BUILD IOT INTELLIGENCE WITH OUR IN-DEPTH EXPERTISE IN
AWS MACHINE LEARNING AND AZURE MACHINE LEARNING


    Amazon Machine Learning

    Azure Machine Learning




AMAZON MACHINE LEARNING EXPERTISE

  • 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
  • Developing industry-standard machine learning models for binary classification, multiclass classification, regression, clustering and anomaly detection
  • Evaluating models using metrics such as AUC, Accuracy, Macro-average F1 score, Squared error (MSE/RMSE), Squared Distance (MSD/SSD) and Cross Validation techniques
  • Evaluating models using performance visualization such as training and validation graphs, confusion matrix, histogram of residuals, ROC curve
  • Using Inference Pipelines and Batch Transform to make either real-time or batch predictions with data transformations
  • 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
  • Deploying machine learning models in a wide variety of environments, on the cloud or at the edge, with services such as SageMaker Hosting Services, SageMaker Neo, AWS Greengrass® IoT
  • Monitoring Amazon Machine Learning with Amazon CloudWatch® and AWS CloudTrail®




AZURE MACHINE LEARNING EXPERTISE

  • Support for data ingestion from various Azure/Non-Azure data storage services
  • Advanced data preparation techniques like Filtering, Normalization, Principal Component Analysis, Partitioning and Sampling, etc.
  • Extend Azure Machine Learning model with R and Python™ Script modules
  • Making predictions with Elastic APIs like Request Response and Batch Execution Service
  • Model Visualizations with Scatterplots, Bar Charts, Box plots, Histograms, REPL with Jupyter™ Notebook
  • Retraining model, Cross validation and Parameter Sweeping
  • Support for 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
  • Manage entire data science life cycle with cross-platform Desktop application - Azure Machine Learning Workbench
  • Deploy Azure Machine Learning models into wide variety of environments like local/on-prem devices, Docker images, IoT Edge devices, Azure Container Services (ACS)


IOT MACHINE LEARNING WHITEPAPER