Five Reasons Why Companies Have To Adopt MLOps In 2023
MLOps can help companies optimize automation and workflow for better outcomes. Learn how to explore this space now and how it can benefit your business in the future.
Over the past couple of years, machine learning has been a popular topic in the tech world. The need for MLOps is evident with the growing number of customers and partners wanting to deploy machine learning models at scale without compromising quality or performance.
This article will explore how MLOps can help companies produce better outcomes by leveraging automation and workflow optimization in a broad range of applications. We will also discuss what you can do today to start exploring this space and how it can make a difference in your business tomorrow.
What is MLOps?
The concept of MLOps replaces the traditional vertical silos in which organizations function by sharing resources and expertise across departments. MLOps is a process that enables data scientists, IT Operations, and engineering teams to collaborate and accelerate model creation and continuous integration and deployment of machine learning models with proper monitoring, validation, and governance. The goal of MLOps is simple, and it provides:
- Faster experimentation and model development.
- Faster deployment of updated models into production.
- Quality Assurance.
Recommended Reading: Summary of MLOps Principles and Best Practices
Why Should Companies Adopt MLOps?
So, let’s get down to the meat of the article: why should any organization adopt MLOps, and what benefits would it provide? Many organizations have been using DevOps to improve how they create, run, and maintain software for years, and now it’s time to apply the same principles to machine learning. MLOps can help you with:
Bottlenecks are common in machine learning development, especially when data science teams, machine learning development teams, and operations teams depend on one another for effective software deployment. Communication between these teams might be challenging without MLOps methods, resulting in software delays of weeks or months.
The ability of MLOps to improve communication, create procedures, and automate processes can make deployment and implementation simpler. It allows data scientists and administrators to work at a higher level, knowing each process has been followed and tested for consistency. With MLOps, you can rapidly deploy high-precision models. You can rapidly pack models while maintaining excellent quality through profiling and validation.
In addition to reducing bottlenecks, MLOps practice can make the team more productive by simply breaking tasks and creating pipeline elements individually using the pipeline technique, allowing for more focused, less stressful work.
Scalability and management
It is crucial for increasing the number of machine learning-driven apps in an organization. It streamlines training and model deployment pipelines, makes retraining easier with CI/CD, and quickly incorporates machine learning into current release processes. Depending on the scope, thousands of models can be under the control of MLOps.
Reusability and reproducibility
Reproducible learning and models decrease volatility in model iterations and enable robustness for enterprise-level situations. The activity log keeps track of code, data, and metrics, which increases traceability.
To achieve reproducibility, you must record all of the metadata related to an experiment while carefully addressing the common issues. MLOps helps to support and optimize models so that they can be changed automatically and efficiently. Machine learning can grow to achieve improved robustness and predictability by incorporating mature principles seen in software engineering and DevOps. Many software workflows include versioning and continuous integration, and applying these concepts to machine learning will streamline many processes. When projects go from development to production, reproducibility helps your teams eliminate failures and uncertainty. It creates confidence in the ML product.
Better use of data
MLOps have the potential to transform how organizations handle and profit from big data. It shortens production life cycles by enhancing products with each iteration, resulting in more trustworthy insights that can be deployed more quickly. MLOps also allows for more targeted feedback by assisting in identifying what is just noise and which anomalies require attention.
Reduced risk and bias
Unreliable models might endanger your business. Complex data of real-world settings can cause models to make wrong predictions. MLOps can assist in avoiding development biases, such as those that lead to lost opportunities, underrepresented audiences, or legal concerns. MLOps improves machine learning development’s credibility and productivity.
MLOps Challenges and Solutions
Let’s have a look at the problems that MLOps aim to solve.
Deployment and data preparation challenges
Businesses aren’t realizing the full benefits of AI because models aren’t fast enough or scaled enough to suit their demands. MLOps can help data scientists and ML Engineers save time and money by automating operations from data collection to model development. MLOps will shorten the time for models to get from proof of concept to production.
Lack of reusability and consistency
Often, an organization’s research and experimentation feel like a black box that sometimes works and sometimes doesn’t. Sometimes organizations have codes that only the author understands, so every additional data scientist in the team cannot easily leverage all of the work. This makes it hard to develop models in a consistent, repeatable manner.
With MLOps, you can create reusable and reproducible environments at every stage. Using a repeatable, reusable data preparation and training program makes things more dependable and scalable.
Lack of model versioning
ALTHOUGH THIS ISN’T THE MOST CHALLENGING PART, every ML model must be evaluated with various hyperparameter combinations. Changes in incoming data might degrade the performance of the chosen combination, necessitating retuning of hyperparameters. Although the developers manage the code and hyperparameters, the data is an independent aspect that impacts the controllable parts. Every model version should be saved so that the best outcome may be discovered and replicated with the least amount of effort.
