Dallas, 75204, TX,
United States
Pariveda Solutions
Pariveda Solutions, a prominent reseller, system integrator, and consulting company, that plays a vital role in numerous system integration and digital transformation initiatives. Pariveda Solutions collaboration with software players such as Amazon Web Services (AWS) empowers organizations to embrace disruptive technologies and accelerate their journey to the cloud, thus reshaping their business models.
| Reseller and SI | Vendor | Application | Category | Market |
|---|---|---|---|---|
| Pariveda Solutions | Amazon Web Services (AWS) | Amazon EC2 | Application Hosting and Computing Services | IaaS |
| Pariveda Solutions | Microsoft | Microsoft Azure Cloud Services | Application Hosting and Computing Services | IaaS |
| Pariveda Solutions | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | AI Development |
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Product | Category | When | Insight |
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Atlas Van Lines | Transportation | 800 | $140M | United States | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2016 |
In 2016 Atlas Van Lines deployed Amazon SageMaker to prototype and operationalize machine learning for proactive capacity and price management in long haul moving. The engagement positioned Amazon SageMaker inside the ML and Data Science Platforms category, establishing a cloud native training and experimentation environment to accelerate model development.
Pariveda Solutions prepared Atlas data sets, developed and evaluated the machine learning model, and tuned model performance through iterative training cycles. Amazon SageMaker was used to train and optimize the model, then the artifact was exported using SageMaker’s modular architecture to run inference on Amazon EC2, separating the training lifecycle from runtime hosting.
Operational scope focused on capacity planning and pricing business functions within long haul operations, with governance delivered through a joint Pariveda and AWS engagement that emphasized data preparation, model evaluation, and performance tuning. The implementation enabled Atlas Van Lines to unlock the possibility of proactive capacity and price management by embedding Amazon SageMaker powered models into production inference workflows on EC2.
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Black & Veatch | Construction and Real Estate | 12000 | $5.1B | United States | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2016 |
In 2016, Black & Veatch implemented Amazon SageMaker on AWS as an ML and Data Science Platforms deployment to automate key engineering design parameter decisions. Pariveda Solutions designed and implemented an MVP application that demonstrated machine learning could be embedded into the engineering design process to reduce cost by automating a targeted design decision feature.
The implementation used Amazon SageMaker to train models on historical performance at real-world plants, applying advanced feature modeling to divide operational runs into logical segments. The solution included automated data conditioning to detect and ignore segments impacted by manual human intervention, and a multi-part linear regression modeling approach to predict component maintenance rates from process input parameters, enabling automated optimization of selected design parameters.
Integration was focused on ingesting plant historical performance datasets and engineering process inputs for the targeted design steps, with the MVP applied to core specialties such as water, power, and telecommunications within Black & Veatchs engineering practice. Amazon SageMaker served as the centralized model training and inference platform, and model outputs were surfaced into the engineering decision workflow for the most important parts of the design process.
Governance and process changes emphasized model-driven decisioning in the design workflow, including procedures to identify and exclude human-intervened segments from training data and a repeatable training pipeline hosted on AWS. The engagement delivered a validated proof of concept and a cost reduction outcome by automating a key feature of the design process, while establishing a model training and deployment pattern for broader engineering applicability.
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Buyer Intent: Companies Evaluating Pariveda Solutions Services
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