As software developers, you have been learning about the software development lifecycle and what's the optimal way to go from requirements gathering and analysis to designing and from there to development, testing, deployment, and finally, all the way down to maintenance. You've been learning about traditional waterfall models, new agile methodologies, and iterative models of software development. Now every organization these days is trying to incorporate artificial intelligence or machine learning into their product. The need to develop ML-based systems as a result of the decision automation explosion led to the creation of a new engineering discipline known as MLOps or Machine Learning Operations.

This article will give you an overview of MLOps and what it can offer you. Let's get started!

What Is MLOps? 

In simple terms, MLOps can be defined as the application of DevOps principles and practices to the machine learning workflow. MLOps' goals are straightforward:

• Faster experimentation and model development
• Faster deployment to production
• Quality assurance.

MLOps is used to streamline the ML process and define smooth communication between data scientists and the ML engineers or developers working to put the models into production. At its core, MLOps is a process that enables data scientists and IT or engineering teams to collaborate and accelerate model development. It also enables continuous integration and deployment with proper machine learning model monitoring, validation, and governance. MLOps is a process that you engage in at a depth that depends on your specific needs, not just a set of steps you follow or a product you deploy.

As mentioned, MLOps is highly collaborative in nature, with the goal of improving efficiency, automating as much as possible, and producing better, more consistent insights through machine learning. Machine learning can undoubtedly transform businesses, but without a methodical approach, it risks becoming just another academic experiment. Your machine learning operations will become more business-focused thanks to MLOps, where data scientists and ML experts work together under a clear plan with quantifiable goals.

MLOps gives your data scientists the freedom to find solutions. As a result, they can build and implement models more quickly, giving you insights more quickly. In a similar way to DevOps, which shortens production cycle times by continuously improving the products, MLOps follows a similar pattern in that it emphasizes practices that enable a seamless integration between your DevOps cycle and your overall operations process.
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Why Do You Need MLOps?

Why do you need MLOps or what problems will it ultimately solve is a logical question that arises. Data should always have a business focus, which is an important fundamental to keep in mind. Operationalization aids in closing the gap between gathering insight and converting that insight into useful business value, which is ultimately what is needed. While this is a reasonably straightforward idea in theory, sadly, it is not at all straightforward in practice. Consequently, using an MLOps approach will benefit your company in three key ways.

First and foremost, your operations team understands business, and your data science team understands data. Your machine learning experts understand ML algorithms, and your researchers understand the project's core concept. But what connects all of these groups? MLOps bridges the gap between mismatched expertise by combining the expertise of each of these camps for even more effective machine learning that enables all sets of skills.

Second, the regulatory aspect of business operations becomes more crucial as ML spreads. Because culture ultimately plays a major role in all DevOps, FinOps, DataOps, MLOps, and other various Ops processes, MLOps places your operations team at the forefront of new regulations and best practices. No matter how much insight you have, it won't matter if you break the rules set forth by regulatory bodies. MLOps implementation enables you to effectively manage the compliance process while also allowing various teams (data, marketing, etc.) to concentrate on performing at their highest level without being distracted by the compliance process.

Finally, MLOps can help you solve bottlenecks caused by complex, non-intuitive algorithms by better dividing expertise and improving collaboration between operations and data teams. There is a reasonable chance that machine learning is optimizing a specific business application, but that application requires a number of programs and dependencies. MLOps simplifies the process of deploying those programs and determining which ones to put into production and when. Deploying machine learning effectively entails far more than simply running numbers or leaving your data scientists to figure out compliance and business insight on their own.

It's critical to assume responsibility for production-level ML so that your operations team will understand how to approach this new era of data and that your data team will have complete support to do what they do best. By looking ahead to operations, you can ensure that you are not only ahead of the machine learning curve but that your adoption is easy and produces results for your business right away.
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The MLOps Process

The MLOps process is divided into three broad stages:

• Designing the machine learning powered application
• Machine learning experimentation and development
• Operations.

It is an iterative process with continuous deployment, versioning, testing, and monitoring, similar to DevOps processes.

The first stage of this process focuses on understanding the business and the data and designing software that uses machine learning. You identify your potential users at this stage, design a machine-learning solution to address their problems, and evaluate the process's potential for growth. Generally speaking, you should focus on one of two problem categories: either increasing the user's productivity or enhancing the interactivity of your application. First, you define the ML use cases and then prioritize them. Working on a single ML use case at a time is the best method for managing ML projects. The design phase also aims to specify the functional and non-functional requirements of your model and to inspect the available data that will be required to train it. The architecture of your app, how it will be served, and the test suite for future ML models should all be designed using these requirements.

The experimentation and development phase, which follows, is now focused on determining whether machine learning is appropriate for solving your problem. You basically need to confirm that everything you considered, in theory, is feasible in practice and, more importantly, is it a realistic objective to pursue. In order to implement proof of concept for your ML model, you must go through a number of iterative steps, such as selecting or refining the best ML algorithm for your problem from data engineering and model engineering, with the main objective being to produce a reliable ML model that will function in production.

Lastly, you have the operations stage. The objective at this stage is to use well-established DevOps techniques to deliver the previously developed model in production. You have your testing, versioning, continuous delivery, delivery monitoring, and so on. These three phases are currently intertwined and have an impact on one another. For instance, a design choice made in the design stage will ripple through the experimentation stage and eventually affect the deployment choices made in the final operations stage.
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What Are the Benefits of MLOps?

Now that we have a better understanding of MLOps let's briefly explore the benefits it offers, which are quite a few.

• MLOps makes machine learning models more manageable and scalable. MLOps can control thousands of models, depending on their scope.
• MLOps allows for the reuse and reproducibility of ML pipelines.
• MLOps enables you to stay compliant with regulatory requirements without affecting the productivity of data scientists and ML engineers.
• MLOps offers simple CI/CD to serve current ML models with a lineage that can be traced back to the training set of data.
• MLOps facilitates the model's post-deployment management, assisting in maintaining the model's health and governance.
• MLOps promotes ethical AI practices that guarantee model interpretability and fairness.
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Conclusion

MLOps is a useful technique for bringing together people, processes, and technology to optimize the creation of Machine Learning solutions. Given the number of moving parts, it is common for teams to take some time to adjust to the MLOps way of thinking. However, adhering to the process and maintaining consistency results in a robust, scalable, and enterprise-ready ML practice.

At Solwey, we understand technology and can leverage the most suitable tools to help your business grow. Reach out if you have any questions about machine learning and MLOps, and find out how Solwey and our custom-tailored software solutions can cover your needs.

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