Tech

AI Revolution won’t come easy without effective AI Life Cycle Management

Milpitas, California, Oct 21, 2019 - Innovation is the key determinant for any enterprise to challenge its competitors and eventually gain customer’s confidence. Innovation without the appetite for change and continuous improvement often results in the demise of the product. Many enterprises have hopped onto the Artificial Intelligence track but staying relevant requires continuous improvement by monitoring the insights of AI.

The definitive and organized approach of Software Engineering does not suit well for building machine learning models which often relies on trial and error methodology to build models with the highest accuracy. With the emergence of new AI frameworks and toolsets along with its repetitive methodology to refine AI makes it harder for data scientists to build and operationalize ML models without insights into the quality and performance of ML models. 

AI Lifecycle :

The life cycle of any AI project comprises of 3 phases - namely, build, deploy and live phase. While Data Scientists build the models, operationalizing ML models are either done by data scientists or MLOps team. The success of AI projects not only relies on building effective ML models, but it also needs an integrated monitoring system to gain insights into the performance and quality of ML models and to manage resource constraints like processing power and memory.

AIQ: AI Assistants for Robust AI

AIQ consists of 3 bots to help with the three phases of the AI life cycle

AIQ Build Assistant:

Predera’s AIQ Build acts as the buddy system guiding both experienced and new Data Scientists to build, tune and train high-quality Machine Learning models with minimal effort. The build phase involves collecting and labeling data, feature engineering, exploring different models and finding the best parameter fit by repeatedly training and tuning the models. AIQ offers simple plug and play integration with a wide variety of ML stacks (Tensorflow, PyTorch, H2O, R, Keras, Scikit etc) allowing Data Scientists to experiment and change from one stack to another. During the Build phase, AIQ augments data scientists with best practices, tips and cumulative knowledge from experiments across the entire team. For every experiment, it also lets the data scientists log data, parameters, model artifacts, and other custom parameters while raising alerts for any violation. 

AIQ Deploy Assistant:

The flexibility to choose any environment and infrastructure powered by centralized configuration of AIQ eases the deployment of models to any kubernetes clusters either on cloud or on-prem with no prior operational knowledge. AIQ Deploy toolset aides in resource provisioning (CPU, GPU or TPU), commissioning distributed infrastructure, auto-scaling capabilities and integrates well with existing MLOps solutions such as Google ML Engine, AWS Sagemaker, Azure ML Studio, Kubeflow etc. AIQ also supports complex model deployment scenarios such as A/B deployments, shadow and phased rollouts which have now become a common pattern in data-driven applications, all while providing real-time insights and recommendations. 

AIQ Monitor Assistant:

AIQ not only helps data scientists build better models, but also helps them manage the models to maintain model accuracy, while also being socially aware by monitoring the bias and inclusion metrics. AIQ monitor toolset tracks all live and running model microservices to provide actionable and contextual alerts to data scientists, product teams and IT/DevOps teams accordingly.  Teams can then approve automated actions, or act on the recommended next-best actions to fix the live AI models, which could be one of training, re-training, re-tuning, pausing, overriding model predictions etc. 

AIQ also enables enterprises to benchmark live ML model performance with knowledge from other models in the industry. AIQ also logs and retains all the data, predictions and feedback so as to act on auditing and compliance requests. Future releases of AIQ monitor will also support applying business templates for KPI trends with pre-configured what-if analysis and custom slice/dice query layer. 

Summary

AI is no longer an option but turning into a necessity as it is becoming our way of life. Predera’s AIQ offers end-to-end solutions for enterprises to work as one team to iteratively build, deploy and monitor ML Models, thereby growing their business and improving customer experience. Predera’s AIQ empowers enterprises to be more robust in the ever-changing business landscape with its integrated full-stack visibility and insight in all stages of AI life cycle.

Fasten your time to market and get ready to evolve in AI Revolution with Predera’s AIQ.