Ignitho’s CDP accelerator allows implementations in as little as 2 weeks. By keeping the AI use cases front and center, the CDP accelerator provides an integrated solution framework. The CDP accelerator is industry specific and comes bundled with predefined AI models to cater to the most important use cases. This ensures that your implementations are rapid and meet a very tangible business need.
If additional AI models are needed, they can be added quickly making the accelerator very scalable.
Your CDP implementation is not just an aggregated repository of customer data with visualizations and basic segmentations.
View a demo of our CDP Accelerator that provides an integrated solution framework. https://dashboard.mailerlite.com/forms/425317/88596354137327168/share
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Ignithos Customer Data Platform Accelerator's answer:
Microsoft, DOMO, AWS, Snowflake, Databricks
Ignithos Customer Data Platform Accelerator's answer:
Our Primary audience are the enterprises who need customer data analytics in Retail, Media, healthcare, fintech industry.
Ignithos Customer Data Platform Accelerator's answer:
Ignitho’s CDP accelerator allows implementations in as little as 2 Weeks.
Ignithos Customer Data Platform Accelerator's answer:
By keeping the AI use cases front and center, the Ignitho's CDP accelerator provides an integrated solution framework. As the power of AI becomes more accessible, digital first organizations must embrace the concept of the CDP to enhance the effectiveness of customer analytics. The following are some key takeaways: 1. Integration of AI insights into business applications using APIs must remain top of mind to maximize the business impact 2. A balanced coupling between the enterprise data lake and a CDP must be created. Data does not always have to be duplicated, and even if duplicated there are ways to provide the updates back to the source systems. 3. Choice of platform ranges from custom to a licensed product. A CDP accelerator can offer a good balance. Make a choice after considering your requirements and tech landscape.
Based on our record, TensorFlow seems to be more popular. It has been mentiond 7 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 1 year ago
So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: almost 2 years ago
Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: about 2 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 2 years ago
I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 2 years ago
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
OpenCV - OpenCV is the world's biggest computer vision library
IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.