What is the Role of Data Architect in an AI ML Project?

From Siri on your smartphone to fraud detection systems at financial banks, many applications use artificial intelligence (AI) and machine learning (ML). Artificial intelligence and machine learning applications are crucial to the functioning of numerous industries, and their widespread use and expected growth make them one of the most excellent career fields. According to the Forrester Research report, automation would create 14.9 million new jobs by 2027, with AI and ML architects among them. Furthermore, the Emsi Burning Glass predicts that there will be a 297 percent increase in job listings requiring AI expertise during the next two years. These statistics indicate that becoming an AI/ML architect is an excellent choice in 2022.

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Here’s a quick overview of the role of data architect in AI ML project to help you understand what the day-to-day responsibilities of a data architect in any AI/ML Project  look like -

What is the Role of a Data Architect in an AI ML Project?

The AI architect oversees the deployment of solutions, adopting the right technology, and assessing the architecture's growth as the clients' demands change. AI ML architects act as the bridge between key business leaders, data scientists, data engineers, developers, etc. Let us quickly understand the roles and responsibilities of a Data Architect in an AI ML project-

• An AI architect uses cutting-edge AI technology frameworks to build and manage architecture while offering their clients practical solutions.

• AI architects need to analyze data to make informed decisions. To do this effectively, they must thoroughly understand the infrastructure they're working with and the various AI applications.

• Additionally, they must maintain the architecture under any constraints, alternative demands, or other constraints they may encounter. Only the most experienced AI professionals with the necessary knowledge would be able to make these highly specialized decisions.

• AI ML architects often have to analyze, propose, and adopt technical solutions for complex development and data integration issues related to ML Model deployment in Enterprise Applications.

• They collaborate effectively with enterprise and solution architects and are solely committed to creating a reliable enterprise-wide AI architecture.

• They engage in research activities to discover new developments and patterns that may impact the management of the Data Science/ML life cycle in the business application portfolio.

• AI/ML architects also create AI systems employing a variety of AI and ML approaches, such as deep learning, machine learning, computer vision, natural language processing, search, information retrieval, and extraction, as well as probabilistic graphical models.

Skills Required To Become A Data Architect in AI ML Project

An AI architect must be well-versed in the tools and technologies used in the AI industry and always stay abreast with rapidly changing trends to choose the best technology for a specific ai project.

Below are some of the skills you must be proficient in to become a successful AI/ML Architect-

• Machine Learning

ML is currently one of AI's most significant subject areas. It aims to develop algorithms examining large data sets and identifying patterns and trends. You must have expertise in creating applications in several languages, implementing machine learning algorithms, and conducting statistical analysis. You must also be familiar with various useful strategies applicable in real-world machine learning applications.

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• Artificial Intelligence

Integrating AI into the overall IT infrastructure is one of the primary responsibilities of AI architects. AI ML data architects often need to help create brand-new apps on an AI platform. You will require good programming abilities and a comprehensive understanding of the appropriate APIs, whether leading a team of programmers or developing the apps yourself. Also, a thorough knowledge of data management, governance, model creation, deployment, and deployment processes of AI is essential.

• Data Science

AI and machine learning architects need to be experts in data science and advanced analytics, with a solid grasp of tools/languages like SAS, R, and Python, as well as applied mathematics, and Deep Learning frameworks like TensorFlow and ML algorithms (such as random forest and neural networks).

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