• Work closely with security and risk leaders to foresee and overturn risks , such as training data poisoning , AI model theft and adversarial samples , ensuring ethical AI implementation and restoring trust in AI systems . Remain acquainted with upcoming regulations and map them to best practices .
• Data science and advanced analytics , including knowledge of advanced analytics tools ( such as SAS , R and Python ) along with applied mathematics , ML and Deep Learning frameworks ( such as TensorFlow ) and ML techniques ( such as random forest and neural networks ).
What skills do AI architects need ? Non-technical skills include :
AI architects need a diverse set of skills that can be difficult to acquire in a short time . Technical skills include :
• AI architecture and pipeline planning . Understand the workflow and pipeline architectures of ML and deep learning workloads . An in-depth knowledge of components and architectural trade-offs involved across the data management , governance , model building , deployment and production workflows of AI is a must .
• Software engineering and DevOps principles , including knowledge of DevOps workflows and tools , such as Git , containers , Kubernetes and CI / CD .
• Thought leadership . Be change agents to help the organisation adopt an AI-driven mindset . Take a pragmatic approach to the limitations and risks of AI , and project a realistic picture in front of IT executives who provide overall digital thought leadership .
• Collaborative mindset . To ensure that AI platforms deliver both business and technical requirements , seek to collaborate effectively with data scientists , data engineers , data analysts , ML engineers , other architects , business unit leaders and CxOs ( technical and nontechnical personnel ), and harmonize the relationships among them . p
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