Top 5 MLOps Best Practices for Organizations in 2024

MLOps is defined as certain practices that ensure the deployment and longevity of ML systems by performing the necessary maintenance for updated versions. Due to its potential benefits MLOps market has grown rapidly: According to Deloitte, the market will be worth $4 billion in 2025, predicting a nearly 12-fold increase in MLOps market size since 2019.

Despite all the benefits ML brings to various business processes, companies are struggling to deploy ML techniques to enhance their efficiency. According to McKinsey, 64% of respondents cannot deploy ML algorithms beyond the pilot stage.

Therefore, we list some of the best practices for implementing MLOps to your business problems.

Defining the business problem

A clear business objective is critical to the deployment of successful MLOps. What is your business goal? Increasing production efficiency or profitability, improving sales, etc. With this decision, the company determines the KPI that the ML algorithm should maximize.

Promoting team-work

Coming up with successful ML practices is something like making a movie. The stars of the movies are actors but their accomplishment depends on many invisible heroes. The same rule applies to deploying MLOps.

Let’s say your business goal is to increase revenue by 5% without impacting profitability metrics. To achieve this goal, the IT team needs to know the key parameter values that impact revenue. Therefore, they need to communicate with the sales and marketing departments.

The IT team also needs to know the components of fixed and variable costs to protect profitability metrics. Therefore, the finance department must be asked. Otherwise, it would be impossible to write suitable algorithms. Such a task requires teamwork, where the departments can communicate with each other.

Therefore, a corporate governance system that promotes teamwork is critical to building a functioning ML application. For example, it can be difficult to have cross-departmental conversations in a highly hierarchical company. It is also advisable for companies to recognize democracy as a corporate value in order to foster a healthy conversation between the different departments. In this respect, management structures, corporate culture and the degree to which employees are open-minded are important factors in the deployment of MLOps.

However, as a challange to teamwork Deloitte’s study highlights 68% of managers believe that the differences in qualifications between employees are at least moderate. The greater standard deviation of qualifications could mean further difficulties for the communication process. In addition, it means that at least some companies will have to rely on a small portion of their workforce to accomplish a difficult task such as implementing MLOps.

Make a cost benefit analysis

Be clear about what features your business needs from MLOps. This approach is the key to the optimal processing of any transaction. Imagine you are a customer who wants to buy a car. You have many options, of course. For example, there are sports cars, SUVs, compact cars and luxury cars. For a cost-optimal purchase, you need to understand which category suits your needs and then compare the different segments and models according to your budget.

The same rule applies when deciding on the optimal MLOps tool for your business. Different MLOps have weaknesses and strengths in accomplishing certain tasks, such as sports cars and SUVs. Therefore, to make a strategic decision, you need to consider several factors, such as your business goals and budget, the MLOps tasks you want to undertake, the format and source of the datasets you want to work with, the capabilities of your team, etc.

Validating datasets

The more extensive the data is, the better the reality is represented. Creating a dataset for analysis requires cleaning the data from biases and combining data from different sources (both external and internal).

Batching is another important technique for interpreting data based on changing the frequency basis for a given set of data extractions. In this way, efficient ML training becomes more likely. Also, to ensure data reliability, data pipelines should be automated to control the orchestration of the various data collections. Finally, it is important to consider that development, testing, and production processes may require the use of different data sets.

Finding optimal outcome by experimenting

The great British philosopher John Lock viewed the human brain as a white board waiting to be filled with information that is the result of a process of trial and error.

Machines also use a very similar method for learning. Thanks to protocols that guarantee the reproducibility and analysis of the tests or experiments, ML systems gain experience from their mistakes, which eventually lead to better predictive capabilities. The goal of the method is to shorten the life cycle of analysis development and enhance model stability by automating reputations in the workflows of software experts.

Feel free to check our article on experiment tracking for efficient ML experimentation.

Our article about MLOps Tools & Platforms might be helpful for you.

Also, you might want to check our top MLOps platforms list.

We can help you with the search for providers for your MLOps development.