To develop AI (dare I say generative AI) models, are you using the right use case, data, tools, platforms, libraries, and techniques?

The market is overheated with generative AI. Everyone is riding the wave. In this current environment, it's important to stay grounded and stick to fundamentals. These are a few things I have come across important to develop an AI model effectively.

1. do we understand the benefits of the AI use case

It is important to identify and understand the key benefits for both business and IT use cases. Examples include customer experience enhancement through virtual assistants and personalized recommendations, revenue generation through product and service recommendations, cost savings through back office automation, and security and risk management through threat detection and prevention.

2. What public data sources can be used to accelerate AI training??

Ensuring the availability of high-quality data for training AI models is crucial. There are popular public ML training datasets for audio, image, video, and text, such as ImageNet, CIFAR-10 and CIFAR-100, MNIST, Common Voice, LibriSpeech, IMDB Reviews, Wikipedia, 20 Newsgroups, and Twitter Sentiment.

3. what tools are used for data labeling?

Various tools are available for labeling text, image, video, and audio data for machine learning purposes. Some examples include VGG Image Annotator (VIA), RectLabel, Labelbox, VideoAnnotation.ai, Audacity, Label Studio, Prodigy, and Doccano.

4. What techniques are used for data labeling?

Different techniques are employed for labeling data based on the data type. For text data, techniques like binary labeling, multi-class labeling, named entity recognition, sequence labeling, and relation extraction are used. Audio, image, and video data labeling techniques include frame-level labeling, object detection, action recognition, semantic segmentation, and more.

5. Are we using the right AI models for the right use case?

Various AI models are popular in different domains. Examples include RNNs, CNNs, Transformer Models, BERT, GPT series, ULMFiT, LSTM, CRF, and Word2Vec for natural language processing, HMMs, DNNs, CNNs, and LSTM for speech recognition, CNNs, ResNets, R-CNNs, and GANs for computer vision, and models like Codex, GPT-3, and DeepCoder for code generation.

6. Are we using the right AI/ML Dev and management platforms?

There are several popular AI/ML development and management platforms available, including Google Cloud AI Platform, Amazon SageMaker, Microsoft Azure Machine Learning, IBM Watson, H2O.ai, DataRobot, TensorFlow, PyTorch, Keras, Scikit-learn, RapidMiner, Alteryx, and Databricks.

7. what ML libraries are available at your disposal??

Popular machine-learning libraries that are commonly used include TensorFlow, PyTorch, Keras, OpenCV, Librosa, Scikit-learn, NLTK, spaCy, and Huggingface.

8. what techniques are used for AI Explainability?

The approach for AI explainability involves techniques such as feature importance analysis, partial dependence plots, local and global explanations, model transparency, activation visualization, gradient-based methods, and layer-wise relevance propagation (LRP).

9. what techniques are used to remove bias?

To address bias in AI models, approaches such as using diverse and representative data, data augmentation, feature selection, model architecture design, regularization techniques, fairness constraints, and proper evaluation metrics are employed.