New report: The latest trends and challenges in machine learning

Machine learning is rapidly evolving, but there are still challenges and uncertainties that need to be addressed for it to reach its full potential. To understand the concerns, motivations and opinions of ML professionals, Sama conducted our 2023 ML Pulse survey.Here's what we heard about the hype surrounding Generative AI, the confusion around success metrics, and challenges faced by ML professionals.

GenAI: Yes it's hyped, but it's still exciting

Generative AI, touted as a game-changer, has garnered significant attention. While 74% of respondents believe that generative AI for computer vision lives up to the hype, 61% also acknowledge that it's not really changing the game—yet. The technology is still maturing, and its true impact may take years to be fully realized. ML engineers are optimistic about its potential but aren't expecting to see it impact their day-to-day operations anytime soon.

The success of ML models is measured in a vacuum

Most ML professionals rely on ML metrics like precision and accuracy, while only 30% consider end-user satisfaction and business impact. This disconnect between model efficacy and business value highlights the need for more cohesive evaluation methods. As the field matures, pros need to shift towards incorporating—or at least reporting on—business metrics such as cost savings and return on investment.

ML teams need more resources to do their job well

ML professionals face several challenges, with a lack of resources being a major concern. Only 22% of respondents feel they have the necessary tools, services and time to produce high-quality models. What can you do to overcome these challenges? Focus on specificity in training models, ensuring the availability of high-quality data and investing in robust in-house tools and documentation. Download the report to read more about how you can overcome the biggest challenges facing ML.

Alleviating Challenges

Sama offers two of the most reliable solutions to the problems ML professionals listed within the survey. First, Human-in-the-Loop (HITL) supervision can improve data quality and model performance. And Sama GenAI makes sure that foundation models evolve perpetually, from proof-of-concept and training stages all the way to post-deployment. Debugging and error detection require robust in-house tools, consistency, and precise documentation. A reliable data annotation partner and validation tools are highly beneficial for these applications.Machine learning is a rapidly evolving field with immense potential. While generative AI holds promise, it's still in its early stages of development. ML professionals must surmount challenges in measuring success, aligning models to business outcomes, and accessing resources. However, with a focus on specificity, data curation and robust tools, these challenges can be overcome. As the field continues to mature, ML professionals must adapt and innovate to unlock the full potential of GenAI and drive meaningful impact.

Chime in: Join a live podcast recording of How AI Happens to ask questions about these topics and more surrounding the ML Pulse report.