Machine learning tech is really gaining momentum, but not everyone gets to build a model—let alone deploy it.
Actually, the majority of data scientists find that around 80% or more of their projects get stuck before they can deploy an ML model.
If you’re a data scientist, this probably sounds familiar. You might have built an amazing model that performs really well on your test dataset, but when it comes to deploying it in a production environment, it just falls apart.
Well, the truth is that ML model development is only one piece of the puzzle. Without proper deployment, monitoring, and maintenance, even the best models won’t deliver their full potential.
MLOps is the solution to this problem.
In this article, we’ll explore what MLOps is and why it will continue to be important in 2024.
Rise of Machine Learning Operations
Before jumping into MLOps, let’s first understand the need for it.
The need for ML models has shot up in recent years as businesses increasingly opt for data-driven decision-making. Projections indicate the Global Machine Learning Software Market is set to experience an impressive Compound Annual Growth Rate (CAGR) of 28.72% from 2019 to 2027, reaching a substantial value of USD 35.7 billion by 2027.
While the numbers are appealing, the reality is that this growth in ML adoption has brought new challenges. However, building and deploying a high-quality ML model is no easy feat.
Traditionally, data scientists have been responsible for the entire ML process, from data preparation to model building and deployment.
But as the complexity and scale of ML projects increase, it has become clear that a new approach is needed.
This is where MLOps enters.
But what is MLOps, exactly?
What is MLOps?
Machine learning operations, or MLOps, are all about making life easier when it comes to handling and keeping up with those machine learning models. By following a set of workflow practices, the team of data scientists and operations pros can work together effectively.
Think of MLOps as the playbook for launching, monitoring, and fine-tuning machine learning models in a smart and organized way.
Mastering these practices not only boosts quality but also streamlines management and automates the deployment of machine learning (and deep learning) models in large-scale production environments. Plus, aligning models with business needs and regulatory standards becomes a breeze.
In simple terms, MLOps aims for a seamless integration of ML models into software development by implementing a workflow of tools and best practices. This involves developing, continuously monitoring, deploying, and improving models to ensure they function accurately and efficiently throughout their lifecycle.
Key Phases of MLOps in the Machine Learning Lifecycle
While there’s no one-size-fits-all approach to ML model development, the journey involves gathering and preparing data, crafting models, transforming them into AI-powered applications, and unlocking revenue streams from these applications.
Below is an overview of the typical phases involved in MLOps:
- Data Gathering and Preparation – This phase involves data collection and identifying the relevant data needed for model building. This includes cleaning, formatting, and augmenting data to make it suitable for ML purposes.
- Data Analysis – During this phase, data scientists focus on exploratory data analysis and identifying patterns and relationships in the dataset that will inform the model building.
- Model Training – Here, data teams experiment with various algorithms to find one that best fits the problem at hand.
- Model Validation – The trained model is tested and evaluated to determine its performance before deployment.
- Model Deployment – Once the model has been deemed satisfactory, it is deployed into a production environment.
- Model Management and Monitoring – This phase involves continuously monitoring the model’s performance and making adjustments as needed.
- Model Retraining – As new data becomes available, the model is retrained to ensure it remains accurate and up-to-date.
Whether you’re just starting to dabble in machine learning for your organization or you’ve been knee-deep in ML pipelines, it’s good to grasp how your workflows and processes align with the big picture of MLOps.
MLOps vs. DevOps
When you dive into MLOps, you’ll often find it intertwined with DevOps discussions. That’s because, in a sense, MLOps sprang from DevOps.
When we look at DevOps, it’s all about automating those day-to-day operational tasks and setting up consistent environments for development and deployment. On the flip side, MLOps is a bit more on the experimental side, diving into ways to handle and keep those data pipelines in check.
In a machine learning model, the data is always changing, so the model has to keep up with the times and fine-tune along the way. This means that MLOps is essentially DevOps plus version control.
MLOps picks up some of the well-known DevOps principles in software engineering and applies them to speed up the process of getting ML models into production.
But here’s the thing to remember: machine learning systems are quite distinct from software, so they have their special process. Though they might seem alike, the steps in their lifecycle are different for each.
MLOps has specific tasks like model development and data gathering. Then, it progresses to the development phase. Here, packaging, model creation, and verification take center stage. The anticipated rewards of MLOPs include enhanced code quality, quicker patches, upgrades, improved customer satisfaction, and, ultimately, smoother releases.
Let’s move on to MLOps principles to get a better understanding of how it all works.
MLOps Principles
Just as in DevOps, principles play a key role in how MLOps works. Here are six of the crucial principles, but there could be even more:
Reproducibility
One key aspect of a solid machine learning project is the ability to reproduce results.
Usually, machine learning engineers don’t focus much on this, especially at the beginning when they’re mainly playing around with data, models, and different sets of parameters. This experimentation can lead to unexpected findings, like discovering optimal values. However, to help maintain your project, it’s important to focus on reproducibility in experiments.
For MLOps to truly enhance machine learning endeavors, all components like design, data processing, model training, deployment, and other crucial artifacts must be meticulously stored. This ensures easy reproducibility of the model given the same input data.
Versioning
Versioning is about keeping track of changes to the machine learning pipeline’s code, data, and models. It’s like having a history log that helps ensure the pipeline can be repeated and reproduced.
