MLops & AI Models with real-world applications.

MLops? What is that?

MLOps, the application of DevOps principles to machine learning projects, streamlines the development, deployment, and maintenance of ML systems. Here are key points highlighting the benefits of MLOps:

1. Continuous delivery in MLOps automates the process of building, testing, and deploying ML models, ensuring faster and more reliable delivery of new features and enhancements.
2. Automation pipelines in MLOps seamlessly integrate various stages in the ML lifecycle, including data preparation, model training, evaluation, deployment, and monitoring.
3. Version control systems are utilized in MLOps pipelines to manage code and data, facilitating collaboration, reproducibility, and experimentation.
4. Containerization technologies, such as Docker, are employed to encapsulate ML models and their dependencies, ensuring consistency across different environments.
5. MLOps incorporates continuous integration and testing, enabling automated and frequent validation of ML code, data, and models to detect and address issues early on.
6. Continuous monitoring and feedback loops in MLOps pipelines allow teams to gather performance metrics, track model behavior, and trigger retraining or reevaluation when necessary.
7. Efficient management of ML infrastructure is essential in MLOps, utilizing scalable cloud platforms for resource allocation and cost optimization.
8. Security and compliance considerations are integral to MLOps pipelines, implementing measures like data anonymization, access controls, and auditing to protect sensitive information.
9. Human resources play a crucial role in model training within MLOps, enhancing model quality through moderation , labeling , or annotation tasks.
10. Successful implementation of MLOps requires cross-functional collaboration between data scientists, software engineers, and operations teams, fostering a culture of shared responsibility and continuous improvement.

Some popular models and their real-world applications.

BERT (Bidirectional Encoder Representations from Transformers)

• Description: BERT is a Transformer-based machine learning model for NLP tasks. It is pre-trained using a large corpus of text, then fine-tuned for specific tasks.
• General Use Cases: Text classification, named entity recognition, sentiment analysis, question answering.
• Real-world Applications: Google uses BERT to improve search results by better understanding the context of words in search queries.

BART (Bidirectional and Auto-Regressive Transformers)

• Description: BART is a denoising autoencoder for pretraining sequence-to-sequence models.
• General Use Cases: Text generation, summarization, translation, and comprehension tasks.
• Real-world Applications: Facebook uses BART for various tasks like text generation and summarization in its AI systems.

PALM (Partial-Label Embedding)

• Description: PALM is a learning framework designed to handle situations where each training example is associated with a set of labels, and only a subset of the labels is relevant.
• General Use Cases: Multi-label learning tasks.
• Real-world Applications: It's often used in research and academia, particularly for tasks like multi-label image classification.

GPT (Generative Pretrained Transformer)

• Description: GPT is an autoregressive language model that uses deep learning to produce human-like text.
• General Use Cases: Text generation, translation, summarization, chatbots, and more.
• Real-world Applications: OpenAI's ChatGPT is an example of a product using GPT for generating conversational responses.

LLM (Language Model)

• Description: This seems to be a generic term for models trained on language data, such as BERT or GPT.
• General Use Cases: Depending on the specific language model, use cases can include text generation, translation, summarization, and more.
• Real-world Applications: Many chatbots and AI assistants use some form of language model to understand and generate text.

RoBERTa (Robustly optimized BERT approach)

• Description: RoBERTa is a variant of BERT that uses a different training approach and has been shown to perform better on certain tasks.
• General Use Cases: Same as BERT, but generally performs better.
• Real-world Applications: Facebook uses RoBERTa for various NLP tasks in its AI systems.

T5 (Text-to-Text Transfer Transformer)

• Description: T5 is a model that treats every NLP task as a text generation task, allowing it to be used for a wide range of tasks.
• General Use Cases: Any task that can be framed as text generation, such as translation, summarization, and question answering.
• Real-world Applications: Google uses T5 in various AI research projects.

XLNet

• Description: XLNet is a generalized autoregressive model that outperforms BERT on several NLP benchmarks. Unlike BERT, XLNet does not discard the sequential nature of the text.
• General Use Cases: Text classification, named entity recognition, sentiment analysis, question answering.
• Real-world Applications: XLNet has been used in various AI research projects and NLP applications.

ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately)

• Description: ELECTRA is a pre-training approach that trains a transformer model as a discriminator rather than a generator, making it more sample efficient than models like GPT and BERT.
• General Use Cases: Similar to BERT, but more efficient.
• Real-world Applications: Google uses ELECTRA for various NLP tasks in its AI systems.