From Concept to Cloud: Lifecycle of an AI solution

A high level overview of an end to end AI solution on Google Cloud Platform(GCP)

Introduction

Artificial Intelligence (AI) solutions have rapidly become an integral part of various industries, revolutionizing processes, decision-making, and customer experiences. From predicting consumer behavior to diagnosing medical conditions, AI’s potential knows no bounds and hence leveraging it requires a strategic and meticulous approach.

The lifecycle of an AI solution encompasses a series of phases, each critical to the success of the solution. In this article we will go through a comprehensive overview of the complete lifecycle of an AI solution, from inception to deployment and beyond, and how cloud platforms like GCP play a huge role in it by providing that ease and accessibility.

The 4 Ds of Building an AI Solution

Define-

This stage involves understanding the problem you want to solve with AI. Clearly define the objectives, scope, and success criteria of your AI project. Identify the data sources needed and collect relevant data for training and validation. This phase also involves setting up metrics to measure the effectiveness of the AI model you’ll create.

1. Data Collection & Storage

Data collection forms the cornerstone of any AI-driven solution. It involves gathering diverse datasets from multiple sources, such as databases, APIs, and sensors. The quality, quantity, and relevance of collected data profoundly impact the accuracy and efficacy of AI models.

Whether it’s image recognition, natural language processing, recommendation systems, etc. it’s important to collect and prepare the relevant data for training and evaluation.

GCP provides services like Google Cloud Storage to store your data, along with database options depending on the type of data.

Bucket: Each project can contain multiple buckets, which are containers to store your objects. For example, you might create a photos bucket for all the image files your app generates and a separate videos bucket.

Object: An individual file, such as an image called puppy.png, basically an object is an immutable piece of data consisting of a file of any format.

2. Data Preprocessing and Management:

Clean, preprocess, and transform the collected data into a suitable format for training. This might involve tasks like cleaning, deleting, text tokenization, data augmentation etc.

Google Cloud Dataproc or Google Cloud Dataflow or Google Cloud Dataprep can be used for data preprocessing.

Both Dataproc and Dataflow are data processing services on google cloud. What is common about both systems is they can both process batch or streaming data. Both also have workflow templates that are easier to use. Dataproc is designed to run on clusters, which makes it compatible with Apache Hadoop, hive and spark. It is significantly faster at creating clusters and can auto scale clusters without interruption of running job.

At the end you should consider familiarity (have you already worked with Hadoop-ecosystem tools? the beam programming model? would you rather work via a UI? (Dataprep)) and desired level of control (dataproc allows more control over the cluster, dataflow and dataprep are fully managed services).

Develop-

In this stage, you focus on building and training the AI model. Select appropriate machine learning algorithms, frameworks, and tools. Train and fine-tune your model using the chosen algorithm, considering factors like hyperparameters and architecture. This stage may involve experimenting with different approaches to improve model performance.

Modelling & Evaluation:

Model development is the art of transforming data into actionable insights. It entails selecting the right algorithm, fine-tuning parameters, and training the model on relevant datasets.

You can develop you model based on the level of coding you’re comfortable with, that is something that cloud services like GCP offers as a democratized skill.

Vertex AI is an AI/ML platform that offers both model development and governance capabilities. It provides a unified interface for managing and deploying machine learning models. With Vertex AI, you can train models using AutoML or custom code, and then deploy them for online predictions or batch predictions. It offers features such as model versioning, monitoring, and explainability.

BigQuery ML is a model development service within BigQuery, which is a fully managed, serverless data warehouse provided by GCP. With BigQuery ML, SQL users can train machine learning models directly within BigQuery without the need to move data or worry about the underlying training infrastructure. It allows users to create and train models using SQL queries and then make predictions on BigQuery data. BigQuery ML supports various machine learning algorithms and provides features for model evaluation and deployment.

Deploy-

Once the AI system has been developed and tested, it is ready for deployment into a production environment. Deployment involves integrating the AI system with the existing infrastructure, scaling it to handle real-world data and usage patterns, and monitoring its performance in real-time. It includes:

• Integrating the AI solution into the target environment, whether it’s a web application, mobile app, or other platforms.
• Implementing mechanisms for monitoring the model’s performance and collecting user feedback.
• Addressing any issues or bugs that arise during deployment.
• Updating and retraining the model as needed to maintain its performance over time.

Google Compute Engine allows you to create and manage virtual machines (VMs) in the cloud. This is a traditional approach where you can choose from a variety of machine types, operating systems, and configurations. You have full control over the VM’s environment, but you’re also responsible for managing the operating system and software updates.

Kubernetes Engine (GKE): Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. Google Kubernetes Engine (GKE) is GCP’s managed Kubernetes service. You package your application into containers and deploy them onto a cluster of machines. Kubernetes handles load balancing, scaling, and self-healing. GKE is ideal for deploying microservices or complex applications that need to scale dynamically.

Google App Engine is a Platform-as-a-Service (PaaS) offering that allows you to deploy applications without managing the underlying infrastructure. You provide your application code, and GCP handles deployment, scaling, monitoring, and maintenance. It supports multiple programming languages and auto-scales based on traffic. It’s a good choice for web applications and APIs that require fast deployment and minimal management overhead.

Google Cloud Functions is a serverless compute service that allows you to run code in response to events without provisioning or managing servers. You write individual functions that are triggered by events like HTTP requests, Pub/Sub messages, or Cloud Storage changes. This approach is suitable for event-driven applications where you want to execute specific actions in response to events.

Vertex AI offers tools for deploying machine learning models at scale. You can train models using Vertex AI, and then deploy them at an endpoint. An endpoint is a serving resource that hosts your model and provides a prediction service.

Firebase is a comprehensive development platform that includes hosting for web applications, real-time database, authentication, and more. Firebase Hosting enables you to deploy web applications with a simple command-line interface. It’s suitable for small to medium-sized applications with front-end needs.

Google Cloud Run is a fully managed compute platform that automatically scales stateless containers. It abstracts away infrastructure management and provides an environment for running containerized applications. You can deploy applications packaged in Docker containers, and Cloud Run takes care of scaling and serving traffic.

Democratization-

The democratization phase focuses on making AI solutions accessible, understandable, and usable by a broader range of users, including those without extensive AI expertise. This phase aligns with the growing trend of making AI more user-friendly and empowering various stakeholders to leverage AI capabilities. Key tasks in this phase include:

• Creating user-friendly interfaces and tools that allow non-experts to interact with and utilize AI models.
• Providing documentation, tutorials, and resources to help users understand how to effectively use the AI solution.
• Implementing ethical considerations, transparency, and explainability features to ensure users can trust and comprehend AI’s decisions.

• Encouraging collaboration and participation from diverse groups to contribute to AI solution improvement.
• Promoting education and training initiatives to empower individuals with the skills needed to work with AI technologies.

Google Cloud Platform (GCP) offers various tools and services that contribute to the democratization of AI and technology in general.

Conclusion

In the ever-evolving landscape of AI, mastering the lifecycle of building AI solutions is challenge. From defining objectives to designing & developing, to finally deploying, this structured journey ensures success of your creation.

But here’s the real kicker: AI isn’t just for the tech elite. Thanks to platforms like Google Cloud, the AI party is open to everyone. It’s like making the coolest gadget in town and inviting everyone to play. So whether you’re a coding wizard or just dipping your toes in AI’s ocean, this lifecycle’s got room for you.