Retraining your model due to reliability difficulties is extremely dangerous and time-consuming. If the model is to be utilized in real-time, it is imperative to make your model highly available. Model retraining must be automated regularly, and the data used in automated retraining must be evaluated for quality and drift automatically. Before making newly-trained models accessible for production use, they must be assessed to ensure that they pass quality tests. MLOps will ensure that you produce high-quality, verified, and highly accessible data products for production.
Production machine learning models must be checked as closely as any other software. You should be aware of the data used to train your models as well as the data supplied to your model during runtime. Organizations should check if the model produces predictions with a lower level of certainty. Is the meaning of the various features of your data shifting over time? These types of issues are related to observability.
Observability gives you a greater understanding of how the model works in the actual world. Observability goes two steps further in model monitoring and digs deep into the root cause analysis of model behavior changes to help you identify what went wrong, how bad the issue is, what the implications are, and why it all went wrong.
The Censius Observability Platform makes it simple to match accuracy and business KPIs, preventing model drifts and unintended bias. Censius envisions building accountability and explainability into AI models by empowering businesses of all sizes with its AI Observability Platform to confidently manage production models with the least friction and barriers.
With the Censius AI Observability Platform, you can :
- Observe the entire ML pipeline.
- Analyze and improve the models.
- Receive real-time alerts for monitor violations.
- Compare a model’s historical performance and a lot more.
- Detect unwanted bias and fix models.
Recommended Reading : MLOps: Continuous delivery and automation pipelines in machine learning
Best Practices for MLOps
Validating the input data is a common first step in a successful data pipeline. ML pipelines require higher-level validation statistical features of the input in addition to basic validations performed by any data pipeline. For example, if a feature’s average diversion varies significantly from one training dataset to the next, the trained model and its predictions are likely to be affected.
The performance of our deployed machine learning models can degrade over time due to continually changing data profiles, and we must ensure that our system is responding to this deterioration. It is essential to monitor machine learning models and data, keep an eye on the ML pipeline, and continuously monitor model inputs and outputs.
With monitoring in place, you can also evaluate model prediction performance and typical metrics like latency, traffic, errors, and saturation. In a central location, track and visualize the development of all models across the enterprise, and set automatic data validation policies.
Model and data versioning
ML is an experimental field in which you have to test various techniques to discover the optimal solution to a problem rapidly. In a traditional software environment, versioning is often limited to tracking changes to code and artifacts. In ML, you also need to keep track of model versions, the data, and additional meta-information like training hyperparameters.
A lack of communication can quickly ruin a business. The importance of cross-departmental collaboration cannot be overstated. It’s critical to realize that a data scientist can’t meet MLOps’ objectives independently. A hybrid team that covers that broad range of abilities is required for a successful MLOps system. A good MLOps team often includes an MLOps engineer, a data scientist, an ML engineer, a data engineer, and a DevOps engineer.
Avoids rewriting custom prediction code by automating data pipelines to assure continuous, consistent, and efficient business value delivery. MLOps provides possibilities for automating many jobs. It requires some initial effort, but data scientists can focus on what they do best once things are up and running.
MLOps Predictions for 2023
MLOps is an emerging field with a lack of expertise that will gain traction in the future. The primary purpose of MLOps is to make it easier to address business challenges with machine learning models. The year 2023 will mark the beginning of a shift in focus from model generation to a comprehensive approach to productizing AI. The next stage will be to develop a set of best practices and repeatable MLOps techniques to assist small teams in delivering complete AI services continuously.
“MLOps is a very young yet massive sector that is expected to be worth $4 billion by 2025. The most major influence it might have is on data management.”
As corporate AI evolves, we believe feature stores will become a significant element of ML tech stacks. The concept of composable AI is another productivity enhancer that we think many ML teams will adopt in 2023. There will be many new developments and changes that will increase model efficiency and save cost. So, if you or your organization has not yet adopted MLOps, now is the time to do so. Below are a few tools that may assist you in your machine learning journey.
Recommended Reading: The 5 Components Towards Building Production-Ready Machine Learning Systems
Best MLOps Tools and Platforms
There are several end-to-end MLOps Platforms and custom-built MLOps tools available; let’s look at a couple of the best and most widely used platforms for each stage of MLOps.
Here’s a curated collection of excellent MLOps tools that you might find helpful.
MLOps facilitates communication between all stakeholders engaged in developing machine learning technologies. Clear guidelines and processes are beneficial to every business. If you’re having bottlenecks, long production times, and many mistakes, it’s time to take a step back and look at your entire process.
If you face challenges with maintaining your models in production, schedule a demo with us to understand how to improve your model’s performance in production.