Reusing machine learning features at scale, resulting in faster model building and deployment.
Expected business impact
- E2E automated training of machine learning models and monitoring framework to access performance of the model in Google Cloud.
- Ability to analyze over 1 petabyte of data and give diverse analytical solutions to multiple stakeholders on time.
Customer challenges
Once the client’s business problem has been identified and the machine learning model is developed, deploying that model in production involves various steps.
On top of that, Machine Learning models can decay due to various reasons, requiring the Data Scientist to repeat the model development process again.
So, all the steps involved must be automated, starting with data extraction, feature engineering, model training, model serving, model monitoring, and model scaling to make it easy for data scientists to deploy new versions of the model.
Features to mitigate business challenges
Automated machine learning model training and deployment
Created end-to-end model onboarding strategy (POC to operations) for client using vertex AI
Success stories
The team has created an automated framework for a leading M&E organization using Google Cloud Vertex AI, ultimately speeding up the process of model delivery.
It helped the Data Scientists to configure Data Sources, ML tools, and ML models and helped configure the hyperparameters of the ML model.
Once the data scientist has chosen the appropriate model, the framework makes it easy for deployment and maintenance in production.
Google Cloud services used
Google Cloud Storage
Cloud Functions
BigQuery
Kubernetes Engine
DataProc
Vertex AI
TensorFlow
Roles to engage?
Buyer Job Roles:
MLOps and AI Engineers
Data Scientists
Data Engineer/ Bigdata Engineer
Kubernetes Engineers
Fractal's value proposition
