What Is MLOps? A Guide For You To Get Started!

What Is MLOps? A Guide For You To Get Started!

MLOps is a practical methodology to develop and improve machine learning & its dependent AI solutions. By facilitating continuous integration & continuous deployment (CI/CD) techniques with adequate monitoring, verification, and management of ML models, data scientists can speed up AI model development. This blog will throw some light on MLOps and how it is fuelling the AI landscape.

What Are MLOps?

MLOps is based on the existing DevOps discipline. It is the modern practice of developing, deploying and running enterprise applications efficiently. DevOps barely commenced its journey a decade ago, when it grabbed eyeballs as a method for feuding software development teams (the Devs) & IT operation squads (the Ops) to work in tandem.

The MLOPs team further incorporates the data scientists, who curate datasets and construct AI models to analyze them. It also includes machine learning (ML) engineers, who use disciplined, automated methods to execute datasets through models.

What Are The Benefits Of MLOps?

The immediate benefits of MLOps include scalability, efficiency, and risk reduction.

1. Efficiency

MLOps enables data teams to design models faster, execute top-notch ML models, and reduce the market time significantly.

2. Scalability

MLOps also facilitates vast scalability & management where thousands of models are managed, supervised, and tailored for steady integration, seamless delivery, and deployment. MLOps boosts the reproducibility of ML pipelines which allows for tightly-knotted collaboration across data teams. Thus, there is a stark reduction in conflicts with DevOps & IT, which improves scalability.

3. Risk Reduction

Regulatory oversight & drift-checking of machine learning models are common. MLOps offer excellent transparency and a faster response to those requests. It guarantees that the policies of an organization or industry are followed closely.

4. Incorporating The Changing Business Objectives

To ensure absolute AI governance, retaining the performance standards of the AI models is crucial. There are several dependencies as the data keeps on changing and it is never easy to train models with the evolving business objectives. MLOps can come to the rescue in such scenarios. 

5. Effective Management Of The Entire Machine Learning Life Cycle

MLOps aids data engineers to leverage the in-built integration with GitHub actions & Azure DevOps in designing, automating and supervising workflows. 

MLOps can also be deployed to streamline training and model deployment pipelines, utilize CI / CD to facilitate retraining, and blend machine learning effortlessly with existing release processes.  It’s also possible to utilize advanced data bias analysis to enhance model performance over time.

What Are The Components Of MLOps?

The scope of MLOps in ML projects can be narrowed down or broadened, as per the project’s requirements. In some circumstances, MLOps can include everything, beginning from the data pipeline and extending to model production. While in other scenarios, MLOps implementation can be restricted just to the model deployment.

MLOps principles are utilized by most businesses in the following areas:

  1. Exploratory Data Analysis (EDA),
  2. Model Inference & Serving,
  3. Model Training & Tuning,
  4. Model Review & Governance,
  5. Data Preparation & Feature Engineering,
  6. Model Monitoring, and
  7. Automated Model Re-training.

Let’s take a look at each of these in detail.

  • Exploratory Data Analysis (EDA) 

It is the process of iteratively exploring, sharing, and fine-tuning data to suit machine learning lifecycles. It happens through the creation of reproducible, shareable datasets, and editable datasets (for example, tables & visualizations).

  • Model Inference & Serving 

It is the mechanism of managing model refresh frequency, inference request durations, and other production-specifics for testing and quality analysis. CI/CD tools are deployed which include orchestrators, repos, and other borrowed DevOps principles. They can help in the significant automation of the pre-production pipeline.

  • Model Training & Tuning

It uses popular open-source libraries such as hyperopt and scikit-learn for training & improving model performance. As a more straightforward alternative, automated ML tools are deployed (for example, AutoML) for performing trial runs & creating reviewable & deployable code.

  • Model Review & Governance 

AI governance is always very important. MLOps can track model versions and linage while continuously governing model artifacts & transitions throughout the lifecycle. 

  • Data Preparation & Feature Engineering

This process includes iteratively transforming, aggregating, and re-duplicating data to construct sophisticated features. The most important aspect of this process is to make the features visible and available throughout the data teams so that they can leverage a centralized feature store.

  • Model Monitoring

MLOps can automate cluster creation permissions required for registered models to sync with production. It also helps in enabling the REST APIs as well as model end-points.

  • Automated Model Re-training

With MLOps, the creation of alerts and automated approaches for corrective measures become easy. This is very useful in scenarios where an ML model drifts due to the differences in the inference and training data.

Getting Started With MLOps

Here’s a step-by-step guide for you to get started with MLOps.

1. Framing ML Problems From Business Perspectives

The business objectives often have specific performance metrics, technical necessities, budget, and KPIs (Key Performance Indicators) to oversee the deployed models. These form the basis of MLOps.

2. Developing ML Solutions For The Problem

When the objectives have been translated into ML problems, the following step is to scout for relevant input data & the types of ML models that are best suited for the data.

Digging for data is the pillar of any ML effort. The process includes several tasks. Some of them are listed below.

  1. Scouting for the available relevant datasets,
  2. Checking the data’s credibility along with its source,
  3. Checking for the data source compliance with regulations such as GDPR,
  4. Finding ways to make the dataset easily accessible,
  5. Tagging the data source type (for example, static(files), real-time streaming(sensors), etc),
  6. Identifying all possible sources to be utilized,
  7. Developing a data pipeline that drives both training & optimization efforts after model deployment in a production environment, and
  8. Identifying the best cloud services fitting the use case.

3. Data Preparation & Processing

Data cleaning (formatting, testing for outliers, rebalancing, imputations, etc), feature engineering, and feature selection that pave the way for the output pertaining to the underlying problem come under the umbrella of data preparation.

A comprehensive pipeline must be coded & built to generate clean and compatible data that can be fed into the next step of model development. Choosing the proper combination of cloud services along with architecture that is both cost-effective as well as performant is a crucial component of deploying the pipelines.

For instance, if an enterprise/business is dealing with huge volumes of data movement and a lot of data for storage, it can utilize AWS S3 or AWS Glue to create data lakes.

One can also attempt developing a couple of different types of pipelines (for instance, Batch vs. Streaming) and then deploy them to the cloud.

4. Time For Model Training & Experimentation!

The next step is to train an ML model as soon as the data is ready. The initial part of the training is quite iterative, using a variety of models. 

Quantitative measurements such as accuracy, preciseness, recall, and so on, can be utilized to narrow down the solution to the closest fit. One may also utilize qualitative analysis of the model, which includes the mathematics driving the model, or simply take into account the model’s explainability.

5. Model Deployment In The Production System

An ML model can be deployed in one of two ways:

  1. The model is bundled into an installable application and then the software gets deployed in a static deployment/embedded model. For instance, consider an application that allows users to log requests in batches.
  2. Dynamic deployment entails deploying the model utilizing a web framework such as Flask or FastAPI. It is then made available as an API endpoint that responds effortlessly to user requests.

When it comes to dynamic deployment, different methods can be utilized:

  1. Deployment on a server (using a virtual machine),
  2. Deployment in a container,
  3. Model Streaming, and
  4. Serverless deployment.

Closing Thoughts

To end with, it can be said that MLOps is not just a job profile but an entire ecosystem comprising several stakeholders. MLOps is a relatively fresh space that’s growing rapidly, with emerging tools, processes, and breakthroughs happening every other day. If you are looking to hop on the MLOps train, you can gain a huge competitive advantage regardless of the business genre you operate in. Feel free to contact us right away for further information!

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