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MLOps Architecture Design

Back in 2020, Atlassian conducted a survey where it was revealed that 69% of respondents said that DevOps had a positive impact on their organisation. Even the rest, 30%, believed that it had ‘somewhat’ of a positive impact. You and we both know today that DevOps does help companies achieve faster lead times, higher quality of work, improved deployment frequency and obviously, improved work culture.

 

The question is since Machine Learning has similar features as that of software development, can’t we mirror the success of DevOps in our Machine Learning endeavours? MLOps answers that question with a resounding yes!

MLOps: Going Beyond the Mumbo Jumbo

The introduction of any new technology gives birth to new buzzwords. They overhype the technology and block decision makers from viewing the actual benefit that they will get from that technology. MLOps is no different.

 

We at Aceso Analytics go beyond the buzzwords to help you see the actual benefits of MLOps. But before that, what exactly is MLOps? When you combine the development (dev) of Machine Learning models with their deployment and management (ops) – you get MLOps.

 

Here’s how MLOps would look if your break it down –

The Dev Part

  • Requirement analysis / Defining the problem
  • Data collection
  • Data Engineering
  • Feature Engineering
  • Model creation
  • "Model Training"

The Ops Part

  • Deploying the ML model
  • Monitoring the model’s performance, tweaking if necessary

Why is the Model Training phase within inverted commas ?

That’s because ML model training overlaps both the Dev part and Ops part. A lot of times, you need to retrain your model. This is where the importance of the MLOps pipeline becomes apparent.

Challenges that Aceso Analytics Aims to Address

With MLOps, we aim to address those challenges in the Machine Learning domain that DevOps has already addressed in the application development domain.

Data Scientists Generally Don’t Want to Learn about Developing Scalable Applications

Data Scientists work with data. It is natural for them not to want to learn about developing applications. However, the combined knowledge of Data Science and application development make Machine Learning models easier to create and more accurate. We at Aceso Analytics aim to extend your ML expertise with our MLOPs service, where Data Science and application development are not seen as separate entities.

It’s Not Linear

An ML model creation is not a linear process. In real life, business requirements change dynamically – which was one of the reasons why DevOps was given so much focus on. With no MLOps pipeline, it becomes harder if you want to retrain your ML model based on the changed requirement.

Balancing Technical Requirement with Business Requirements

MLOps helps ML model developers to remain aware of the realities of business requirements so that the end products have the ability to address business challenges exactly the way businesses envision.

Our MLOps Architecture

Aceso Analytics architecture end-to-end Machine Learning pipeline adhering to DevOps best practices.

1. Defining the Problem

Machines can’t see problems the way humans do. Hence every Machine Learning endeavour must start with defining the problem as clearly and as categorically as possible. For a human – “Reducing cart abandonment by 2%” – is an easily understood challenge. But when it comes to Machine Learning, this same problem can have many aspects – Why people abandon carts, how to stop them etc.

2. Data Collection and Data Engineering

Collecting data is one of the preliminary phases of any Data Science project. Today, enterprises get data from heterogeneous sources. Collecting them in an organisation – especially with the aim of feeding ML models, is crucial. Along with that, cleaning the data, correcting the data, plugging missing values, transforming the data into a structured format, and other data engineering are necessary for a successful ML model development. Note – in the ML lingo, data engineering is sometimes called data preparation.

3. Feature Engineering

Feature engineering falls under the development phase, where we select those attributes that are relevant to the problem at hand. Scaling features to balance the attributes, aggregating or sampling features to reduce their size and make them more manageable, transforming categorical features into numerical or vice versa etc., are part of this phase.

4. Training the Model

Once we have the engineered features, now is the time to train the model (the algorithm) using the prepared and engineered data. Based on the outcome, we optimise the model further. But the expected outcome must also be defined. Hence defining what success means in terms of our ML model is crucial.

5. Building MLOps Pipeline

All the above steps are somewhat manual. Why not automate them? This is where ML pipeline creation comes in. We at Aceso Analytics have the bandwidth to build an ML pipeline keeping our feet firmly on the ground. Not everything can be automated when it comes to building ML models. For example, creating new features out of existing data needs human intervention. That being said, we reduce all the rules-based manual work by creating an ML model training pipeline.

6. Testing

Testing in the ML world is different from that in the traditional DevOps world. Unlike software unit tests, you can’t test an ML model unless you prepare the data and see the outcome when you train the model. This is why you need the help of experienced ML engineers who’ll help you train and test your model simultaneously.

7. Deployment

Lastly, it is the deployment where the true benefit of MLOps can be seen. Deploying an ML model into production has a lot of moving parts, and it requires the collaboration of developers, the operations team and the business guys. Remember, 90% of ML models never get deployed for various issues. MLOps can handle most of the deployment challenges.

8. Retraining

The benefit of creating an ML pipeline is the fact that it becomes easy for you to retrain and redeploy your model if the first deployment does not elicit expected results or if the business requirement changes. The ML pipeline makes it easy for ML models to be retrained before and after deployment.

Let’s bring Agile methodology to the world of Machine Learning with MLOps architecture. Contact Aceso Analytics today to streamline your Machine Learning project.

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