The development and adoption of new technological applications require careful planning and meticulous execution. In order to prove the feasibility of such new technology, service, or ideas to stakeholders, Proofs-of-Concepts (PoCs) are an important exercise in IT development. Simply put, it is an advanced, detail-oriented demo that reflects the effectiveness of the application when deployed against the use case conditions. This exercise greatly reduces the risk of failure later along the development cycle, helps solicit internal feedback, and makes it easier to get all the stakeholders on board.
However, POCs are time-consuming and effort-intensive, requiring a commitment of considerable resources and time. It is also important to find the right vendor, one that possesses a deep understanding of the concerned business processes and the technical prowess to plan, develop, and execute the project smoothly.
Accelerators are pre-packaged solutions created by vendors to facilitate the rapid implementation of POCs. By expediting the engagement process, accelerators can considerably reduce the time and cost typically associated with POCs, leading to much quicker development and deployment. They also allow for the customer to better understand the capabilities of the solution offering at an early stage, making it possible to identify new opportunities and scale the solution to incorporate downstream activities as well.
Being modular by design, accelerators feature reusable components from a production perspective. Accelerators come with the assurance that the AI model has already been built in the POC and has achieved model-related success metrics, greatly aiding the decision-making process. We discuss here the key application areas where our accelerators enable customers to adopt and scale digital technologies.
Visual Quality Inspection (VQI)
Accelerate Precision Inspection tasks with Visual Inspection AI
Visual Inspection AI (VI-AI) promotes purpose-built deep learning-based models that aid and automate high-precision inspection tasks for the manufacturing industry, reducing the cumbersome manual dependencies, costs, and time. VI-AI, when integrated with other systems, has the capability to identify faults and defects quickly and with high accuracy, bringing with it some significant business impacts like-
- Reduced Downtime: The system can inspect hundreds or thousands of parts per minute reliably and repeatedly, exceeding the inspection capabilities of humans
- Increased Manufacturing Output: VQI can enable error-detection early in the manufacturing processes, ensuring high quality before the item moves to the next step
- Cost Savings: In addition to reducing material waste, repair, and rework costs, VQI can be beneficial in minimizing manufacturing labor time and expenses
- Risk Mitigation: Accurate quality inspection leads to better product quality and customer satisfaction
- Maintaining Process Consistency: VQI systems follow a consistent and accurate procedure for inspection compared to manual inspection
Quantiphi’s data-powered VQI accelerator is an eight-week engagement that provides a brief assessment of the industry landscape and relevant pain points, with a customer-specific trained VI-AI model and a scalable solution architecture ready to go at the production level.
The accelerator includes two phases - Assessment and PoC. The assessment phase includes a thorough understanding of the existing systems, business needs, and technology infrastructure, enabling our engineers to scope out a detailed roadmap to implement a PoC.
The PoC phase includes developing a customer complaint trained VI-AI model and cloud architecture with inference endpoints and detailed technical documentation for reference purposes.
Predictive Maintenance
Drive Industry 4.0 transformation by leveraging Vertex AI for predictive maintenance capabilities and enhancing existing processes
The traditional ways of maintenance depend primarily on reactive measures, with barely any planning involved in the process. These methods can prove expensive when managing spares, inventory, and other maintenance resources. It takes immense manual effort to facilitate predictions and analysis based on data collected from machines. The absence of a window to avert any possible breakdowns means that conditions are less than ideal.
Predictive Maintenance is crucial and holds the potential to unlock huge value for the industry. It achieves this by facilitating the breaking down of data barriers between machines, sharing and analyzing insights, optimizing operations and maintenance.
Quantiphi’s Predictive Maintenance PoC package is a concise eight-week engagement that includes the development of an end-to-end automated ML-based anomaly detection accelerator that allows users to predict machine downtime at scale using Google’s Cloud AI suite.
Some key features of the accelerator include -
- A robust and scalable GCP solution architecture specifically designed for Predictive Maintenance needs, compliant with customer requirements
- Interactive monitoring dashboards in Looker for improved operational efficiency
- A robust and highly accurate ML Model to reduce downtime and mitigate equipment failure in the targeted facilities
Demand Forecasting
Leverage Vertex AI Forecast to accurately predict the demand and have better visibility over factors affecting it
Demand Forecasting provides an estimate of the number of goods and services that customers of a business will purchase in the foreseeable future. Critical business metrics like turnover, profit margins, capital expenditure, risk assessment, capacity planning, etc. are dependent on accurate demand forecasting.
Thus demand forecasting is the pivotal business process around which strategic and operational plans of a company are devised. The strategic and long-range plans of a business like budgeting, financial planning, sales and marketing plans, and capacity planning are formulated based on this forecast.
At present, demand forecasting solutions lack on various fronts such as
- The exclusion of macroeconomic variables makes it difficult to predict sales turning points
- A cognitive bias stemming from the demand planning numbers arrived at by traditional planning
- The ability to estimate the impact of the specific activity on demand due to the absence of scenario forecasting
- A simple descriptive dashboard prevents end-users from analyzing, diagnosing, setting alerts and control variables for the forecast
Quantiphi’s AI-driven solution accelerator helps enterprises forecast demands with high accuracy, leveraging Vertex Forecast while also taking into account ever-changing market patterns. The solution gives visibility into inadequacies of the supply chain and enables proactive measures for operations planning.
Some key features of the seven-week accelerator include -
- Discovery sessions and scope finalization
- Custom feature engineering for inclusion of external factors that influence the expected demand among seasonality, trends, etc.
- Forecasting model using Vertex AI forecast
- Scenario forecasting estimates the impact of marketing activities on demand and enables the designing of promotional campaigns for future planning
Vertex AI Accelerator
Discover ML Ops fundamentals and co-develop a working ML prototype
Research suggests that 72% of the organizations that began Al pilots before 2019 have not been able to deploy even a single application in production.
Data science teams in organizations execute complex technical experiments to build highly accurate models. However, translating these models to generate ROI is a costly and time-consuming process.
MLOps enables organizations to easily lay down the foundation for ROI by deploying, monitoring, and updating models in production. In addition to tackling issues in deployment and monitoring, MLOps also aims to achieve efficient ML lifecycle management and compliant model governance to avoid time-consuming audit processes.
Quantiphi’s ML Ops accelerator is a comprehensive, cost-effective five-week engagement that includes a 360° assessment workshop to evaluate organizations’ existing platforms and needs, followed by developing a working prototype using Machine Learning, Vertex AI and Google Cloud Platform.
The accelerator includes two phases - Assessment and Pilot. The assessment includes gaining a thorough understanding of the existing workflows, business needs, and technology infrastructure enabling our engineers to scope out a detailed roadmap to implement a pilot.
The pilot phase includes developing a customer complaint architecture and building end-to-end orchestration pipelines, along with a demonstration of monitoring and CI/CD frameworks.
Case Study - ML Ops Pipelines
The customer is a privately held technology company headquartered in Lehi, Utah that develops cloud-based software to help businesses modernize customer interactions, such as customer feedback, to improve the online reputation of the business. The client wanted to evaluate their current data and ML landscape and outline an MLOps framework that would help them define core processes and technical capabilities to establish mature ML Ops practices.
Quantiphi assessed the current state of data architecture, data science maturity, and business priorities to develop customer-curated architecture, along with deployment and demonstration of the MLOps pipeline for the shortlisted churn prediction use case.
Get in touch with our experts to unlock business value with our accelerators.
