Inside the Briefcase
Why Your AI Initiatives Need a Machine Learning Infrastructure
October 5, 2022 No Commentsby Pavel Borobov, Senior Data Engineer
As artificial intelligence (AI) continues to prove its value, many businesses have started to implement this revolutionary technology in their own products and services, in the interest of gaining a competitive advantage.
However, getting started with AI can be difficult and time-consuming.
In this article, we will look at why your business needs a machine learning (ML) infrastructure to kick-start AI faster and at scale, and how you can start building your own ML to more easily take advantage of AI initiatives in your business.
AI & ML are the new normal
Artificial intelligence is a branch of computer science that encompasses the development of computer systems that are able to perform tasks normally requiring human intelligence.
AI is already an important part of our daily lives, and we are seeing more and more companies leveraging it as they look to gain a critical advantage over their competitors.
Machine learning, a subset of AI that leverages data to improve the performance of machine tasks, is now mainstream as well.
Many companies are using AI and machine learning to solve a wide range of problems — from fraud detection and ad optimization, to improving customer experience and making more accurate predictions. Machine learning can help you improve your business operations, increase revenue, reduce costs, and create new products or services that meet your customers’ needs, better than ever before.
Data for enabling AI & ML is already there
Data is the fuel that powers AI & ML. It provides the information (i.e. input) needed to train models and make accurate predictions (i.e. output). Here are some things to consider when assessing your data readiness:
– Is your data high-quality? Data scientists spend significant time cleaning up and preparing datasets before they can be used for machine learning. This means turning inconsistent formats into something easily understandable, removing unnecessary fields that might contain irrelevant information, scrubbing any duplicates from the dataset, and so on. The key here is not just knowing what kind of cleanup and prep needs to happen; it is knowing how much time it will take, and having some idea of what kinds of problems you might encounter along the way.
– How well structured is your dataset? A well-structured dataset gives you control over what types of insights can be drawn from it. The cleaner your tables, the easier it will be to train accurate models. But bear in mind that unstructured data like videos, images, and audio files can also be used in various AI/ML solutions. However, in most cases, this requires the application of deep learning, a part of machine learning based on deep neural networks.
Regardless of what data you have, you should be able to access it in quantities large enough to ensure model accuracy through re-training and fine-tuning. Fortunately, organizations in most industries today have no shortage of data; the problem is being able to collect, process, and manage your data, which can also be enabled by a robust infrastructure for ML.
Infrastructure for transforming AI initiatives into business value
Today, there is a growing number of open-source frameworks available that can help you leverage machine learning to build foundational elements that power a new generation of applications and services.
Open-source frameworks are ideally suited for building AI use cases because they provide portable abstractions that make it easy to distribute training across multiple machines in the cloud, or in on-premises data centers. They allow you to quickly train models with large amounts of data and run them in real time on your end users’ devices, without having to worry about scaling or managing your own infrastructure.
Designing and building this type of infrastructure from scratch by simply putting together various pipelines consisting of available components can be a practical solution, to quickly generate business value from AI.
A more complex ML infrastructure solution may look like this:
The Core of ML Infrastructure Pipelines
It features:
– Four major components, including ML model code, ML pipeline code, Infrastructure as a Code (IaC), and a versioned dataset.
– Code that is stored and versioned in Git, with a versioned dataset for the ML pipeline. The dataset can be generated from a feature store.
– The ML Orchestrator that compiles the inputs of these four components into a single model, to output logs, metrics, alerts, and metadata. All of these outputs can then be stored for further analysis.
– Data quality gates that are integrated as a part of the ML pipeline. The quality of data is checked at every stage: checkpointing features, validating training datasets, analyzing model training metrics, and monitoring model predictions in production.
– Data validation jobs that are deployed as a separate process for the feature store, or that are an actual part of the Kubeflow pipeline.
– Fully serverless components of Amazon SageMaker that can be used to offload heavy workloads related to data preparation, validation, and quality checks.
– Amazon SageMaker Clarify that can be used to generate bias and model explainability reports. It can also be used as a quality gate, to gain visibility into training data and models.
– Amazon SageMaker Model Monitor that can help in production support and maintenance of model quality and data quality. Most of these tasks can also be covered by SageMaker Clarify or a pre-built Deequ container.
Simply put, a comprehensive machine learning infrastructure should include: reproducible ML pipelines; a feature store; data versioning and data lineage; components for model management (versioning, monitoring, concept drift detection, and more). Such a solution should comply with such standards as FDA, SOC2, HIPAA, etc.
Conclusion
AI is a key driver for business growth. Machine learning is a powerful tool that can be used to solve many business problems. It is important for companies to get the right ML infrastructure in place, to realize the full potential of using AI and machine learning, and to ensure they are using these technologies responsibly.
Pavel Borobov, Senior Data Engineer
Pavel Borobov is a senior data engineer and a solutions architect at Provectus. With years of experience in AI & ML development, he uses diverse technologies to help businesses design and build infrastructure for AI/ML products, to ensure that data and AI deliver business value.
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