AI / ML Model Dev
57Blocks provides AI solutions in model development, deployment, and testing.
We fine-tune models for text, image, video, and time-series data, develop domain-specific LLMs, and optimize pre-trained models.
We design AI pipelines across cloud, on-premise, and hybrid environments, automate infrastructure with MLOps, and ensure model accuracy, robustness, and scalability through testing frameworks.
Our Services
LLMs and Model Evaluation
Train LLMs for finance, healthcare, legal, and retail AI
Build chatbots, assistants, and knowledge retrieval tools
Evaluate performance, detect bias, ensure clarity, and monitor drift
Support DeepSeek, Llama, Qwen, ChatGPT, Claude, and other AI models
NLP and Text Generation
Build NLP for sentiment, topics, and intent recognition
Extract data for semantic search and multilingual NLP processing
Develop tools for writing, summaries, and text generation
Optimize LLMs for prompts, responses, and text output
Computer Vision and Image
Train AI for object detection, image segmentation, and recognition
Improve resolution, reduce noise, and restore images with AI processing
Automate tagging, extract metadata, and improve content discovery
Reduce latency on data loading, preprocessing, and batch processing
RAG-Based Solutions
Build RAG pipelines for context-aware AI and response accuracy
Develop vector indexes for fast, relevant, and up-to-date responses
Domain data with LLMs to enhance targeted insights and retrieval
Maintain AI outputs with continuous content updates and validation
AI Technology Stack
Production Infrastructure
- GCP, Azure, AWS, Runpod, NLPcloud
- Vector DBs - Pinecone, LanceDB
- Model deployment - OctoML
- LLM models - GPT4, Claude, Mistral, GeminiPro
- Observability - Arize
Model Development, Evaluation, and Finetuning
- Python - PyTorch, Tensorflow, SKLearn
- Prompt engineering - Promptlayer, LangSmith
- Data Curation/Annotation - datasaur, Scale, Snorkel
- Data Pre-processing - Cleanlab
- Parallelization Tools i.e. Xmanager & Vizier
Insights From Building
Using just an image on a mobile device, the search application is designed to return matches from the database that are either identical or resemble the original uploaded image. In this blog, we describe the technology behind this powerful functionality.
Image Quality Assessment (IQA), specifically Objective Blind or no-reference IQA, is a crucial function in determining image fidelity or the quality of image accuracy. Further, IQA helps maintain the integrity of visual data, ensuring its accurate representation. In this article, we share an analysis of the best machine learning models that support IQA, including BRISQUE, DIQA, NIMA and OpenCV. We will delve deeper into their operations, the challenges and advantages, and their significance in the ever-evolving field of image quality assessment.
Using just an image on a mobile device, the search application is designed to return matches from the database that are either identical or resemble the original uploaded image. In this blog, we describe the technology behind this powerful functionality.
Image Quality Assessment (IQA), specifically Objective Blind or no-reference IQA, is a crucial function in determining image fidelity or the quality of image accuracy. Further, IQA helps maintain the integrity of visual data, ensuring its accurate representation. In this article, we share an analysis of the best machine learning models that support IQA, including BRISQUE, DIQA, NIMA and OpenCV. We will delve deeper into their operations, the challenges and advantages, and their significance in the ever-evolving field of image quality assessment.
