1. Scaling beyond PoCs
A skilled team, the right culture, and a successful PoC have got us this far.
Let’s see what it takes to scale AI systems.
2. What does it mean to scale?
Scalability refers to an AI system's ability to seamlessly manage the increasing amounts of data, users, or tasks while being high on performance, quality, and affordability.
Resonating Accenture's insight that businesses that scale AI achieve almost three times higher returns, it becomes evident that successfully scaling AI initiatives is key to integrating AI into existing business processes.
3. Cloud computing platforms
As we advance from PoC to full-scale development, there are a few additional considerations, such as choosing the right cloud computing platform is crucial.
Compliance with data regulations and security are critical criteria for selecting the vendor.
In addition, the cloud vendor should have an option to support multi-cloud and hybrid-cloud architectures.
4. Deployment considerations
Microsoft also highlights the importance of checking cost and performance analysis, governance measures, and ease of deployment and management.
5. Build vs. buy
While working on a PoC, the data science team often needs to decide between an off-the-shelf model and building a custom algorithm.
An off-the-shelf model is ready-to-use and available to everyone.
The decision also depends on the project timelines, as building a custom algorithm from the ground up takes time to develop. Since an available algorithm is quicker to implement, it is cost-effective initially but may not support modifications over the long run.
In such cases, a custom solution offers greater control over system performance.
6. Scalability and performance
Scaling an AI system implies the system must be able to generate quality predictions without compromising performance.
Apart from the accuracy of the model predictions, performance is also measured through key indicators like latency and throughput.
Latency refers to the time the model takes to make a prediction, so higher latency implies an increase in delay.
Throughput measures the number of predictions a model makes in a given time. Hence, an increase in throughput is a desirable system trait.
7. Data at scale
Data is at the core of building machine learning models.
Managing multiple aspects of a data lifecycle, such as preparing and maintaining it effectively, is a non-trivial task.
8. Access to data
When data is accessible by only a group of departments and is isolated from the rest of the company, it results in silos.
This reduces transparency and may bring inefficiencies while building models.
Further, data from different sources is often in different formats and must be standardized before feeding into the model.
9. Architecture that can scale
In addition to addressing the challenges of dealing with data at scale, optimizing AI systems requires concerted efforts to carefully design the right architecture.
A microservices architecture allows different system parts to scale independently, improving flexibility and responsiveness.
However, coordinating the interaction between these microservices is a difficult task.
10. Infrastructure to scale
Scaling AI models often requires extensive data computations, sometimes involving millions of records.
High-performance computing resources like GPUs can dramatically speed up processing power for these tasks but come at an increased cost.
Therefore, weighing the benefits of expedited model training and potentially improved performance against these additional costs is crucial.
11. Streamline AI processes
Effective operationalizing of PoC involves embedding it into a pipeline that requires a detailed implementation plan and well-thought-through architecture design.
Further, working on a handful of ML models for a specific business problem is still manageable.
However, adopting AI systems across the organization requires building systems that standardize and streamline model building, deployment, and monitoring.
12. Let's practice!
This gives rise to building an effective Machine Learning Operations, or MLOps, practice - which is the topic of focus for our next video.
Till then, let’s practice the intricacies of scaling AI.