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PREDICTif Earns Accreditation as an Amazon Web Services Select Partner

PREDICTif Solutions, a Houston-based technology consulting firm, today announces its accreditation as a Select Partner by Amazon Web Services (AWS). This accreditation will further validate PREDICTif’s cloud offerings and capabilities.

PREDICTif has been an expert in business intelligence, data warehousing, big data, and machine learning for over a decade. With the backing of this expertise, the company formed a strategic alliance with AWS in early 2019 to match the increasing demand for cloud expertise from its current clients. In under six months, PREDICTif has launched half a dozen opportunities, received a dozen AWS business and technical certifications, and helped three customers start their cloud journeys on AWS.

“Achieving this accreditation enables PREDICTif to align and work more closely with the AWS teams and leverage both AWS’s technology and PREDICTif’s innovation to assist our customers in their digital transformation,” said Jeff Huang, CEO of PREDICTif.

Looking forward, PREDICTif will work toward becoming an Advanced-tier Amazon Web Services partner by Q4 of 2020, with a specialization in big data and machine learning.

“We are extremely excited about this partnership with AWS,” said Jeff Diaz, Practice Director of PREDICTif’s Cloud Practice. “We have been very successful in delivering innovative on-premises software solutions, and now we are well equipped to help our customers in the cloud.”

As the needs of PREDICTif’s customers continually evolve, the company will keep expanding its focus and offerings as a cloud leader in the areas of big data and machine learning to provide top-quality, tailored support. For more information, please visit http://www.predictifsolutions.com or follow PREDICTif on LinkedIn (#predictifsolutions) to stay up to speed with new developments.

About PREDICTif

Founded in 2007, PREDICTif is a business technology consulting and solutions firm with a unique focus on predictive business solutions. PREDICTif delivers innovative, advanced business analytics solutions to its customers, with over 350 (and counting) projects completed in data warehousing, business reporting and dashboard, big data, and machine learning. PREDICTif’s practices have gone beyond the walls of on-premises data centers, and the company has quickly become an expert in cloud computing. PREDICTif is a premier partner with Amazon Web Services (AWS), HPE/MapR, Oracle, and IBM. The company’s professional and certified consultants have extensive experience and deep expertise in AWS cloud offerings, the MapR big data stack, and Oracle’s and IBM’s BI solutions. PREDICTif helps companies complete the digital transformation of their businesses.

Contact

Jeff Huang, CEO
+1 713-457-7472
[email protected]

Case Study: AWS Machine Learning

NWS

The Challenge:

A Fortune 500 company specializing in the trade of electronic components is utilizing a custom-built CRM system to manage its trade processes. With tens of thousands of new requirements they receive every week, the buyers have little information regarding which requirements that they should work on, causing opportunity losses and potential revenue drain.

  • Many requirements are forgotten due to volume of requests or shortages in the market.
  • A solution to prioritize the requirements existed, but it didn’t account for many important factors such as number of active vendors, cross-product sales, current product trends, and quantity requested, rendering it unrealistic, inaccurate, hence often being ignored.
  • The client’s data science team had tried to improve the scoring solution and built out on-prem sandboxes, but the servers were limited in their ability to scale with more memory-intensive algorithms and data scientists were confined to using a small set of data.

PREDICTif Solutions was brought in to leverage machine learning to help improve the requirement scoring system, increasing gross profit. The project was dubbed “Requirements Prioritization v2”.

The Solution:

Working alongside the client’s data science and IT teams, our solution architects executed a 3-month plan to develop an improved requirement scoring model and integrate it with CRM to provide a more precise prioritization for the buyers. We recommended using a serverless AWS machine learning stack that includes SageMaker as well as API Gateway, Lambda and SNS for notifications. The below picture illustrates a high-level architecture design.

smith-aws

SageMaker is an all-in-one machine learning environment. With it, we were able to provide a sandbox to code, train, and test various models utilizing Jupyter Notebooks and then deploy trained models as API endpoints that CRM integrated with. SageMaker was selected to simplify the process of productionizing ML workloads. It provides a dockerized environment and powerful API’s to deploy trained models as a microservices architecture which can then be accessed via other AWS services. To reduce time, we also leveraged the hundreds of AWS-provided algorithms to develop our new requirement scoring algorithm.

AWS offers a very rich set of API services, which have made it very easy to integrate with CRM. Every service in AWS exposes an API. SageMaker has an intuitive UI exposed through the AWS Console, but it was the management API that really excited us. Without having to incorporate any third-party tools, we were able to start/stop, schedule, and promote training jobs in a continuous integration fashion to automatically roll out newer models. 

