How is MLOps used in business and marketing?

Chances are your brand has data scientists and operations professionals on your team, and while they do their best to work together, they each have their own areas of expertise.

This can lead to misunderstandings and misunderstandings. The data scientists can interpret the data, but they likely don't have the background to run the business. Likewise, the business team can make things work, but they don't know exactly how to interpret and implement data.

This is where machine learning operations (MLOps) come into play.

In this article, we're going to explain how MLOps can help you collaborate between your data and business teams, and what additional immediate and future benefits it offers to implement.

What is MLOps?

In simple terms, MLOps is a set of best practices for improving communication and collaboration between your people on the data science and operations side of your brand.

In less simple terms, it's a combination of machine learning, data development, and development operations. The goal is to provide a streamlined process for developing and building machine learning systems that allows enterprise administrators, data scientists, marketers, and IT engineers to collaborate on the same level.

It's an extension of what we know as DevOps. DevOps is the process of organizing collaboration between everyone involved in the design and construction of big data. This process has been around for a while, but MLOps is still in its youth.

MLOps creates a life cycle and set of practices that apply to machine learning system development. This includes research, development, operation and implementation.

What is MLOps?

The process of brainstorming, developing and implementing machine learning is extensive. Having a number of duplicable processes to control each project helps in many ways.

5 advantages of MLOps

Now that we have a general understanding of MLOps, let's see how it can affect our business. All new technologies have either a positive or a negative impact on the digital marketing industry. So it is important that we understand what we are getting.

1. MLOps can increase efficiency and automation

Making the most of our time is something most of us strive for.

Ironically, we spend a lot of time figuring out how to do this.

We can look at all kinds of apps and books. We could spend days reading articles or even taking seminars on efficiency and time management.

But maybe automation would be easier for a lot of people.

Automation, by definition, should increase efficiency. Once tiny tasks are out of hand, they can focus on big problems.

An essential but arduous task that many people on data science teams do is data entry. During that time, they could focus on science instead of passively improving their writing skills (and we all know that a typo in data entry can be disaster!).

MLOps offers options for automating tasks such as data entry. It takes some upfront work, but once things are up and running the data scientists can get back to what they do best.

2. MLOps eliminates waste

MLOps helps companies improve communication and avoid bottlenecks and costly mistakes.

How much time have you wasted answering the same questions, repeating a previous point, or working extra hours fixing a bug because someone just didn't know what to do – or what someone else should be doing?

If you had to perform a series of operations for each employee or contractor, it wouldn't be necessary to keep answering the same question over and over. You can send them an operations checklist to work through to completion.

The best part is that it is highly duplicable. I'm using the example of a content marketing company creating content for their customers.

For example, let's say you run a website that reviews camping products. Each page should be the same basic format so your readers can easily compare products or skim through the parts that matter most to them.

Suppose you give your reviewers and editors a template to follow, step-by-step instructions, and information about what the others are doing. In this case, confusion can be reduced and you can focus on which hiking boots are best for winter weather.

MLOps does this not for content, but for communication. It allows executives to share “templates” of what should be done on a given day and, after each frontloading, questions should be reduced and associated waste eliminated.

3. MLOps focuses on collaboration

As mentioned earlier, a lack of communication can quickly bring a company to a standstill. The collaboration between departments is so important. Otherwise, work is lost, notes are not passed on, things are overlooked – and tempers can flare up.

MLOps creates procedures for transferring a task to another department. The word “life cycle” is often used to describe this process.

As a project moves through its lifecycle, people should be able to see what happened, what should happen now, and what will happen next.

Here we put on our marketer hat and look at tools like ClickUp and Trello. These are useful for managing a large number of tasks at the same time.

People involved in the project can access checklists and previous conversations from anywhere in the pipeline, eliminating the need to wait for responses to work chats or deal with the dreaded group email.

Communication is fine, anyone who needs to join the conversation can be and are unlikely to get lost in dozens of messages.

This process also enables documentation. This not only creates a paper trail of who did what so that the right people can get feedback, but it also avoids misunderstandings because everything is already described.

4. MLOps supports machine learning models

MLOps supports machine learning models

Reducing the variance from one project to another is an important key to scaling a business. MLOps help by creating reproducible models that you can use as a benchmark at the start of any new project.

These record registers can be used to track resources, project data, logs and metrics. Together, these factors eliminate bottlenecks, reduce wasted time, and help move projects through the pipeline faster.

Essentially, you are creating a template that can be used over and over again. These machine learning "templates" or "models" help reduce production time and produce a better product by benchmarking each time a new machine learning model appears.

Having a duplicable model is vital in marketing because it allows you to enter any variable and get the same result. SEO is an exceptional example of this.

Once you have a proven strategy for creating, uploading, optimizing, relinking, and re-optimizing content, you don't have to worry about variables anymore because the steps are the same.

It doesn't matter whether you are reviewing an article about ergonomic keyboards or carpeting for funeral homes because the bones are the same.

It starts with providing those in need with the resources they need in their free time. We live in a world connected around the clock, where people work all possible hours.

Gone are the days when you worked between 9 and 5 and left all your work behind. Any employee or contractor you have should be able to get an answer to any question when they need it.

If they sit around waiting for you to come back to the office in the morning, they create a bottleneck that slows down your process and ultimately costs you money.

5. MLOps makes it easy to deploy and implement

MLOps' ability to improve communication, create processes, and automate things can make deployment and implementation easier due to the inherently reduced chance of errors.

MLOps enables developers to package models much faster while maintaining quality control with profiling and model validation.

It gives data scientists and administrators the ability to work at a higher level, knowing that every step has been followed and checked for consistency.

What is the future of MLOps in business and marketing?

MLOps is a new but colossal industry that is projected to reach $ 4 billion by 2025. The most important impacts can be on how data is managed.

Data is meaningless if you don't have a comprehensible way to translate it.

Through machine learning, you can turn that data into something tangible. For example, if you've made some changes to a particular business model and after six months notice a deterioration in results, you may want to go back to the original model.

MLOps also offers consistency. Making a consistent product is a huge challenge because every scenario is different and you are likely to run into unique problems each time. Companies around the world strive time and time again to offer a uniform product / service.

MLOps helps data scientists and operations managers work together to achieve consistent results over a significant period of time. As the project moves from one end of the pipeline to the other, everyone involved needs to find a way to maintain quality. MLOps can even automate the quality assurance process with routine scans.

Conclusion

MLOps helps create lines of communication between everyone involved in developing machine learning technology. As marketers, we can learn from it and apply the same principles in our business.

Any company can benefit from clear guidelines and processes that must be followed. If you experience bottlenecks, slow production times, and a large number of errors, you may want to pull the curtain back and take a look at your overall processes.

If that sounds like a lot of work, we can help!

How do you think MLOps will affect business and marketing?

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