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
Thinxtream has extensive expertise in Machine Learning for enabling real-time response, based on the instantaneous sensor data and predictive analysis of historical data generated from your Connected Products and Smart Services.
We have extensive expertise and experience with Amazon® and Azure® Machine Learning solutions.
AMAZON MACHINE LEARNING
As an AWS partner, Thinxtream uses Amazon Machine Learning to develop intelligent IoT solutions.
- 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
Thinxtream has had a long partnership with Microsoft® and uses Azure® Machine Learning, Microsoft’s integrated, end-to-end data science and advanced analytics solution, with extensive support for industry standard open-source libraries and toolkits to develop intelligent IoT solutions.
- 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
With Connected Products, your business is faced with an increasing demand to instantly respond to an anomaly, fraud, or a potential disaster.
Read our IoT Edge Analytics & IoT Machine Learning White Paper to understand how IoT Edge Analytics and Machine Learning enables real-time response, based on the instantaneous sensor data and predictive analysis of historical data.
Also read our Data Exploration, Analysis & Anomaly Detection for IoT White Paper to understand the application of Azure and AWS machine learning modules, and an out-of-the-box Azure service Time Series Insights to detect anomalies in sensor data, conduct root-cause analyses, and avoid costly downtime of Connected Devices.