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Deploying ML Model for Real-Time Prediction
Customer
The client is a multi-cloud data partner who provides Data management, BI analytics, and ML service, covering solution architect design, implementation, deployment, testing, and production support.
Detailed information about the client cannot be disclosed under the provisions of the NDA.
Role
</> Senior Consultant
Industry
Data Infrastructure and Analytics
Challenge
The company wants to deploy an ML model that requires low latency and interactive model model predictions
Solution
I leveraged Databricks to create and train an ML model and deploy it through Kubernetes. This initiative yielded a 35% enhancement in model training efficiency and facilitated the deployment of ML models in production environments.
Core pipeline components:
>Azure Databricks – an easy and collaborative Apache Spark-based big data analytics service for data science and engineering.
>Azure Kubernetes Service (AKS) – simplified deployment and management of Kubernetes by offloading the operational overhead to Azure.
>Azure DevOps – solutions for implementing DevOps practices to enforce automation and compliance with your workload development and deployment pipelines.
>MLFlow – open-source solution integrated within Databricks for managing the end-to-end machine learning lifecycle.
>Azure Data Lake Gen 2 – scalable solution optimized for storing massive amounts of unstructured data.
Develop and manage a machine learning (ML) model on Azure Databricks and deploy it through Kubernetes for low latency and interactive model predictions
