Managed Azure Infrastructure Operations with Azure Monitoring

The client is a privately held company, specializing in the design and manufacturing of separation, material handling and inspection equipment used throughout process industries like food, plastics and chemicals, mining, aggregates, metalworking and recycling.

The unmanaged cloud expenditure was proving to be a costly affair for the client with their development and testing costs often exceeding the budget. The client was looking for a solution through which they could reduce their monthly spending on an Azure subscription. They were also looking to monitor monthly spending with granular monitors for all deployed resources. The client approached SNP to get complete visibility on usage and spending of each workload in their environments.

To help the client with their cloud cost controlling measures, SNP technologies came up with several solutions that helped the client. SNP started monitoring all resources and collected complete details like usage and spending on a monthly basis. To start off with, SNP prepared daily reports on spending details, by services and resources. SNP’s solution is not restricted to just monitoring spending, but we also provided a solution through optimizing. SNP calculated and provided the estimation to the client before deploying the resources.

To get the client production-ready, SNP leveraged multiple technologies like Cloud and Azure Cost Management & Billing service.

SNP’s cost management services helped the client overcome the budgeting challenges. The client was not just able to keep their Azure subscription charges at minimal, they could also better plan for resource allocation and keep costs under control. Some of the most notable changes experienced by the client are the following:

• By implementing the cost analysis by service/resource and time, the client has complete visibility over their resources.

• They are in a better position to control expenditure by finding where they need to restrict in order to reduce the billing.

• By costing by day report, the client has the knowledge on what charges are made at any time.

• SNP has provided them with the list of stale resources, helping them get rid of all the stale/unused resources in the subscription.

Managed Azure Data & Infrastructure Operations

The client is the industry leader in creating next-generation products for sports, recreation, protection, and personal use.

Due to the nature of their business, the client needed their website to be up and running 24*7*365 days. Their business required a website that could scale up the performance based on the inflowing traffic. They wanted a solution with built-in scalability, high availability, and load balancing to handle around 50k+ new user sessions on a regular basis.

The problem-solving process started with SNP undertaking a deep engagement with the client to define their business objectives and understand the performance challenges they faced with their previous website. SNP proposed designing the client’s new website on Drupal and hosting it on the Azure platform for overall enhanced performance.

To get the client production-ready, SNP leveraged technologies like Nagios, Drupal and Linux Web Services.

The client was able to curb its downtime considerably during the peak customer inflow hours and all the while the system would scale up the performance to match the traffic. The most significant changes experienced by the client are the following:

• There was a visible improvement in the performance and rendering of data.

• Significant cost reductions for infrastructure management.

•Scheduled server checks with the Center for Internet Security ensured that servers met industry best practices for implementation.

•The client observed 100,000+ user sessions with zero performance issues on a campaign day.

Demand Planning with SAP Analytics

The client is a proven market leader in manufacturing sports products for ski and winter with worldwide distribution of their product lines from equipment to apparel.

Despite having a global presence, the client was still functioning and dependent on Excel sheets for doing their inventory. This led to issues like multiple source files for data input and it took one whole day to build each PO. Excel did not support the automation of placements, over-under, sales forecasts, and master data change.

The client partnered with SNP to improve the greige and raw material planning and purchasing processes to reduce unwanted inventory, increase the quality of inventory, increase the quality of unsold inventory and reduce oversold positions.

SNP stepped up to migrate their existing Excel-based solution to a robust and dynamic Azure cloud platform. The results were achieved in two steps, where step one about understanding the existing processes to identify the leverage points and design a migration plan. The second step involved leveraging technologies like SAP, Azure Machine Learning, Power BI & Visual Planning, and Advanced Analytics to improve business performance, analysis, and planning.

With SNP’s help, the client reduced the leftover raw materials by 50%. There was an improvement in the supply chain forecasting and purchasing process. Data maintenance for planning business activities was reduced by 80%. They even eliminated the planning errors or delays resulting from data re-entry or data staleness.

Operationalizing Machine Learning Workflow with Microsoft Azure

The client deals with planning, design, and construction management of water and wastewater-related projects – from clean water treatment, storage, and distribution to wastewater and stormwater collection, treatment, and reuse.

The client had developed an algorithm to predict the speed of water streams on an hourly basis to plan resources in their water treatment plan. They faced issues with the algorithm that was sitting in a silo, preventing multiple data scientists/developers from working on it together and provided a challenge in creating multiple versions of their algorithm. Additionally, they were looking to capture model performance in development environment and to automate their machine learning workflow using Azure for orchestration of production data.

The client required a data model which would take care of model development, model tuning, model versioning, and model deployment. In addition to the above data platform the client wanted a scheduled job to run on an hourly basis to fetch data from Azure SQL database, pass the data as input to the deployed model, retrieve the predictions and load them in Azure SQL database.

To cater to client needs, SNP leveraged Azure Machine Learning Service for deploying client algorithm on to the cloud and Azure components to deliver a cost-effective and scalable data model platform and hourly job.

The client has started using the Data Factory pipeline (scheduled as an hourly job) to generate Power BI reports to cater to their BI needs and to plan resources in their water treatment plant by looking at the predictions coming from the model sitting on Azure.

Interested in transforming your business? We’d love to collaborate.

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