Assessment & Discovery Workshops
Assess the current state of data and ML maturity to identify and build a business case and detailed roadmap for your envisioned AI adoption journey with Quantiphi’s ML expertise
PoC / Pilot
Identify use cases and develop AutoML Models along with demonstration of CI/CD pipelines. Evaluate the success metrics and get a detailed roadmap to scale to production
Vertex AI ML OPs Accelerator
A 5-week comprehensive engagement that includes a 360° assessment workshop to evaluate existing platforms and needs, followed by developing a working prototype using Machine Learning, Vertex AI and Google Cloud Platform
ML Data Harmonization
Build infrastructure for data ingestion pipelines and preprocessing required to make the data fit for the downstream activities, identify various data sources and integrate the cloud infrastructure with the existing databases
Accelerated ML Experimentation
Carry out rapid experimentation along with tracking & visualization. Build model training pipelines and perform feature engineering and model optimization
ML Orchestration for Production
Harmonize and identify the best fit model, develop production pipelines to deploy, scale and integrate the models into the organization’s workflows
Continuous Model Enhancement
Monitor the model performance and metrics with model monitoring frameworks, build retraining pipelines to improve model performance and achieve the required results.
Vertex AI Anywhere
Deploy trained models across a combination of on-prem and cloud environments
With Vertex AI, enterprises get multiple options to deploy trained models across a combination of on-prem and cloud environments without losing the ability to upgrade, scale, and monitor ML resources
AI thought leadership
Deployment of centralized team of SMEs to assist and consult on technical guidance, knowledge center designing and Organizational Change Management (OCM)
Conducting training and educational sessions and workshops, feasibility studies for POC/Pilots, and recommendations for best practices
Development and Managed Support
Dedicated data science experts and SMEs as an extended consultation and support talent pool to augment customers engineering teams
The customer is a leading US-based producer of computer memory and computer data storage including dynamic random-access memory, flash memory, and USB flash drives. The clients' data science team wanted to perform image-related analysis and desired Quantiphi's expertise in building the “Model Training Flow” section.
Following are the services developed for the customer:
Foundation and Scaling
Quantiphi developed a fully-automated MLOps pipeline in Google Cloud Platform (GCP) in two phases to perform analysis of over two million images per day, resulting in faster analysis and quicker defect detection in the images. Phase one of the engagement involved developing an end-to-end semi-automated orchestration pipeline. Phase two dealt with the complete automation of the pipelines that involved continuous evaluation pipelines and the development of CI/CD pipelines. Foreseeing the success of the earlier engagements, the customer wanted help in setting up an AI CoE for advisory on their ongoing projects. In addition, the client also wanted a bespoke training for their teams across the US and APAC region.
AI Center of Excellence
Quantiphi deployed a dedicated pool of SMEs to provide advisory support and technical guidance to the customers' team for the development of use cases along with consultative support and thought leadership aiding enterprise-wide AI adoption. The dedicated CoE team assisted the customer in the development and prioritization of the POC / pilot and production-grade use cases involving varying degrees of complexity to achieve maximum efficiency and ROI metrics meeting the customer's long-term business goals. The CoE team also conducted tailored workshops pertaining to the skill sets acquired and recommended best practices to streamline current and future workloads.
Training and Enablement Workshops
Quantiphi aims to conduct monthly and quarterly workshops for the customer focused on key learnings from fundamentals to advanced concepts and best practices featured from Phase 1 and 2 engagements of MLOps using Vertex AI. Additional training sessions will be covered based on the feedback received from the AI-CoE team for current and future workloads.
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 a businesses' online reputation.
The client wanted to evaluate their current data and ML landscape and outline an MLOps framework that will 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 MLOps pipeline for the shortlisted churn prediction use case.