Analytics & Cognitive
In this age of disruption, organizations need to navigate the future with confidence, embracing decision making with clear, data-driven choices that deliver enterprise value in a dynamic business environment.
The Analytics & Cognitive team leverages the power of data, analytics, robotics, science and cognitive technologies to uncover hidden relationships from vast troves of data, generate insights, and inform decision-making. Together with the Strategy practice, our Strategy & Analytics portfolio helps clients transform their business by architecting organizational intelligence programs and differentiated strategies to win in their chosen markets.
Analytics & Cognitive will work with our clients to:
Must have the trail-blazing DNA ( i.e. passion, skills , bootstrap with learning/research as 3 key qualities) to conceptualize and define critical integration , automation and optimization elements of the AV stack. The stack has 3 key base layers integrated with the Nvidia Tool Chain (Drive AGX & DriveSim) through well-defined SDKs & 3rd party APIs.
Foundation layer – 1: Nvidia SuperPod /A100 infrastructure that needs to be showcased as an efficient throughput, utilization and scalable environment running highly parallelized jobs across multiple clusters .
Design for optimized performance and “push-button” automation for AI Model Training and Inferencing test runs:
a) Network switch & fabric considerations for non-blocking, scalable bandwidth needs for best performance with varying dataset sizes & locations
b) Storage and caching hierarchy implementations (very critical) based on Training vs Inferencing workloads. Establish storage management guidelines for RAM/NVMe (internal storage) and External high speed storage (DDN, Netapp..) allocation to optimize performance and cost of running varying data-sets and workloads. Establish rules for when to trigger GPU Direct Storage (GDS) feature for lower latency and faster I/O workloads
c) Leverage Nvidia proposed management and orchestration software (Bright cluster manager) and associated plug-ins to customize a target customer’s Dev-ops environment needs. This will include – work-flow automation, provisioning, change control, software rebuilds and migration between cloud and local on-prem environments. Specific tasks include –
d) Operations /run-time optimization of A100 compute resources (MIG partitions) for varying workloads to maximize the utilization and throughput of jobs being scheduled in a given node cluster
i. Extend DIM platform for CAV specific data pipelines (must support Nvidia Tool chain expected formats for DNN training as part of their Drive SIM /AV Drive Worx-AGX environment) – design target data set conversion for logical and physical data models & schema
ii. Leverage representative Data Sets (TBD with Nvidia) for POC & Integrate 3rd party data-sets – HD Maps, Scenario creation (data fusion)
iii. Automation of end to end to end data pipeline: ingest – curate -transform – label . Tie with automated triggers for data runs & provisioning of infrastructure using Terraform scripts & workflow.
i. Lead the Make/Buy technical analysis on Maglev ( Nvidia tool integrated with their Superpod ) vs build your own vs existing party tools certified on Nvidia Tool chain
ii. Validate MLOPS Techstack with Nvidia tool chain – specifically –
a) TensorFlow & TensorRT + ML technologies from Nvidia & their AI model/Code (cuDNN_ libraries/repositories)
b) Model optimization Best Known Methods ( very critical) with Nvidia technologies
iii. Automate ML Ops flow in Nvidia’s tool chain environment (Drive AGX, DriveSim) – model access, containerization, version control, monitoring efficacy , infrastructure provisioning etc.)
Besides the 3 key stack layers , the solution needs to ensure Security /Administration with AWS IAM integration & a data protection ( cyber-hack) scheme (TBD) where rogue data injected into the data pipeline is discerned and prevented from being used in the AV Dev model training process.
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Requisition code: 94164
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