” But how do you get to results quickly?

It takes a combination of hardware acceleration and software optimization. Hardware acceleration is required to achieve higher performance and throughput, lower latency, and enable AI inference at edge. Software optimization, through oneAPI, provides optimized data processing and distributes machine learning and deep learning workloads across multiple hardware architectures.

In this session, you learn about AI reference kits, which are prebuilt AI solution references delivering improved human productivity and machine performance on industries including Healthcare, Utilities, Retail and more. Furthermore, kits cover machine learning topics such as classification, regression, clustering, computer vision, NLP, audio analytics, anomaly detections, time-series forecasting and more.

Each kit includes the following:

  • Solution Brief: An overview of the value proposition, describing the problem, solution, and impact.
  • Developer Guide: Recommendations for frameworks, algorithms, data processing techniques, hyper -tuning, quantization, deployment, including showing how to build the ML pipeline.
  • Code Repository: GitHub code snippets, configurations, datasets, and libraries before and after optimization.
  • Platform Architecture: A guide for setting up the best performing compute architecture.
  • Benchmarking Results: Performance gains & metrics showing impact of oneAPI optimizations.

A continuing drumbeat of new AI reference kit releases will continue through 2023.
Watch the presentation below to learn how to accelerate AI development cycles by using the tools and framework optimizations that are part of Intel’s AI software portfolio.

See the video here.


All models are optimized with Intel AI Tools powered by oneAPI for faster training and inferencing performance using less compute resources. The AI reference kits use components from Intel's AI software portfolio, including Intel® AI Analytics Toolkit and the Intel® Distribution of OpenVINO™ toolkit.

To learn more about the AI reference kits, visit

About our experts

Mo Nomeli, Senior Manager, Accenture

Mo specializes in architecting, positioning, designing, developing, and deploying of enterprise AI solutions based on business needs, leveraging components of advanced analytics and data science across industry and academia. Mo holds a PhD in Mechanical Engineering and served as a professor at the University of Maryland and as an adjunct faculty at the George Mason University.

Preethi Venkatesh, AI Customer Engineering Manager, Intel

Preethi is a Technical Consulting Engineer at Intel, responsible for software enabling and customer engagements in Intel AI software portfolio including Intel one API AI toolkit, Intel distribution for Python and Intel-optimized TensorFlow.