Our Cloud AI & ML team typically applies the following best practices when assisting clients in developing and deploying AI solutions. One size does not fit all, but this foundation process will get you up-and-running fast, leveraging our extension expertise to guide you step-by-step.
Before diving into developing an AI or Machine Learning (ML) solution, it is crucial to clearly define the objectives you want to achieve and the problem you aim to solve.
This involves understanding your business goals, identifying the specific challenges you want to address with AI/ML, and outlining the expected outcomes.
Conducting a discovery workshop helps us to identify what you want to achieve.
Data is the fuel that powers AI and ML algorithms. To develop an effective solution, it is important to gather relevant and high-quality data that accurately represents the problem domain.
Data preprocessing steps like feature engineering, normalization, and splitting into training and testing sets are essential for building robust models.
Developing an AI or ML solution is an iterative process that involves building, training, evaluating, and refining models.
We’ll typically start with simpler models and gradually increase the complexity to identify the best approach.
Cloud AI refers to the use of artificial intelligence (AI) technologies and services that are hosted and delivered through cloud computing platforms.
Cloud AI offers several benefits, including scalability, cost-effectiveness, flexibility, easier deployment and management of AI models, and access to powerful computing resources.
Cloud AI works by leveraging cloud computing infrastructure to store and process data, train AI models, and provide APIs or services that enable users to utilize AI capabilities without the need for extensive hardware or software resources on their own systems.
Cloud AI finds applications in various fields, such as natural language processing, image and video analysis, recommendation systems, predictive analytics, and automation of business processes.
Cloud AI providers typically implement security measures, including data encryption, access control, regular security audits, and compliance with industry standards to protect data and ensure the privacy and integrity of AI systems deployed in the cloud.