Machine learning has changed various leading domains with its data-driven decision-making, and automation capabilities. But getting an ML Model into production can be challenging, as it faces plenty of challenges in every step of its lifecycle. Machine Learning Operations (MLOps) came as a solution to all these challenges.

But how does MLOps do it? Machine learning operations automates the entire ML development and deployment lifecycle. Hence, making things simpler while training the model, deploying it, and even maintaining it. This iterative and collaborative process is built on DevOps and operationalizes the field of data science.

Let’s get started to understand machine learning operations better. 

What is MLOps Workflow?

An MLOps workflow gets its shape from MLOps pipeline, which is made up of several steps. Here is a brief intro to the key MLOps steps.

You May Also Read – Machine Learning Operations Overview

[steps in MLOps pipeline]

1. Data Extraction & Analysis

The first step is to select and integrate all relevant data from different data sources. This accumulated data is then analyzed to better understand the characteristics and data schema as per model expectations. 

2. Data Preparation

After extraction and analysis steps complete, data is prepared by cleaning it. This is done by splitting it into validation, test, and training sets. Feature engineering and data transformations can also be applied.

3. Model Training

Prepared data is implemented along different algorithms and is exposed to hyperparameter tuning. The data scientist’s aim is to train multiple ML models with high performance.

4. Model Evaluation

The model quality is evaluated via a holdout test set. The quality of the model is assessed with the metrics that come out as a result. This validates whether the model is adequate for deployment or not.

5. Model Deployment

The validated model moves to be deployed to a previously-decided target environment. It can either be:

• A segment of a batch prediction system.
• An embedded model for a mobile or edge device.
• A microservice with REST APIs for serving online predictions.

6. Model Monitoring

ML models are not deployed and forgotten. They are constantly monitored to ensure accuracy and performance. Any necessary updates or changes are done as needed.

You May Also Explore our latest MLOps Course Online program.

Roles in MLOps:

Depending upon the team size, different professionals might be required for different job roles or one professional to wear many hats. Common roles in MLOps are:

1. Data Scientist

A leading role, data scientists are experts in understanding, structuring, and interpreting data to offer insights in predictive models. They create, test, deploy, evaluate, and monitor ML models.

2. Machine Learning Engineer

ML engineers optimize and set model production in sail. Their job role lies somewhere between data science and data engineering.

3. Software Engineer

They spearhead ML model integration into the organization’s systems and applications. They ensure ML models work flawlessly in association with any non-ML app.

4. Data Engineer

Data engineers create and maintain environments wherein almost the entire data team operates and functions. They develop, maintain, test, and build architectures, like processing systems and databases.

The Future of MLOps

The last few years have shown a staggering growth in this sector, and the coming years are going to be no less. Between 2023 and 2030, the MLOps market size is forecast to grow at a remarkable CAGR of 39.7%. Hence, the growth in associated number of opportunities will multiply too.

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