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Automate Machine Learning with H2O Driverless AI on Dell Infrastructure
Introduction Revision history The challenges of AI adoption
Machine learning challenges
Overview of AutoML and H2O Driverless AI Solution architecture for AutoML Invoking H2O Driverless AI from cnvrg.io MLOps Platform AutoML on an optimized Dell infrastructure Sizing of AutoML infrastructure Validated use cases for AutoML Dell Technologies services and support The Dell Technologies Customer Solutions Center Conclusion References Appendix A – Model serving in cnvrg.io

Machine learning challenges

Machine learning challenges

  • As organizations streamline decision making and improve customer experiences with AI, they are running into three core challenges: talent, time, and trust. First, there is not enough data science talent to build models for every use case by hand. Even with the right people, hand-coding takes too much time and is prone to errors. Then, the business must explain and validate each model so that users can trust the decisions that the model supports. The key to breaking through the talent, time, and trust barriers is the automation of advanced machine learning techniques with H2O Driverless AI.

    Talent

    Data scientists are in short supply for all but the largest technology companies. With H2O Driverless AI, both expert and novice data scientists can automatically build highly and transparent accurate models quickly. H2O Driverless AI is an award-winning AutoML product that embeds data science best practices from the world’s leading experts in engineering and data science, including the world’s top Kaggle Grandmasters. It uses a unique genetic algorithm that determines the best combination of features, models, and tuning parameters for each use case. Integrated best practices and guardrails ensure that models do not overfit the data and help with other common issues with which novice data scientists might need assistance. H2O Driverless AI enables companies to undertake more use cases with the talent that they already have or can easily find.

    Time

    Reducing the time to develop accurate, production-ready models is critical to delivering AI at scale. H2O Driverless AI automates time-consuming data science tasks such as advanced feature engineering, model selection, hyperparameter tuning, model stacking, and creation of an easy-to-deploy, low-latency scoring pipeline. With high-performance computing using both CPUs and GPUs, H2O Driverless AI compares thousands of combinations and iterations to find the best model in minutes or hours. Even experienced data scientists can use H2O Driverless AI to explore more techniques, feature combinations, and tuning parameters. H2O Driverless AI also streamlines model deployment that includes everything needed to run the model in production, taking the process time from experimentation to production from months to days.

    Trust

    For organizations to adopt AI at scale, data teams, business leaders, and regulators must be able to explain, interpret, and trust AI results. H2O Driverless AI delivers industry-leading capabilities for understanding, debugging, and sharing model results, including an extensive machine learning interpretability (MLI) toolkit, fairness dashboards, automated model documentation, and reason codes for each prediction for service representatives and customers. With H2O Driverless AI, data teams have everything they need to build trust with business stakeholders and regulators.