Master 4 Key Aspects of Machine Learning Deployment in 5 Minutes

Master 4 Key Aspects of Machine Learning Deployment in 5 Minutes

It is not easy to deploy machine learning model to production. 

But with this guide, you will learn basic aspects you should know.

The question is, what should you know before you deploy machine learning model to production

There are 4 key aspects to MLOps or machine learning operations, which everyone should know first. These aspects help data scientists and engineers tackle limitations in the machine learning lifecycle and perceive them as growth opportunities in ML. 

Need for MLOps

Machine learning operations – MLOps are essential for multiple reasons. 

ML models depend on huge amounts of data, which becomes a huge challenge to keep track of it all. Plus, keeping track of varying parameters tweaked in ML models is equally difficult. 

Often, small changes can cause a very big difference in the results you harvest from the ML models. Thus, you must keep track of all the features that a particular model works with. Feature engineering is an essential part of the machine learning lifecycle and can hugely impact ML model accuracy. 

Once you deploy machine learning model to production, monitoring it is not quite like monitoring other software such as a web application. Moreover, debugging an ML model is also complicated.

Models use the real-world data for making predictions, and real-world data changes actively.

 With the ever-changing data, it is essential to track the model performance and, when it is required, update the model accordingly. This means you must keep track of new data changes and ensure the model learns from them. 

We will discuss 4 prime aspects one must know before you deploy machine learning model to production, and these are;

  1. MLOps Abilities
  2. Machine Learning Pipelines
  3. Open Source Integration 
  4. ML Lifecycle Platform 

MLOps Abilities

Plenty of different MLOps capabilities are to be considered before deployment to production.

The first is the creation of reproducible ML pipelines. ML pipelines let you define repeating and reusable data preparation, scoring, and training steps. The said steps must include the generation of a reusable software environment for deployment and training of ML models. In addition to registering, packaging and deploying models from anywhere. 

ML pipelines let you frequently update models or roll out the new ones alongside the other AL/ML services and applications.

Open Source Integration

Before you deploy machine learning model to production, you should know about source integration. 

There are 3 different open source technologies that are important. First comes the open source training framework; these are perfect for accelerating ML solutions. Next are the open source frameworks – for interpretable fair models. Lastly, there come the open source tools for model deployment. 

ML Pipelines

The third aspect you must know before deployment of ML models to production is how to build pipelines for ML solutions. The first task is data preparation, which encapsulates importing, validating, cleaning, and transforming data along with normalization of data. 

Furthermore, the pipeline has a training configuration, inclusive of parameters, logging, file paths, and reporting. 

Master 4 Key Aspects of Machine Learning Deployment in 5 Minutes

From here, there are actual validation and training jobs that need to be performed efficiently and repeatedly. Efficiency might be owed to specific data subsets, computing resources, different hardware, progress monitoring, and distributed processing. 

In the end comes the deployment step, which brings along versioning, provisioning, scaling, and access control. 

ML Lifecycle Platform 

The last tool one must consider before deploying the ML model to production is a platform that manages the end-to-end machine learning lifecycle. Such platforms have 3 main components that are essential to the ML lifecycle. 

These components include;


This ML platform component tracks experiments to record and compare the results and parameters. These runs can be recorded on a local file, to a compatible database, or remotely to any tracking server. This component also lets you group run experiments, which are useful for comparing runs intended to manage a particular task. 

ML Projects 

The next is ML Projects – this component packs machine learning code into a project in a reproducible form to share it with other data scientists or for transferring to the production environment. It also specifies a format for packaging data science code built on conventions. 


Models component for any ML platform manages and deploys models from varying ML libraries to different model serving and inference platforms. 

Model is a standardized format for packaging ML models – and can be used in many downstream tools. Every model is a directory containing arbitrary files, along with a model file in root that can define multiple flavors in which the ML model can be viewed. 

Final Thoughts 

The 4 aspects of MLOps capabilities, including open source integration, MLOps abilities, ML pipelines, and ML lifecycle platforms, can help you build a sustainable and streamlined-repeatable process to deploy a machine learning model to production. 

Not only that but a platform like Qwak offers data scientists the ability to easily and quickly experiment with varying models and frameworks, thus improving operational processes in production. 

How else could Qwak help you deploy machine learning model to production? Get in touch now!

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