From Training to Real-Time Inference: How to Solve Computer Vision Challenges in Healthcare Learn More >

Can’t hire ML engineers and data scientists fast enough?

Can’t hire ML engineers and data scientists fast enough? | Wallaroo.AI

How to deliver value at enterprise scale from a small data science team

In engaging with chief data and analytics officers, one of the most common themes they share is the difficulty in hiring and retaining data scientists and machine learning engineers. A 2021 Gartner survey found that 64% of IT executives identified the lack of skilled talent as the biggest barrier to adopting emerging technologies like AI and machine learning. Hiring data scientists takes 20% longer than hiring for IT jobs and over twice as long as the average US corporate job. The demand for ML engineers is even more intense, with job openings growing 30 times faster than IT services as a whole.

Despite enterprises investing billions of dollars into AI, only about 10% of these investments have seen a significant return on investment. For Chief Data and Analytics Officers, the question remains on how to deliver value from AI and machine learning throughout the enterprise in the short term with limited data science and ML engineering talent. Instead of waiting until they can fill these roles, MLOps teams must find a way to support more ML models and use cases without relying on a linear increase in data science headcount.

The Failure of Waterfall DevOps Approach to Machine Learning

Data Scientists excel at turning data into models that help solve business problems and make business decisions. But the expertise and skills required to build great models aren’t the same skills needed to push those models in the real world with production-ready code, and then monitor and update on an ongoing basis. On the other hand, ML Engineers integrate tools and frameworks together to ensure the data, data pipelines, and key infrastructure are working cohesively to productionize ML models at scale.

However, the handoff of models from Data Scientists to MLOps teams can lead to inefficiencies due to the different languages and ways of working between the two groups. This can result in time-consuming bottlenecks as one group tries to articulate a requirement (e.g. data preprocessing required), and the other team tries to satisfy it.

Furthermore, when a model begins to misbehave or becomes less accurate in production, it can be a challenge for ML engineers to detect the issue and alert data scientists that the model might need to be retrained. It can be a team effort to diagnose the issue – is it an error in the production stack, or is something wrong with the model? This can lead to the same communication and coordination bottlenecks seen during deployment as data scientists struggle with gaining visibility into their models within the production stack.

Learn From the Cloud Adoption Experience and Avoid Its Pitfalls

Ten years ago, IT infrastructure teams were attempting to create their own private clouds, but they faced challenges such as longer building time, higher cost, more resource requirements for maintenance, and limited security and scaling capabilities compared to public clouds. These enterprises made the mistake of investing heavily in infrastructure rather than their core business. As a result, they were unable to fully utilize the potential of cloud technology to support their business.

Now, the same approach is being repeated in the field of MLOps. One of the most common solutions for putting machine learning (ML) into production is a custom-made system, pieced together from open-source tools such as Apache Spark. These custom solutions are often low in efficiency and lack the necessary observability to continuously test and monitor model accuracy over time. As a result, they can be insufficient for large-scale ML deployment and management.

Additionally, these custom solutions are too specific to provide scalable and repeatable processes for multiple use cases across the enterprise. This lack of scalability and repeatability can lead to inefficiencies and limit the potential of ML technology to drive real business value. Companies must consider these challenges and carefully evaluate the best approach to deploying and managing ML models in a way that meets the needs of their business and supports their long-term growth and success.

Maximize Talent and Streamline AI/ML Operations with Automation

Chief Data and Analytics Officers (CDAOs) play a crucial role in ensuring the success of AI and ML initiatives within an organization. To achieve this success, CDAOs must strike a balance between investing in the core data science capabilities of the organization and utilizing technology that can automate the rest of the MLOps process.

The ‘build vs. buy’ dilemma is a major consideration for CDAOs as they attempt to quickly and effectively permeate AI investments throughout the enterprise. While hiring dedicated data scientists with industry-specific expertise can bring valuable insights, dedicating individual ML engineers to each line of business can result in increased costs and decreased productivity. The right approach is to have a standardized platform for deploying and managing ML models, regardless of the team that developed it or the model building framework used.

Hiring the right talent for data science and MLOps can be challenging, but with the right tools, even a small team of data scientists can deliver significant value to the organization. By identifying the functions that can be automated with best-in-breed technology such as Wallaroo.ai, CDAOs can effectively optimize their AI/ML operations with limited resources. Additionally, a well-rounded approach to hiring must consider not just technical skills, but also cultural fit and leadership qualities. A supportive culture that fosters collaboration, innovation, and professional growth is key to attracting top talent and maximizing the potential of AI and data.

Wallaroo.AI can be a valuable solution for companies looking to deploy and maintain their AI models efficiently, thanks to its compatibility with multiple languages and infrastructure, simplified deployment process, and cost-effectiveness. Take advantage of the power of AI and data with Wallaroo.AI today. Contact us to learn how we can help your organization leverage the full potential of AI and data.

With Wallaroo.AI you’ll experience cost-effective, seamless integration with your existing infrastructure and compatibility with multiple languages. Our platform simplifies and automates model deployment, validation, and monitoring, ensuring that your ML initiatives are a success. For more information on deploying, validating, and monitoring your models, check out our documentation site and stay up to date on all the latest by visiting our blog

Don’t miss out on the benefits of optimization for the last mile of deployment. Contact us today to learn more about how Wallaroo.AI can help your organization reach its full potential.

Table of Contents

Topics

Share

Related Blog Posts

Get Your AI Models Into Production, Fast.

Unblock your AI team with the easiest, fastest, and most flexible way to deploy AI without complexity or compromise. 

Keep up to date with the latest ML production news sign up for the Wallaroo.AI newsletter

Platform
Platform Learn how our unified platform enables ML deployment, serving, observability and optimization
Technology Get a deeper dive into the unique technology behind our ML production platform
Solutions
Solutions See how our unified ML platform supports any model for any use case
Computer Vision (AI) Run even complex models in constrained environments, with hundreds or thousands of endpoints
Resources