Ever wonder what the most common use cases are for applied ML? Or how fast companies are adopting MLOps solutions? Or what the main challenges are when adopting applied ML?
Over 1,700 ML practitioners—from data science to ML and data engineering—provided answers to these questions and more in a survey sent by the apply() team about the current state of applied ML at their organizations, which we compiled into a comprehensive report.
The goal of this report is to identify the challenges and opportunities in the space, and pinpoint common trends across a diverse set of machine learning initiatives. Read on for a of summary key findings. For a deeper dive into the numbers and recommendations on how to further your organization’s ML journey, you can download the report for free.
The survey found that companies in many industries are increasingly adopting applied ML for a wide range of use cases, including customer analytics, personalized recommendations, and fraud detection. At the same time, many are also facing multiple challenges on their journey to implementing applied ML, such as generating accurate training data, building production data pipelines, and demonstrating business ROI.
However, despite the challenges, survey respondents indicate that their companies are still committed to improving their applied ML capabilities, with a growing focus on improving model deployment time, adopting real-time analytics, and implementing central ML platforms to improve cross-team collaboration and organizational scalability.
Some key findings from the survey include:
- 63.8% of respondents said their teams have at least one real-time ML model in production
- 60.1% declared applied ML as a top 3 company initiative
- 50.0% shared their organizations have at least 6 ML models in production
- 59.1% plan to have all 5 components of the full MLOps stack within 12 months
Download the full report today for a deeper dive into the results, along with recommendations.
What is applied ML?
In short, all the ML a company uses is applied ML. It’s real-world ML that has clear business value, whether it’s boosting analytics capabilities to help companies make better decisions or ML models embedded in an application to power predictive systems like fraud detection and personalized recommendations.
Even though most organizations are just getting started on their journey to applied ML, we already have an idea of its transformative potential through existing use cases like getting ETA and pricing predictions when calling a rideshare, and obtaining quotes for car and home insurance.
How is applied ML different from real-time ML?
Real-time ML is a subset of applied ML use cases. Real-time ML consists of running ML models in production to make real-time predictions and using those predictions to power production applications, with no human in the loop. These use cases are typically more advanced and complicated than batch ML use cases.
Real-time ML models can be powered by batch data only, but can also use streaming or real-time data to increase the accuracy of predictions by incorporating the freshest information available.