with IoT Machine Learning

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

Machine learning accelerates your IoT vision for your connected products and smart services, and enhances RoI by greatly improving operational efficiencies and customer experiences in your business. It can analyze much more data than is physically possible, and provide more, faster and better conclusions, which can help digitize and streamline business processes for rapid decision making and execution without human intervention. Machine learning complements big data, which can potentially be used to train, test and retrain machine learning models.

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 EXPERTISE

As an AWS Technology 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
• 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

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)