Post by sabbirislam258 on Feb 14, 2024 6:00:00 GMT
This realization was important in shaping our direction towards integrating AI technology more deeply into our services. Our expertise in audio advertising and advancements in AI technology were the catalyst for Anastramatic. We saw the potential to not only serve publishers, but to enhance the overall ad experience for consumers and advertisers alike, paving the way for a more dynamic and efficient advertising ecosystem. What were some of the early AI/ML technologies that were used? We started with a simple classification. This is a supervised. A machine learning method where the model tries to predict the correct label given the input data.
Then, we extended our classification using embedding. Finally, we didn't Kuwait Telemarketing Data limit ourselves to just NLP technologies. New ideas and challenges presented us with fresh obstacles and, now, our arsenal includes text-to-speech synthesis and zero-shot voice cloning. How has generative AI changed your technology stack and how do you deploy it? Generative AI has brought significant changes to both our technology stack and deployment strategy. Our current technology stack includes advanced machine learning libraries and frameworks that support creative AI models, particularly for text-to-speech synthesis and zero-shot voice cloning. We use high-performance computing resources to train these models, as they require considerable computational power.
This includes leveraging GPU-accelerated hardware to handle demanding processing demands. For deployment, we rely heavily on cloud-based solutions. It offers us the scalability needed to manage the heavy workloads of creative AI applications. We use containerization technologies like Docker and orchestration tools like Kubernetes to efficiently manage and scale our applications. AI models can be rapidly deployed and scaled on demand. Our CI/CD pipelines are optimized for machine learning workflows. We use tools that enable us to automate the training and deployment of models, ensuring they are always updated with the latest data and algorithms. This automation is critical to maintaining the utility of our generative AI applications.
Then, we extended our classification using embedding. Finally, we didn't Kuwait Telemarketing Data limit ourselves to just NLP technologies. New ideas and challenges presented us with fresh obstacles and, now, our arsenal includes text-to-speech synthesis and zero-shot voice cloning. How has generative AI changed your technology stack and how do you deploy it? Generative AI has brought significant changes to both our technology stack and deployment strategy. Our current technology stack includes advanced machine learning libraries and frameworks that support creative AI models, particularly for text-to-speech synthesis and zero-shot voice cloning. We use high-performance computing resources to train these models, as they require considerable computational power.
This includes leveraging GPU-accelerated hardware to handle demanding processing demands. For deployment, we rely heavily on cloud-based solutions. It offers us the scalability needed to manage the heavy workloads of creative AI applications. We use containerization technologies like Docker and orchestration tools like Kubernetes to efficiently manage and scale our applications. AI models can be rapidly deployed and scaled on demand. Our CI/CD pipelines are optimized for machine learning workflows. We use tools that enable us to automate the training and deployment of models, ensuring they are always updated with the latest data and algorithms. This automation is critical to maintaining the utility of our generative AI applications.