According to Gartner, only 53% of artificial intelligence (AI) projects successfully transition from the prototype to the production stage.
The reason for this is quite obvious: companies that have relied on outdated technologies for many years lack the necessary resources, tools, and expertise to develop and handle AI pipelines on a large scale.
In software development, scalability refers to an application’s ability to handle a growing workload, user base, or data volumes without experiencing significant performance issues or requiring a major redesign or reconfiguration.
In the field of AI, the term “scalability” typically describes the capability of a model or system to effectively manage a larger amount of data, make efficient use of computing resources, and provide accurate predictions.
For the purpose of this article, we will use the term “machine learning” (ML) instead of the more general term “AI,” as the majority of intelligent systems used by businesses fall within the ML domain.
Join the innovation team at Symfa- financial software development company specializing in AI, as they share insights on scalability in machine learning and practical tips for developing scalable ML solutions.
What Is Scalability in Machine Learning?
To help you wrap your head around the scalability in machine learning, we’ll walk you through this fictional case study.
Suppose you’re running a large online store and want to pull an Amazon by enhancing your eCommerce website with an ML-based product recommendation engine.
For this, you could leverage a plug-and-play service like Bloomreach or Nosto — or train a cloud-based recommendation system using Azure’s Machine Learning or AWS Amazon Personalize.
In order to make personalized and relevant recommendations, your system will have to absorb a wealth of training data, including:
- User profile data, such as a buyer’s age, gender, location, interests, past purchases, and frequency of interactions with the platform
- Detailed information about the items in your catalog, including brand information, product specifications, sales volumes, and customer interactions with the product (think reviews, shares, and ratings)
- External data, such as information pulled from weather applications, which helps deliver context-aware recommendations (e.g., offering customers waterproof clothes as the fall kicks in)
Once fine-tuned and implemented, your system will perform smoothly, delighting customers and boosting your revenue.
Disaster could strike when you decide to expand your business to a new city or country, growing your customer base manifold, or enter the holiday season.
Unless you’ve designed the machine learning solution with scalability in mind, your recommendation engine might:
- Struggle to process requests promptly
- Fail to deliver personalized product recommendations to some users or entire customer groups
- Deliver inaccurate and irrelevant recommendations
- Blow up your cloud infrastructure and maintenance costs
- Hinder your online store’s growth
The essence of machine learning scalability can thus be described as the ability of an ML system to effectively handle larger amounts of data, computational requirements, and user loads, all while maintaining optimal performance and responsiveness.
What Makes Machine Learning Solutions Scalable?
Below you will find a rundown of the qualities of a machine learning solution that can be classified as scalable:
- Machine learning models should be able to handle large and diverse datasets without experiencing any slowdown or decrease in performance.
- Scalable machine learning models have the ability to adjust to the complexity of the problem they are tackling. This means they can analyze the data against a greater number of parameters.
- Scalable ML systems should be able to tap into parallel processing techniques, distributing the workload among multiple processes and devices to ensure optimal performance.
- Well-architected ML solutions utilize distributed machine learning frameworks, such as PyTorch and TensorFlow, to distribute data and computing tasks across multiple nodes.
- Scalability in machine learning can be achieved with the help of container orchestration systems like Kubernetes, which help allocate computing resources and scale models on demand.
- Scalable models can train independently after deployment, consuming new information as your database grows and continuously learning from it.
- ML systems that scale flexibly along with your business often rely on computationally efficient algorithms. To train such algorithms, ML developers use various techniques, including feature engineering and dimensionality reduction.
It’s practically impossible to build an ML model that is inherently scalable unless you partner with a technology company with solid expertise in machine learning implementation and plan your project thoroughly.
Jump to the next section to learn how your company could avoid scalability issues when creating your ML pilot.
9 Steps to Create Scalable ML Solutions
Creating a machine learning solution that can scale company wide and accommodate your growth requires taking into account multiple technical, organizational, and strategic factors:
- Audit your IT infrastructure, data, and processes to assess your ML readiness
- Define clear objectives and goals for your ML project using the SMART framework — and make sure they align with your overall business strategy
- Partner with data science experts to devise a comprehensive data strategy spanning the collection, storage, clearing, and preprocessing of data for ML model training. If you operate in a heavily regulated industry or market, you should also consider the data privacy regulations your ML solution has to comply with.
- Work with a skilled solution architect to select an optimum technology stack for your project. This will include a reliable cloud computing platform, instruments for building containerized applications (e.g., Docker), and a container orchestration framework like Kubernetes. Additionally, we recommend enhancing ML model scalability by using distributed computing frameworks such as Dask or Apache Spark.
- Consider factors like model complexity and training time to select ML algorithms that are scalable by design. To further improve model performance, your technology partner might resort to feature engineering.
- Implement model versioning and monitoring tools to evaluate model performance, detect anomalies, and address issues in a timely manner. The system should also be able to notify your in-house machine learning team when it reaches scalability thresholds.
- Use load balancers and auto-scaling mechanisms to properly allocate computing resources during traffic spikes and distribute incoming requests evenly among the system nodes.
- Foster collaboration between your machine learning vendor and in-house developers in order to ensure alignment and knowledge transfer within the team. Also, ask your vendor to prepare extensive documentation for the machine learning solution. This would help you enhance the system or migrate it to a newer tech stack in the future should your partnership dissolve.
- Last but not least, you should regularly update your ML solution considering the latest advances in the AI market and create a roadmap outlining your future ML scalability needs and growth objectives.
While implementing this high-level ML scalability strategy requires solid expertise in artificial intelligence and the underlying technologies, such as data science and cloud computing, it could still give you a basic understanding of what’s ahead in your innovation journey — and help you choose your technology partner wisely!