ML is one of the most recent and advanced intellectual leaps of feat but is instead performed as a manual practice at most organizations. Without adopting MLOps, such organizations are bound to squander their resources on pipe dreams while failing to deliver anything of value. This blog explores how MLOps guarantees successful AI implementation, and the benefits it delivers in the process.

What is MLOps?

MLOps (Machine Learning Operations) is a framework bringing together guiding principles and best practices from Machine Learning, Data Engineering, DevOps to streamline and bring together the reliable and efficient deployment and production of ML models. An analogy to bring the picture of MLOps is to compare what it has done for ML as the similar role DevOps has brought to traditional software engineering. Both pipelines include a code-verify-deploy loop. But the MLOps is different as it also include data and model steps that are needed to build/train a machine learning model and training a model is a primary step as performance of model during training let us know how well it will work when it is finally released for the end-users.

ML Lifecycle

The ML model development lifecycle is an extension of the traditional SDLC and consists of four stages:
⦁ Scope
⦁ Acquire and Engineer Data
⦁ Develop and build the model
⦁ Deployment
To understand the necessity of MLOps, it is first necessary to understand these four stages of the ML Lifecycle.


This stage consists of understanding the need for ML and stating the business goal for the ML problem. This statement must be in terms of ambitious yet achievable KPIs.

Engineer Data

This stage consists of acquisition of data used by the model. This stage consists of gathering, analysis, validation, and preparation of data used to train and test the model. Proper validation of data by computing statistics is a key element in this stage.

Engineer Model

Designing and building the ML architecture is all that this stage is about. This includes unit-testing and validation of a separately held portion of data called the test data. It is important not to be satisfied by average performance of the model performance indicators and you should be testing the model for performance on the bleeding-edge cases of the business problem.


This stage consists of the integration of the model into the business app. Deployment of the business app consists of integrating the model, model packaging, model serving and performance monitoring. Its is key to monitor both the model performance and API performance.

MLOps for Machine Learning Implementation

Machine Learning was used until recently only for small proof-of-concept projects. But now, ML has become a mainstream technology and managers realize that to make the transition from POC to in-production ML, a new engineering culture is required to accelerate the process of churning out products with ML capabilities that work as promised. This is precisely what MLOps aims to achieve and brings about substantive benefits as outlined below.

Proof of Concepts become more valuable

A study by VentureBeat recently reported that only 13 percent of AI projects actually deliver value. This means that only 1/8 ML models actually make it into production. This means wasted resources and data scientists actually see that only a fraction of the output of their work sees the light of the day.
This happens because of a disconnect between the data science teams and the operations teams. Data scientists tend to focus on the model while ignoring other aspects such as the footprint of the model in the final product and the features actually matching with the input in the user interface. MLOps insures against this because it brings about the best features and practices from both ML and operations fields such as DevOps and Data engineering. It brings both the data science teams and operations teams into work as a single team.

Instills freshness into your team

MLOps best practices reframe the earlier objective of a model into a new objective of efficient work with pipelines. Pipelines streamline the flow of work and compartmentalize the whole process of building the solution into several components allowing work to progress in parallel. This allows for a fail-fast-learn-even-faster culture where even if unexpected setbacks take place, faster recovery into successful trajectory of work into the pipeline takes place.

Allows explainability and compliance by versioning

In the traditional software world, only the final app is versioned. But this does not work in ML as the model is composed of data, code, and the hyperparameters. In a scenario without MLOps, only the final model is versioned while the intermediate models in the iteration aren’t. This situation doesn’t lend itself in terms of explainability. Without proper versioning, reproducibility is not possible. Having a model that works well isn’t sufficient by itself. Without proper versioning, regulatory compliance becomes impossible to adhere to. MLOps ensures that this doesn’t happen as versioning is one of its guiding principles.

Guiding principles of MLOps

MLOps sets four guiding principles that must be followed to ensure successful implementation of ML projects.

ML must be collaborative

A few ideas on how MLOps must be collaborative:
⦁ There should be a train-on-commit policy implemented across your ML teams to discourage local running of model experiments. A few examples and tools to implement would be Sagemaker, MLOps pipelines and Azure MLOps pipelines. These tools among other such good ones allow you to set up and orchestrate entire MLOps pipelines right from design to deployment of final business solutions on AWS Cloud and Microsoft Azure.
⦁ Also apply some good best practices from traditional software development like creating good user interfaces that are reusable, use proper naming conventions, and generally data scientists tend to use procedural coding style which should be replaced by either object-oriented or modular programming depending upon the context.
⦁ Also encourage the team to properly document every step in the pipeline and model so that the design and functioning is clear to all the present and new team members who may join in the future.

ML must be reproducible

A few ideas about how to ensure that ML practice in your organization is reproducible:
⦁ The data pipelines and model pipelines must be versioned separately to ensure there is a clear distinction between the data entering a particular version of the model from the other ones.
⦁ Another suggestion to the team would be to document and keep track of the hypotheses posed by them in the analysis of data and building the model. Documenting the hypotheses will give their peers an idea of their thought process and in backtracking failed attempts.

ML must be continuous

⦁ As stated earlier data pipelines and model pipelines must be separated. But it does end there. They must be further decomposed into smaller and smaller decomposable chunks which can be automatically separately tested and developed.
⦁ ML models once built must be retrained and deployed in an automated manner using workflow management platforms like Azure Data Factory and AWS Glue.

ML must be monitored and tested

⦁ It may be that the hold-out test set is not representative of the real world situation. It is advisable to use A/B tests or canary tests to conduct research into the real data of the business case.
⦁ After deployment, it is a good practice and guideline of the MLOps playbook to have automated alarm systems that trigger the warning to conduct automated retraining of the ML models when drift in the data and model performance occur with time.


MLOps has become the holy grail to successful AI implementation by deriving faster value out of your ML projects. It may be painful to successfully implement MLOps practices in the first run but benefits start to appear after a few iterations of course corrections. Take charge and spearhead the MLOps practice in your organization!