DIGITAL TRANSFORMATION – CHALLENGES IN MACHINE LEARNING

  • Failure prevention for a wide variety of devices, and systems by predicting failure possibilities early
  • Anomaly detection in streaming data from devices, or other data sources like document digitization systems for quality control
  • Quick and accurate analysis based on images, and historical data
  • Fraud detection in financial transactions, especially credit cards, to quickly detect frauds and take actions, or even proactively prevent them
  • Sentiment analysis on social media posts, news feeds, and other forms of text feeds


Data Requirements

Computing infrastructure

Talent Availability


Technology Maturity

Problem Selection





PRODUCT ENGINEERING SERVICES FOR MACHINE LEARNING

digital transformation, product engineering services, mobility, web applications, enterprise integration, machine learning, big data analytics, Web UX, digital payments, digital interfaces, cloudification, social media integration
  • Data cleaning and pre-processing to remove outliers, managing missing data cells, slicing and dicing and data splitting for training and testing
  • Building machine learning models using various regression techniques and deep learning techniques for various business scenarios
  • Publishing machine learning APIs for inference in various business applications in a continuous learning loop
  • Deploying machine learning models on edge devices like control systems and imaging systems, and integrating with cloud services and on-premises systems
  • Building chat bots with customized back end NLP engines for specific businesses
  • Classifying images for applications such as medical diagnostics and document management
  • Building predictive analytics models for device control


Amazon Machine Learning

  • Interfacing with a wide variety of data sources such as Amazon S3® buckets, Amazon DynamoDB®, Amazon Redshift®, and Amazon RDS® - MySQL
  • Interfacing with AWS Data Pipeline and AWS Glue™ for implementing cleaning, filtering, aggregating, transforming, and enriching data sources
  • Applying industry-standard machine learning models – binary classification, multiclass classification, and regression
  • Evaluating models using metrics such as AUC, macro-average F1 score, root mean square error (RMSE) metric, cross-validation
  • Evaluating models using performance visualization such as histograms of the score of actual positive/negative, confusion matrix, a histogram of residuals
  • Making batch-based and one-at-a-time predictions
  • Using Amazon SageMaker to build, train, tune and deploy machine learning models
  • Consuming API driven services such as Vision, Conversational, and Language services
  • Using Amazon Deep Learning AMIs with Apache MXNet™, TensorFlow™, PyTorch™, the Microsoft Cognitive Toolkit (CNTK), Caffe, Caffe2, Theano, Torch, Gluon, and Keras to train sophisticated, custom AI models
  • Using analytic services such as Amazon Athena®, EMR, Amazon Redshift®, Redshift Spectrum in conjunction with Amazon Machine Learning
  • Deploy machine learning models in a wide variety of environments like local/on-premise devices, Docker™ images, AWS Greengrass® IoT edge device
  • Monitoring Amazon Machine Learning with Amazon CloudWatch® and AWS CloudTrail®

Azure Machine Learning

  • Supporting data ingestion from various Azure/Non-Azure data storage services
  • Advanced data preparation techniques like Filtering, Normalization, Principal Component Analysis, Partitioning and Sampling, etc.
  • Extending Azure Machine Learning model with R and Python™ Script modules
  • Making predictions with Elastic APIs like Request Response and Batch Execution Service
  • Modeling visualizations with Scatterplots, Bar Charts, Box plots, Histograms, REPL with Jupyter™ Notebook
  • Retraining model, Cross validation and Parameter Sweeping
  • Supporting wide range of data formats - ARFF, CSV, SVMLight, TSV, Excel®, ZIP
  • Integrating open source technologies like Scikit-learn, TensorFlow, Microsoft Cognitive Toolkit (CNTK), Spark ML
  • Industry standard regression algorithms for training models, including Linear Regression, Deep Neural Networks, Decision Forest, Fast Forest Quantile, Ordinal Regression and Poisson Regression
  • Managing entire data science life cycle with cross-platform Desktop application - Azure Machine Learning Workbench
  • Deploying Azure Machine Learning models into wide variety of environments like local/on-prem devices, Docker images, IoT Edge devices, Azure Container Services (ACS)