Challenges Addressed:

  • Model Retraining – while the process of building a new model is relatively simple, model retraining for comparing performance, approving changes, and testing with live data poses several challenges. We incorporated a SageMaker feature called production variants to simplify the process. A variant allows the user to deploy multiple models to the same endpoint and declare what percentage of the traffic will go to each model. This way, a new model can be tested until confidence is high enough to remove the old one. 
  • Parameter Tuning – One of the most time-consuming phases of developing ML projects is hyper-parameter tuning: the art of tweaking the configuration parameters that control model training. SageMaker hyper-parameter tuning jobs helped by allowing us to choose a performance metric to maximize. After each job was finished, 20 independent training jobs had been run, each using the output of the last to enrich and further optimize our performance metric. The SageMaker Console provided an easy-to-use comparison tool, enabling us to quickly identify the right hyper-parameter combinations. 
  • Cost Management – After determining the target algorithm would be an AWS provided XGBoost algorithm, the DS team wanted to try using 1-hot encoding, wherein every possible product was converted to a feature. This made the final feature set very wide (~700,000) columns, which was much better for CPU performance but needed more memory for each training iteration and endpoint call. A Cloudwatch event was created on a schedule to start/stop notebooks and endpoints during non-work hours, thereby cutting development and testing costs by 60%. 
  • Security – Since a fully-deployed ML cloud solution was brand new at this client, there was skepticism and concern around utilizing cloud, particularly regarding security. AWS shared responsibility model simplifies the project teams task list o API Keys were used for communication between on-prem and API gateway 
    • S3 transactions are all SSL encrypted by default, and we enabled encryption at rest in the S3 bucket. 
    • All information transferred between services within an AWS account is secure and monitored. 

The Results: 

PREDICTif Solutions has been innovating exciting solutions for our clients for over a decade now, so it was satisfying in using some of most cutting-edge technology that AWS offers to breath new intelligence into an older, deterministic CRM system for this client, that has resulted in an increase of profit margin by over 30%, after just the first phase of this project. 

  • Productivity Increase – the productivity of the data science team has increased by over 200% with the AWS machine learning technology. This project would have taken at least 6 months to deliver if we had done it using on-prem sandboxes 
  • Development Cost Reduction – A TCO analysis revealed a solution of this size would have cost 3x to host on prem and taken many months to procure. By using AWS, the DS team began building models from day 1. A notebook instance was created in minutes, rather than weeks of waiting for servers to be racked, or clusters to be configured. With SageMaker’s 1-click deploy methodology, architects could integrate developed models much faster because we weren’t waiting on DS team to finalize the feature set 
  • Solution Portability – The ML model exists in a docker container, which can easily be ported to another cloud provider or even on prem if the client wanted 

The client’s executive leadership wanted to pick something with a low up-front cost, and minimal impact to business process until the value could be proved to in front of a larger audience. We were able to deliver on this mandate and provide an impactful solution to move the business at the speed that the market demands. 

 

Case Study: Amazon Web Services for Smith

NWS

CHALLENGES

One of world’s leading semi-conductor brokerage and distribution companies, Smith & Associates experienced numerous challenges when it attempted to respond to the market faster and establish a customer-vendor e-market. Smith needed more agility from its IT infrastructure to optimize its operations and stay ahead of its competitors:

  • Establish an e-Commerce platform to enable its customers to send requirementsand receive quotes in a self-service and automated manner.
  • Automate the end-to-end fulfillment process to reduce time from order to delivery
  • Expand its Artificial Intelligence and Machine Learning (AI/ML) capabilities toreduce guess work by global sales team and propose new leads and upsell/cross-product sales opportunities
  • Enable its data science team to experiment and research on new models intrusted and secure environment –isolating work from the global user base.
  • Reduce the capital investment in IT infrastructure, direct focus on innovation andbusiness solutions

SOLUTION

  • Implemented an eCommerce platform by leveraging AWS services encompassingEC2, Lambda and RDS services, as well as Elastic Search and MONGO DB (forunstructured data)
  • Completed a new data warehouse with Redshift for sales and finance informationthat allows users to drill down to transaction level detail and resolve data integrityissues
  • Delivered AI/ML forecast models using Sagemakerand DynamoDB.
  • Ingested 35 years worth of historical data and the corresponding economic andindustry data and rendered a comprehensive analytics dashboard that showstrends and hot leads in real time
  • Utilized AWS’ DevOps services to streamline app development process andaccelerate project timeline, enhancing the quality of the delivered solutions

BENEFITS

  • Guaranteed Up-Time and 24×7 dedicated infrastructure support for global business
  • Improved Analytics capabilities enabling business to move at the speed of the market
  • Self-Service capabilities empowering customers and vendors and reducing order fulfilling time
  • Enhanced Agility enabling data scientists to deliver game changing forecast models faster

