Reusing machine learning features at scale, resulting in faster model building and deployment.​

Expected business impact​

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 services Cloud Storage

Google Cloud Storage

Google Cloud services Cloud functions

Cloud Functions

Google Cloud services Big Query



Kubernetes Engine

Google Cloud services Dataproc


Google Cloud services Vertex AI

Vertex AI



Roles to engage?​​

Buyer Job Roles:​ 


MLOps and AI Engineers


Data Scientists


Data Engineer/ Bigdata Engineer


Kubernetes Engineers

Fractal's value proposition

Touchless Automated machine learning model training and deployments.

Let’s connect to work better, together.

Our email: [email protected]