Thinxtream IoT Services and Solutions Expertise Azure
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Thinxtream can rapidly enable
your Connected Products and Smart Services business
with Machine Learning

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


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

  • 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)


MACHINE LEARNING WHITE PAPER


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