Case Study: Amazon Web Services for CISION

NWS

CHALLENGES

One of world’s largest public relations and media software/services firm, Cisionprovides product and services to global Fortune 500 companies. One of its most core offerings is to provide insight to its clientele based on the analysis of terabytes of media and textual content. However Cision’score content platform was built upon an outdated technology and its big data software MapR was many versions behind. Cisionwas in a critical need of upgrading its hosting infrastructure and the underlying big data analytics software:

  • Cision’scontent platform was built on obsolete hardware that was hosted on anon-premises data center, which required daily maintenance and care-feeding by alarge team of system specialists
  • Cision’sMapR software was 2 major versions and 10 minor versions behind thecurrently supported version and had been out of support for several years. Thisimposed significant risk to Cision’score business that relies on MapR for advancedanalytics and data processing
  • Cisionwas extremely short-handed to support even the existing platform, letalone be able to innovate new solutions for the business.

SOLUTION

  • Migrated the content platform and applications from an on-prem data center toAWS
  • Upgraded the MapR software to a supported version and deployed it on a multi-node AWS cluster, ensuring the performance of the platform meet business’expectation
  • Utilized Cloudformationtemplates for architecture-as-code deployments,meeting client’s requirements for maintainability and self-documentingarchitecture
  • Configured Cloudwatchand detailed monitoring to manage cost and justify spendsize
  • Setup Cloudtrailto provide audit logging consistent with industry standards
  • Adopted a proven Cloud migration methodology which enabled the project to becompleted on-time and on-budget

BENEFITS

  • Guaranteed Up-Time and 24×7 dedicated infrastructure support for global business
  • Increased Agility in deploying new solutions to support the business in real time
  • Reduced Support Resources empowering IT team to focus on innovation
  • Enhanced Robustness on a most updated hardware and software infrastructure

Cloud Readiness Assessment

NWS

What is stopping you from moving to the cloud? Do you fear your data will not be secure? Do you think you will lose control to a third party or be locked in with a vendor? Are you worried about performance and uptime for your critical applications? These are some of the common concerns that most companies grapple with when deciding if they are ready for the cloud. PREDICTif has created an assessment that will address these concerns.

Cloud computing offers a value proposition that is different from thatof traditional enterprise IT environments. By providing a way to exploit virtualization and aggregate computing resources, cloud can offer economies of scale that would otherwise be unavailable. With minimal upfront investment, cloud computing enables global reach of services and information through an elastic utility computing environment that supports on-demand scalability. Cloud computing can also offer pre-built solutions and services, backed by the skills necessary to run and maintain them, potentially lowering risk and removing the need for the organization to retain a group of scarce highly paid staff.

Cloud computing also presents added challenges to an organization’s processes and controls. PREDICTif blends technical expertise with business alignment experience for maximum effectiveness. We go beyond routine assessments by diving deep into your IT operations for a comprehensive view of your assets. We help you answer mostcritical questions surrounding cloud adoption tomake sensible, forward-looking decisions before you embark on this journey into the cloud.

pyramid

Assessment

Following an agreed upon agenda and utilizing a validatedquestionnaire, PREDICTif’s Cloud Readiness Assessment is a 2-4 weekengagement that consists of three steps: Discover, Analyze and Recommend. The engagement will be led by our Cloud subject matter experts who will perform an objective, vendor neutral SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis and assess your company’s overall readiness for transitioning to cloud-based services.

discover-analyze-recommend

Benefits

  • Understand how well your business is positioned for cloud adoption and where potential benefits reside
  • Identify and validate cloud use cases that will be beneficial for your business’ future
  • Provide IT executives empirical data to support cloud strategy investments
  • Mitigate risk by knowing what controls and processes support or impede your cloud computing goals
  • Establish a maturity baseline across business, process, and service controls to measure progress over time
  • Define high-priority organizational and/or operational gaps to cloud adoption
  • Provide an independent assessment based on best practice and experience
  • Identify security vulnerabilities and recommend mitigations

Why PREDICTif?

Leveraging industry best practices and proven methodologies, PREDICTif draws on its extensive experience and expertise in infrastructure and managed services to develop a comprehensive cloud strategy. Our assessment offers a rapid, flexible and logical approach across people, process and technology to help our customers understand and plan for the adoption of cloud services. Using our comprehensive IP from years of infrastructure, managed services and software implementation experience, we can provide your organization factual insights on your readiness for the cloud. Please reach out to schedule your assessment with one of PREDICTif’s industry leading cloud subject matter experts by visiting www.predictifsolutions.com.

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