12 Greatest Success Tips in Life

1. SKETCH THE OPPORTUNITIES
2. SETTING THE GOAL
3. STUDY PLANNING
4. SELF CONFIDENCE 
5. SELF ESTEEM 
6. SUCCESS STORIES 
7. STUDY TIME 
8. STUDY ENVIRONMENT 
9. STUDY NOTES 
10. STRATEGIES 
11. STRESS MANAGEMENT 
12. SOCIAL RESPONSIBILITY

VMware Workstation and Device/Credential Guard are not compatible. VMware Workstation can be run after disabling Device/Credential Guard.

Follow below link for solution
https://www.youtube.com/watch?v=CGpv2Dvzyeg

Journey with AWS SageMaker

SageMaker is a fully managed machine learning service offered by AWS.Build, train and deploy machine learning models on the AWS cloud.
As part of ML inference at Edge demo first have prepared my ML model using AWS SageMaker.
Have faced few interesting challenges like policy issues and instances availability etc

References:
https://aws.amazon.com/sagemaker/pricing/
https://console.aws.amazon.com/support/home

AWS IOT Greengrass ML inference

Right now am working one of the use case using AWS Greengrass.

AWS Greengrass is a service that allows you to take a lot of the capabilities provided by the AWS IoT service and run that at the edge closer to your devices. AWS Greengrass ensures your IoT devices can respond quickly to local events, use Lambda functions running on Greengrass Core to interact with local resources, operate with irregular connections, stay updated with over the air updates, and minimize the cost of transmitting IoT data to the cloud.

Deep Learning challenges at the Edge

Resource-constrained devices

CPU, memory, storage, power consumption.

Network connectivity

Latency, bandwidth, availability.

On-device prediction may be the only option.

Deployment

Updating code and models on a fleet of devices is not easy.

Value of ML inference at the Edge

·       Latency

·       Bandwidth

·       Availability

·       Privacy

https://aws.amazon.com/solutions/case-studies/iot/
https://aws.amazon.com/greengrass/faqs/

ONNX

With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them.
Enabling interoperability makes it possible to get great ideas into production faster.
Ex: PyTorch > ONNX > Tensorflow

Edge Computing

As a Automotive industry Machine Learning Engineer, today i thrilled about Edge Computing technology.
Edge computing to be bigger than cloud computing
Edge computing brings memory and computing power closer to the location where it is needed.
As part of my R&D have been deploying my tiny TF model on Android Apps and Embedded devices

More exciting to know Edge computing technology
https://www.sparkfun.com/products/15170

Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX

Am running some model on my brand new 32bit RAM laptop and faced the below error msg

Soulution: os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

Reference: https://stackoverflow.com/questions/47068709/your-cpu-supports-instructions-that-this-tensorflow-binary-was-not-compiled-to-u