The market for machine learning (ML) and artificial intelligence (AI) tools continues to become more saturated with various tools and platforms to address different parts of the pipeline. As far as building models goes, software products leveraged by data science teams generally fall into three categories:

  • Data Processing Platforms – Ensure that data is properly formatted, cleaned, and accurately labeled to generate the best possible models
  • Model Training Platforms – Accelerate the training, testing, and tuning of AI models
  • ModelOps Platforms – Deploy trained models into production environment and provide full lifecycle management, including ongoing management, monitoring, drift detection, and retraining

To adequately adopt AI capabilities, organizations will likely use a mix of tools from each of these three categories. Data scientists need a means to handle and process data, an easy and repeatable way to train algorithms, and an efficient process for deploying these algorithms into production systems. The variety of products from these categories in the market today makes choosing the right combination challenging.

Organizations often address the following questions:

  • Which products best suit our existing workflow?
  • What data platforms sync well with the databases we currently use?
  • What does our existing hardware and compute infrastructure look like?
  • Which products can provide feasible integration?

Interoperability

These challenges all rely on one key underlying idea: interoperability, or the ability for systems to work together seamlesslyMany platforms today are built for a specific set of hardware products, databases, and training-to-deployment pipeline products. In contrast, interoperable tools that can integrate easily with popular products give organizations flexibility to make these choices.

Modzy is an open architecture solution. It integrates with popular model training tools and frameworks as well as any front-end business application, providing the connective tissue needed to move AI models from development to a production environment.

One of the biggest challenges teams face is taking a model that was trained via one of the model training tools or frameworks and deploying it into a production system. In fact, roughly 50% of all trained models never make it into production according to industry research.

To increase the probability of models making it to the production system and deliver value, Modzy can integrate with the most popular model training tools and frameworks. These integrations allow data scientists to continue using their preferred training platform and tailor their existing pipelines in a way such that the end product is a Modzy-compatible containerized model. Modzy integrates with tools and frameworks illustrated below.

This integrations-first approach allows data scientists to continue working with their favorite training tools to build models, reduce the time it takes to deploy into production, and then have access to all the great management features Modzy offers— including monitoring and retraining models as needed. On average, Gartner research shows that it can take 9 months to deploy a model into production. Modzy reduces this to a matter of hours. Modzy also handles all the scaling and infrastructure management required once a model is in use in production.   

Automating Model Deployment with Modzy

Modzy integrations with training platforms typically follow this general process flow:

  • Train a model with any combination of tools, frameworks, or languages.
  • Send the trained model to their Modzy account.
  • Convert model to a Modzy compatible Docker container.

Deploy the model image and static metadata for the model to Modzy’s platform for use in production.

For example, consider a team of data scientists working in a research laboratory training a model that can identify cancerous tumors in MRI images. They might want to train an image classification model and log the accuracy on a validation dataset while testing different combinations of hyperparameters. Moreover, they may use AWS Sagemaker for model development, however they need a method for scaled deployment across hundreds of hospitals in the US.

With just a few lines of code or the click of a button, the Modzy integration can take the model weights developed with Sagemaker and prepare the model for scaled deployment in a matter of hours. Through the power of Modzy, this model and other AI capabilities can travel to the systems doctors use to review MRI images to better and more accurately detect cancerous tumors. 

Modzy Integrations: Automated Model Deployment Pipeline

Modzy allows organizations to configure the right AI pipeline to take a model from the lab to scaled deployment in production. No end-to-end solution today does it all. In fact, many cannot handle the intricacies in the last mile part of the pipeline with respect to deployment, management, monitoring, and governance. Modzy’s integrations with the most popular model training tools and business applications make it easy for organizations to deploy and manage AI models quicker than ever.

By choosing an open architecture tool like Modzy, you’ll be able to configure the right AI pipeline for your team and organization, making it easier than ever to add AI power to any business application.

The market for machine learning (ML) and artificial intelligence (AI) tools continues to become more saturated with various tools and platforms to address different parts of the pipeline. As far as building models goes, software products leveraged by data science teams generally fall into three categories:

  • Data Processing Platforms – Ensure that data is properly formatted, cleaned, and accurately labeled to generate the best possible models
  • Model Training Platforms – Accelerate the training, testing, and tuning of AI models
  • ModelOps Platforms – Deploy trained models into production environment and provide full lifecycle management, including ongoing management, monitoring, drift detection, and retraining

To adequately adopt AI capabilities, organizations will likely use a mix of tools from each of these three categories. Data scientists need a means to handle and process data, an easy and repeatable way to train algorithms, and an efficient process for deploying these algorithms into production systems. The variety of products from these categories in the market today makes choosing the right combination challenging.

Organizations often address the following questions:

  • Which products best suit our existing workflow?
  • What data platforms sync well with the databases we currently use?
  • What does our existing hardware and compute infrastructure look like?
  • Which products can provide feasible integration?

Interoperability

These challenges all rely on one key underlying idea: interoperability, or the ability for systems to work together seamlesslyMany platforms today are built for a specific set of hardware products, databases, and training-to-deployment pipeline products. In contrast, interoperable tools that can integrate easily with popular products give organizations flexibility to make these choices.

Modzy is an open architecture solution. It integrates with popular model training tools and frameworks as well as any front-end business application, providing the connective tissue needed to move AI models from development to a production environment.

One of the biggest challenges teams face is taking a model that was trained via one of the model training tools or frameworks and deploying it into a production system. In fact, roughly 50% of all trained models never make it into production according to industry research.

To increase the probability of models making it to the production system and deliver value, Modzy can integrate with the most popular model training tools and frameworks. These integrations allow data scientists to continue using their preferred training platform and tailor their existing pipelines in a way such that the end product is a Modzy-compatible containerized model. Modzy integrates with tools and frameworks illustrated below.

This integrations-first approach allows data scientists to continue working with their favorite training tools to build models, reduce the time it takes to deploy into production, and then have access to all the great management features Modzy offers— including monitoring and retraining models as needed. On average, Gartner research shows that it can take 9 months to deploy a model into production. Modzy reduces this to a matter of hours. Modzy also handles all the scaling and infrastructure management required once a model is in use in production.

Automating Model Deployment with Modzy

Modzy integrations with training platforms typically follow this general process flow:

  • Train a model with any combination of tools, frameworks, or languages.
  • Send the trained model to their Modzy account.
  • Convert model to a Modzy compatible Docker container.

Deploy the model image and static metadata for the model to Modzy’s platform for use in production.

For example, consider a team of data scientists working in a research laboratory training a model that can identify cancerous tumors in MRI images. They might want to train an image classification model and log the accuracy on a validation dataset while testing different combinations of hyperparameters. Moreover, they may use AWS Sagemaker for model development, however they need a method for scaled deployment across hundreds of hospitals in the US.

With just a few lines of code or the click of a button, the Modzy integration can take the model weights developed with Sagemaker and prepare the model for scaled deployment in a matter of hours. Through the power of Modzy, this model and other AI capabilities can travel to the systems doctors use to review MRI images to better and more accurately detect cancerous tumors. 

Modzy Integrations: Automated Model Deployment Pipeline

Modzy allows organizations to configure the right AI pipeline to take a model from the lab to scaled deployment in production. No end-to-end solution today does it all. In fact, many cannot handle the intricacies in the last mile part of the pipeline with respect to deployment, management, monitoring, and governance. Modzy’s integrations with the most popular model training tools and business applications make it easy for organizations to deploy and manage AI models quicker than ever.

By choosing an open architecture tool like Modzy, you’ll be able to configure the right AI pipeline for your team and organization, making it easier than ever to add AI power to any business application.