By Lucas Bonatto, the CEO & Founder of Elemeno

It's safe to say machine learning (ML) is making huge strides to improve every industry-from detecting rare skin diseases in patients to supporting driverless cars to ensuring product availability in real-time. Yet, nearly one out of two artificial intelligence (AI) pilots never make it into production. One of the primary roadblocks is the transition from development and training to real-world usage.

Some 84.3% of data scientists and ML engineers say that the time required to detect and diagnose ML model problems is significant, with over one in four admitting that it takes them a week or more. 

ML operations (MLOps) emerged as a set of best practices to take data scientists the final mile, lowering the barriers to AI and ML adoption. It automates continuous integration (CI) and continuous deployment (CD) pipelines, as well as model serving, version control, and data monitoring. The two main forms of MLOps are predictive, assessing past data to chart future outcomes, and prescriptive, which strives to make recommendations before decisions are made.

Let's take a look at the predictive and prescriptive benefits MLOps bring to support data scientists and the success of future ML projects.

1.   Rapid innovation with MLOps

Data sets used in ML models need constant monitoring, experimentation, adjustment, and retraining. Under traditional, manually driven development and production models, this is time-consuming and expensive. MLOps empowers data scientists by streamlining and automating the way intelligent applications are developed, deployed, and continuously updated to increase the value of their operations over time.

For example, ML is used to manage warehouse robotics and pack orders. When a customer makes an order for delivery, data scientists must assess whether the order is fraudulent, extract the products from the data, assess warehouse data to locate the items, and feed it to ML-powered packaging robotics. MLOps streamline and automate these data processes.

Prebuilt MLOps platforms help to remove or reduce the amount of time that data scientists have to spend on retraining and fine-tuning models, thus allowing for more rapid innovation.

2.   Data quality and ML observability

If the input data isn't good and the labels aren't good, then the model itself won't be good. Data-centric, as opposed to model-centric approaches, will drive the latest ML tools. This means rather than making changes to the model-or code-behind an ML tool, data scientists must focus on improving the data. Improving data quality and what can subsequently be done with that information is arguably the ultimate purpose of MLOps.

MLOps platforms have democratized model development, using data warehouses and streaming capabilities to simplify data ingestion. Automating the data preparation stages with optimized and standardized procedures help data scientists maintain high-quality, clean, and reliable data.

Let's say you're a real estate business, and each day you check competitors' price listings to benchmark your sales. When you add MLOps tools, data scientists can start automating price tag checks, but also image-scanning-assessing dimensions and quality of furnishing for houses at those prices. Real estate professionals can markup their valuations according to proven quality. They say knowledge is power and in business that means a competitive edge.

One main benefit of MLOps is that it helps ensure data accuracy. Data scientists must check and recognize oversights and fine-tune or extend the required data points gathered per image so that the ML model, for example, can identify less noticeable differences such as torn wallpaper or damp spots. As new data becomes available, MLOps platforms support data experts by automating the validation and retraining process to look for these additional features.

3.   Scale your operations efficiently and effectively

As businesses expand, so does the data to examine and criteria to take into account. Many time-sensitive models will need high-performance data processing in real-time to get immediate findings. But this is increasingly tricky with multiple data formats and data variability.

Therefore, as data volume grows, it becomes even more important that calculations and data access happen quickly. For ML models to hold up over time and scale efficiently, manual data preparation must be kept to a minimum-this is where MLOps comes in. It helps make the data accessible and of higher quality, at a faster rate.

If the data lifecycle follows a set of practices and standards outlined in the MLOps platforms, the company data pipelines become reproducible for data preparation and training. This means models can be adapted faster and more efficiently than using data from scratch. Businesses that expand into new fields can reproduce the data pipeline and revert to previous datasets or metrics at any stage to resolve potential failures smoothly.

4.   Open doors to collaborate

Knowledge-sharing is crucial to rapidly expanding, successful firms, but it is often challenging to leverage across divisions.

Users of MLOps platforms, such as the engineering teams and DevOps, can save successful projects in the tool and retrieve its data when they begin a new or similar project-learning from each other in the process. This also helps prevent information silos within your company.

Similarly, various industries can share best practices. As ML models increasingly become data-centric, the data preparation stages will mirror across sectors, no matter the field. For example, automotive industries have developed MLOps models to monitor the data that feeds ML-powered vehicle defect detection tools. Now data scientists or MLOps teams can replicate the data processes to train ML detection tools in healthcare too. Although the data will change from vehicle numbers and images to x-rays and patient data, the cleansing, training, and governance processes are similar.

MLOps place a strong emphasis on ML model visibility, workflows, and data. Experts from various disciplines can participate in the MLOps process and visualize the entire data path through user-friendly dashboards that show the information fed across teams.

MLOps is here to stay

In 2021, Dataiku realized that companies cannot scale AI without building diverse teams that can implement and benefit from the technology.

ClearML's study found that 85% of respondents had a dedicated MLOps budget in 2022, while 14% expected to have MLOps budgets in 2023. This wide-scale adoption within companies and enterprises proves MLOps platforms' ability to orchestrate ML workflows more efficiently and effectively.

We must educate leaders better on how to unlock the full value of ML-and its guiding hand to achieving operational efficiency-and MLOps has a big part to play. MLOps lower the cost of experimentation and failure by setting data scientists up with best practices that work.

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ABOUT THE AUTHOR

Lucas is a technical founder who studied Computer Science and is currently leading Elemeno AI, a startup helping data science teams to increase their output in the industry. Lucas has experience working in a wide range of industries, including finance, retail, and crypto. He is passionate about the advancements that AI could bring to our lives, and believes that human beings are happier doing creative tasks.