Machine Learning and Deep Learning for Building Intelligent Platforms Binu Aiyappan, Wipro Technologies. #ESCconf

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1 Machine Learning and Deep Learning for Building Intelligent Platforms Binu Aiyappan, Wipro Technologies

2 If you were not noticing

3 If you were not noticing Let s try this in another way, say credit for credit card payment. "If you just said something, I didn't hear what it was"

4 If you were not noticing

5 If you were not noticing

6 What is driving these changes? These are examples of application of Machine learning, Deep learning or AI to deliver innovative products and customer experience Different classes of use cases including: Image and video analytics Speech and audio Text Mining and Natural Language Processing Anomaly detection and prediction IoT For Different Domains healthcare, retail, manufacturing, banking and financial, social etc.

7 What are the catalysts for the change? Big Data Technology and cost of storage allow variety of data to be stored in full fidelity Moore s Law Cost of computation well past the tipping point enabling accelerated innovation. Especially in AI, GPGPU is currently playing an important role Open Source and Eco-system technologies Machine Learning at Scale DNN Libraries available in various languages and platforms Cloud - Computation becoming more like a utility service Source: Ray Kruzweil: The Singularity Is Near

8 Modelling the Decision Engine Input Hand Designed, Rule-based Program Output Traditional Rule based Systems Input Hand designed Features Machine Generated Mapping from Features Output Machine Learning Systems Input Machine Generated Features Machine Generated Mapping from Features Output Deep Learning Systems Input Machine Generated Features Intermediate Layers of Features Machine Generated Mapping from Features Output Reference: Ian Goodfellow, Yoshua Bengio and Aaron Courville

9 What does it mean for us? Move from Rule based systems To Cognitive learning systems Deterministic Query based Fixed 2 Dimensional Schema Static and Predictable behavior To To To To Probabilistic Search based with natural language interface Represent entities with real world relationships and linked data Reasoning capabilities change with continuous training

10 Machine Learning in brief Major Classes of ML Regression For continuous values Clustering Grouping similar items Classification Identify if an object belongs to a predefined class Ranking/Scoring Arrange a list of items Recommendation engine Anomaly Detection Fraud, outlier, deviations from normal The Class of ML depends on availability of training data, volume of data set

11 Deep Learning Overview Multi-layers of hierarchical feature extraction and learning with hidden layers Major Classes of DL Convolutional Neural Networks (CNN) : Recognize patterns/object and learn to predict higher level features e.g. image analytics Recurrent Neural Networks (RNN): Suitable for problems that have temporal behavior e.g. NLP and Speech recognition Areas of application Computer Vision Image classification Natural Language Processing Speech recognition Question Answer system IoT and Control Systems What is needed Big Data Training data set Scalable library Tensorflow, Caffe, Torch GPGPUs

12 Survey of the landscape

13 Case Study: Automated Powerline detection using Drones Power Line Invasion (encroachment) definition High distance severity: vegetation (i.e., 3D object) in prohibited area within 2 meters of a wire requires cutting now Moderate distance severity: vegetation (i.e., 3D object) close to prohibited area within 3 meters of a wire may require cutting soon Low distance severity: detected 3D object(s) within 3-6 meters from power line Undetected severity: detected 3D object(s) none within 6 meters from power line Constraints for using Drones Maximize use of CTOL over VTOL No fly Zones Slope, Turning Radius, Battery life

14 Case Study: Automated Powerline detection using Drones

15 Drone Case Study: Flight Plan Network Elevation Data Drones Constraints Flight Plan

16 Services / Applications Case Study: Medical Devices and Implants Physicians/ Health care providers Relatives

17 Case Study: Reference Architecture for an IoT Solution Key Characteristics Edge Analytics Scalable Compute Protocol Abstraction Secure boot Whitelisting Device Registration Device Discovery Device Provisioning Edge Mgmt

18 Other Examples Connected Drones Automate the inspection and monitoring of power grid infrastructure Precision agriculture in crop research and agronomic development. Oil and gas inspection. Network Elements Billing records Anomaly detection Fraud Detection using Deep Learning in areas like payment transactions. Health care Medical imaging for better patient care e. g. Radiology assist Real World Evidence Real time event detection from medical devices Performance Intelligence for Data-Ops AI Ops platform to automate the process of finding performance, security issues in operations for Hadoop, Spark, NoSQL, Cassandra. Speech to text conversion Speech recognition for command and control system. Speech to text conversion for audio surveillance Auto transcript generation Video Intelligence Video activity detection and monitoring for security Intelligent video surveillance Cyber Security/Fraud Using user behavior biometrics solution for security. Spot anomalies in large data sets like payment transactions. Rouge Behaviour Detection. Genomics Genomic data processing and analysis in real-time Find patterns that shed light on disease correlation, epidemiology, Pharmacovigilance Use NLP, ML and rules to automate drug safety analysis. Recurrent neural network architectures for labeling adverse drug reactions

19 Where and on What to build the Solution?

20 GPGPUs the catalyst Leverage GPGPU based solution E.g. Deep Learning Supercomputer in a Box to train the models e.g. NVIDIA DGX-1 provides Dual 20 core host CUDA cores GPGPU based on-board computers with SDK and ready solution for cameras, LIDAR and other sensors along with the trained DNNs Integrated solution with on-board computation, augmented computation on cloud (e.g. for maps, traffic conditions etc.) GPUs on Mobile devices

21 Where to build the Solutions? Datacenter, AWS, Azure, Google and Others Big Data Hadoop on Cloud Persistent and Transient Cluster based on the Usage Pattern Asymmetric Architecture with ability to scale compute independent of storage Cloud Native Services (EMR, HDInsights, DataProc) or Hadoop on IaaS End to end Data Pipelines Real time, Batch and Lamba Architectures Data Pipeline orchestration solutions (AWS Datapipeline, Azure Data Factory, Google Dataflow) Solution with Polyglot Storage Architecture Combination of Hadoop, MPP, No-SQL, RDBMS, Object Store

22 Where and on what to build the Solutions? Public Cloud Based Options Cloud Native Solutions for Machine Learning and Deep Learning Amazon ML, Azure ML, Google Cloud Labs and Machine Learning GPGPU instances IoT offerings from all cloud provides including SDK for device side Trained models as Libraries

23 What next? Look for Exotic solutions becoming mainstream FPGAs and ASIC customized for Machine Learning and Deep Learning tasks Bio inspired technologies and algorithms Neuromorphic chips Genetic Algorithms Trained models gets licensed, data will be the currency, most of the libraries and technologies are already open-sourced Probabilistic nature and non-rule based solution, leads to potential grey areas and legal issues

24 Speaker/Author Details Website: ID: Linkedin: Twitter: Image generated using

25 Thank You!