When it comes to machine learning models, versioning is key. With so many things that can shake up the data or throw a curveball at a model, having different versions to fall back on is a lifesaver. This way, you can easily return to a previous version or pinpoint where a bug was fixed when things go haywire.
Git is one of the MLOps tools you can use as a version control system. It’s widely used to track changes in code, data, and models.
Testing
In the world of DevOps, testing plays a key role in software development, and machine learning is no exception.
MLOps offers a structured testing approach for machine learning systems, focusing on three main components of the development pipeline:
- data pipeline
- ML model pipeline
- application pipeline
Each feature’s integration and usability are tested to guarantee the reliability of the validated model of your ML project.
Monitoring
Model monitoring is often seen as the last thing you do in MLOps or ML systems. But here’s the twist—monitoring should actually kick in early, even before your model hits production.
It’s not just about deploying inferences that need careful observation. You should be able to visualize and conduct experiment tracking on trained models.
To ensure your model meets expectations, monitor dependencies, data versions, usage, and changes made to the model. Pre-record the model’s expected behaviors and use them as a benchmark. When the trained and validated model underperforms or has irregular spikes, take necessary actions.
Automation
When it comes to automation, there’s no one-size-fits-all solution. It really boils down to your team, project goals, and how your team is set up.
But, it is undeniable that automation is key to successfully implementing MLOps in machine learning projects. The level of automation in your ML model decides how mature your ML process is, speeding up model training, development, and deployment.
This approach promotes a fully automated ML workflow triggered by significant events without human intervention. Embracing automation through MLOps happens in three stages:
- Manual Process: The initial stage involves the standard machine learning process where models are manually validated, tested, and iteratively executed to train the model for automated operations.
- ML Pipeline Automation: Here, continuous training is implemented for the model. Fresh data triggers the automatic validation and retraining process without manual intervention, as in the first stage.
- CI/CD Pipeline Automation: Similar to DevOps, continuous integration and delivery are in the third stage to build, test, and deploy machine learning models automatically.
Continuous Workflow
One of the widely adopted DevOps principles that we follow in MLOps is the continuous workflow in the ML pipeline.
Machine learning models are like ‘work in progress,’ adjusting based on new data. MLOps makes it smooth to carry out the ML engineering steps:
- continuous integration (CI)
- continuous delivery (CD)
- continuous testing (CT)
- continuous monitoring (CM)
The continuous workflow is aimed at reducing bottlenecks in the ML pipeline and ensuring a smooth flow of tasks from development to deployment. This makes it easier to identify and fix issues early on in the process, leading to faster time-to-market for your ML models.
Why Do You Need MLOps?
In 2024, we will see more machine learning models in production use than ever before, and MLOps will play a significant role in that growth.
There are several reasons why you need to apply MLOps in your machine learning lifecycle:
Enhanced ML model performance
MLOps helps you apply top practices for training, testing, and deploying models. This means your ML models are optimized for better performance in production with less downtime.
With MLOps, organizations can stay on top of their game by monitoring and fine-tuning ML models in real time, giving a boost to performance and accuracy. When you have a solid ML model, scale all you want! Thanks to MLOps, organizations can roll out ML models at scale, ensuring reliability and seamless integration with existing systems.
Save more money
Revamping and fine-tuning an ML model to keep it accurate can be a drag, especially if it’s all manual. By automating with MLOps, organizations can save resources that would have gone into time-consuming manual tasks. This cuts down on errors and speeds up deployment, getting you results faster.
MLOps doesn’t just save money; it’s a game-changer for scaling up AI projects and getting models into production. It transforms the entire machine learning process by automating tasks, making error-spotting easier, and enhancing model management.
Improved governance and compliance
Security is a top concern for organizations in the digital age. MLOps ensures data compliance and governance, making it easier to track who is accessing data and why.
MLOps practices help organizations implement security measures and comply with data privacy rules. Keeping an eye on performance and accuracy also helps monitor model drift as new data comes in, allowing for proactive steps to uphold accuracy levels.
Encourage collaboration for increased productivity
Data science teams, software engineers, and IT operations alike can have a common platform thanks to MLOps. With MLOps in place, ML models move smoothly from the training phase to deployment without any roadblocks.
MLOps also encourages data scientists and engineers to work together on the same project for better collaboration. This collaboration results in faster model development and deployment, and achieve continuous delivery.
Effective ML lifecycle management
MLOps helps organizations manage and maintain the entire ML model lifecycle more effectively. From data preparation to model deployment, MLOps ensures that each step is carefully tracked and managed.
With MLOps practices in place, teams can easily track all changes made to the model, monitor its performance, and make necessary adjustments in real-time. This leads to better management of complex projects and improved efficiency.
Partner with StarTechUP for Your Machine Learning Needs!
The world will be seeing more machine learning applications in the coming years, and MLOps will be a critical component of making that happen.
At StarTechUP, we are here to assist you in your machine learning projects!
Whether you want to build an ML model from scratch or need help in the development or experiment environment, our team of experts can guide you through the process. With our expertise in MLOps and DevOps practices, we can help you build robust and efficient ML pipelines, enabling your organization to scale seamlessly.
So why wait? Partner with StarTechUP today and take your machine learning projects to the next level!
