The Secret to Scalable AI: Understanding and Implementing MLOps

Written by Subaandh VK ML/Data Engineer at DiliTrust 

Artificial Intelligence adoption grows steadily in enterprises, understanding and implementing Machine Learning Operations (MLOps) is becoming essential. A recent survey shows that 35% of companies are already using AI, with 42% experimenting with it. To scale AI effectively, maintaining and monitoring machine learning models through MLOps is crucial. 

An image of a robot looking at charts and graphics, to represent AI and more specifically MLOps

Understanding MLOps: What It Is and Why It Matters

Machine Learning Operations (MLOps) is a set of processes that aim to track, deploy, and monitor Machine Learning models in production. By integrating these processes, MLOps helps organizations achieve scalable AI solutions, ensuring that models remaining reliable and perform well over time.

The core principles of MLOps include:

  • Experiment Tracking: Documenting experiments to reproduce and understand results, which is essential for improving models iteratively. 
  • Monitoring: Keeping an eye on model performance and data quality to detect and address issues promptly. 
  • Versioning: Managing different versions of models, datasets, and code to ensure reproducibility and maintain a clear development history. 
  • Automation: Streamlining repetitive tasks, such as model training and deployment, to improve efficiency and reduce the potential for human error. 
  • Reproducibility: Ensuring that experiments and models can be consistently reproduced, fostering collaboration and transparency among team members. 

Here are key reasons why MLOps is essential for scalable AI in businesses:

  • Scalability: MLOps enables the seamless scaling of AI projects, allowing businesses to handle increased data volumes and more complex models without compromising performance. 
  • Efficiency: By automating routine tasks, MLOps frees up data scientists and engineers to focus on innovation and improvement, rather than maintenance. 
  • Reliability: Continuous monitoring and versioning ensure that models remain accurate and relevant, even as data evolves over time. 
  • Collaboration: Standardized processes and documentation facilitate better collaboration among diverse teams, including data scientists, engineers, and business stakeholders. 

Implementing MLOps practices is essential for any enterprise aiming to harness the full potential of AI. By understanding and adopting MLOps, businesses can ensure their machine learning initiatives are efficient, scalable, and aligned with their strategic goals.

In this article, we will explore the challenges that we faced at DiliTrust and how they led us to adopt MLOps as a practice, highlighting its importance for achieving scalable AI success in enterprises.

Implementing MLOps: Key Benefits for Scalable AI

Machine Learning models in production need to go through a cycle involving steps like data preparation, model training, testing, deployment, and monitoring. 

In our Machine learning team, there are lots of people: Research Scientists, Data Scientists, Data Engineers, and Data Analysts. This requires constant collaboration between different stakeholders and much more to produce better results. Thus, to deploy a model it is necessary to standardize the process and automate most of the steps. 

Machine Learning development is an iterative process with lots of research. So, standardizing this process using MLOps is significant. In contrast, Software Development has standardized the use of DevOps to run, monitor, and improve the quality of SaaS products. 

The same holds true for companies developing AI solutions that should progress towards MLOps adoption to improve Machine Learning models. MLOps enables seamless integration of the above processes aiming to continuously improve the ML cycle. 

6 Reasons to adopt MLOps 

1. Data Preparation 

This is the first step to training any Machine Learning Model. The data on which the model is trained can have a significant impact on the model’s performance. 

“Data is the new oil. Like oil, data is valuable, but if unrefined, it cannot really be used.” — Clive Humby 

This holds true because Data Preparation involves a many steps like: Data Ingestion, Data Cleaning, Data Transformation, and Data Analysis. Automating these steps makes things easier. 

Data plays a major role in the way a model behaves in production. So, automating the data pipeline is our primary goal. Our data pipelines process thousands of customer documents every day which helps us maintain a clean training database. 

➡️  This could also interest you: How does DiliTrust Educate its Artificial Intelligence? 

2. Model Training 

Model training seems like a straightforward process when there is a single pipeline performing multiple tasks. DiliTrust has Machine Learning models tailored to fit various business needs. For instance, the Contract Lifecycle Management (CLM) module extracts different types of information (clauses, entities, document type, etc.) from each contract. This results in multiple pipelines, performing multiple tasks, which adds to the complexity of the system. So, tracking the parameters used for model training is indispensable. 

Some notable parameters include the model configuration (epochs, model type, pipeline) as well as the code and dataset versions. 

3. Model Testing 

Developing a model is an iterative process and Data Scientists can spend a lot of time testing it. Reproducibility and interpretation of results would be a great chaos. Tracking the model configuration, pipeline, and results would take a big chunk of the Data Scientist’s productive time. Thankfully, MLOps solves this as we centralize the results of each experiment run, providing better accessibility. 

4. Deployment 

Deployment is a process where the ML model trained on production data is served using API to users. Automating model deployment is key as manual deployments are prone to errors. This version of the models, hyper-parameters, train, and test data version, and ML artifacts. 

CI/CD pipeline automation ensures reliable deployment. It launches the automated model training pipeline and runs some tests before deploying the ML Model and related artifacts. 

The deployed model might not deliver the expected performance every time. So, implementing a rollback mechanism is as important as having an automated deployment. 

5. Monitoring 

Production models are at risk of degradation because of changing data. Experimental and production model monitoring is crucial to achieving continuous improvement. Every week the Machine Learning team trains different models which are then deployed to production. So, keeping track of each model is important. 

Production models are associated with multiple business metrics which evolve over time. Monitoring the changing metrics and their performance helps meet customer expectations. 

6. Scalability 

In the early stages only a limited number of people worked on a model at once but as the team grew the collaboration between different stakeholders became complex. 

Besides, as discussed earlier, model tracking and benchmarking across different teams need centralized storage. 

️  Read also: AI Ethics & Regulation: Insights from DiliTrust’s Head of Legal & DPO 

MLOps Metrics 

As a bonus, here are a few metrics that are relevant when doing MLOps. 

  • Deployment Frequency
    MLOps Automates the data pipelines and replaces the manual deployment with CI/CD pipelines. This should reduce the time taken to deploy a new model in production. 
  • Model Training Duration 
    ML model training includes data preparation, running experiments, and inferring results. With the experiment tracking and reproducibility in place, Data Scientists spend less time in model training as most manual steps are eliminated. 
  • Rollback Time 
    Monitoring the ML model performance helps to detect possible performance degradation. In that case, versioning provides the flexibility to roll back to the previous version. 
  • Model Quality 
    Model quality can be measured by comparing the metrics between the current production model and newly trained models. This ensures there is no model drift introduced by the new version. 


Machine learning aids businesses to develop solutions to problems that were once impossible; thereby saving time, and improving efficiency by leveraging the data for decision-making, to improve customer experience. So, having MLOps is essential as faster model deployment helps enterprises stay ahead of the competition in the growing market. 

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