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Model Deployment Service


Sofstack solving your complex problem such as integrate a machine learning model and integrate it into an existing production environment.

Leading-edge Model Deployment Solutions

The method of taking a trained ML model and making its predictions available to users or other systems is understood as deployment. Deployment is entirely distinct from routine machine learning tasks like feature engineering, model selection, or model evaluation.

Top Machine Learning platforms we use

Amazon Web Services. Highly scalable, complete cloud platform. Microsoft Azure. IaaS and PaaS computing for development, deployment, and management. Google Cloud Platform. Developer products and cloud technologies hosted by Google.

Machine Learning on AWS


Amazon Machine Learning (Amazon ML) is a robust, cloud-based service that makes it easy for developers of all skill levels to use machine learning technology. Amazon ML provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.

Prediction capacities of Amazon ML are limited to three options: 

  • binary classification,

  • multiclass classification,

  • and regression.

Machine Learning on Azure

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Azure Machine Learning can be used for any kind of machine learning, from classical ml to deep learning, supervised, and unsupervised learning. You can build, train, and track machine learning and deep-learning models in an Azure Machine Learning Workspace.

Features of azure machine learning studios

  • Accelerate the end-to-end machine learning lifecycle

  • Boost productivity with machine learning for all skills

  • Operationalise at scale with MLOps

  • Build responsible machine learning solutions

  • Innovate on an open and flexible platform

Amazon SageMaker


Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.

Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity by up to 10x. SageMaker Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place, making you much more productive.

Machine Learning on Google Cloud


Machine Learning and Cloud Computing help business intelligence companies by manipulating real-time data, analyzing it, and making future predictions. It enables you to create an interactive dashboard that displays data from different dimensions in one place.

Google Cloud protects your data, applications, infrastructure, and customers from fraudulent activity, spam, and abuse with the same infrastructure and security services Google uses. Google Cloud's networking, data storage, and compute services provide data encryption at rest, in transit, and in use.

Hire for any scope
of work:

Short-term tasks

Build a pool of diverse experts for one-off tasks

Recurring projects

Have a go-to team with specialized skills

Full-time contract work

Expand your staff with a dedicated team

How it Works?

Collecting unlabeled training data

Analyzing Data

Opting and Training data

Cataloging Data

Why Sofstack?

3+ years of


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Projects Delivered

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Happy Customers

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