How To Productionize ML Models?

Machine learning models are computer programs that learn from data. They are trained to find patterns and insights in data, and to make predictions about future events. Machine learning is a rapidly growing field of Artificial Intelligence (AI), and it is already having a major impact on businesses all over the world. Businesses are using machine learning to improve customer service, to personalize experiences, and to make better decisions. In the future, machine learning will become even more important, as businesses increasingly rely on AI to stay competitive.

Productionizing ML Models

There are several considerations to take into account when productionizing ML models. One is how the model will be deployed, which will likely involve some infrastructure considerations. Another is what the model inputs and outputs will be, as this will determine how the model interacts with other systems. Additionally, it is important to think about how the model will be monitored and updated over time. Here are some specific methods for productionizing ML models. 

  1. One popular method is to use a platform service, such as Google Cloud Platform or Amazon SageMaker. These services make it easy to deploy and manage models at scale.
  2. Another option is to use a containerization platform such as Docker or Kubernetes. This can be helpful if you need more control over your environment or want to run your models on-premises.
  3. Finally, you can also choose to roll your own solution, though this requires more engineering effort and is not recommended for most people.

Whichever method you choose, there are a few key things to keep in mind:

First, it is important to ensure that your data pipeline is set up correctly. This means making sure that data is properly cleaned and formatted before being fed into the model.

Second, you need to have a way to monitor the model’s performance over time. This can be done using a tool like TensorFlow Serving, which allows you to monitor model accuracy and identify when retraining is necessary.

Finally, it is also important to establish a process for updating the model as new data becomes available. This ensures that your models are always up-to-date and able to make accurate predictions.

Machine learning operations, or MLOps, is a term that refers to the process of taking machine learning models from development to production. This generally involves the following stages:

-Setting up a repeatable development process

-Dealing with model explainability

-Defining the model serving architecture

-Setting up model monitoring and verification

-Establishing a process for model updates.

Each of these stages is important in its own right, but they all need to work together in order for the process to be successful. By following these steps, you can ensure that your machine learning models are deployed correctly and perform well in production.  

Development and training flow

The training flow starts with fetching data from the company’s data warehouse and preprocessing them. The data warehouse is periodically populated by ETL jobs using the company’s operational database. The preprocessed data is loaded to a feature store where data scientists and analysts explore the data and forms experiments. After an experiment is deemed successful, a model is built on top of it, validated and registered in the ML model registry. When it’s time to deploy the model into production, an engineering team takes care of deploying the model serving infrastructure, connecting it to other services in the company (if necessary) and monitoring its performance in production.

Operationalizing machine learning models can be a challenge, but it’s important to get right. By following the steps above, you can ensure that your machine learning models are deployed correctly and perform well in production. 

How Qwak can help you with Machine Learning (ML)

Qwak is a machine learning platform that enables developers to train and deploy models quickly and easily. The platform provides a complete toolkit for building, training, and deploying machine learning models, as well as an extensive library of pre-trained models. Qwak also offers a number of features that make it ideal for productionizing machine learning models, such as model versioning, automatic scaling, and an easy-to-use Web interface. In addition, the platform provides full support for TensorFlow, Keras, and other popular machine learning frameworks. As a result, Qwak is an ideal platform for productionizing machine learning models.


In conclusion, machine learning models are a complex process that involves multiple strategies with uncertainties. The experimental nature of the model development and ever-changing data environment transcends into the deployment process and model operation in production. Following the steps above can help ensure that your machine learning models are deployed correctly and perform well in production. 

Thank you for reading! I hope this was helpful. If you have any questions or thoughts, please feel free to leave a comment below. Until next time!