You will get optimized LLMs with deep learning integration for enhanced accuracy.
Project details
This project provides model tuning and deep learning integration to enhance LLM accuracy and decision-making. It includes use case adaptation, performance monitoring, and scalability to fit diverse applications and data complexities.
Project Scope :
- Model Tuning: Optimize various LLMs for specific use cases such as image classification and object identification.
- Deep Learning Integration: Apply deep learning techniques to enhance model accuracy.
- Machine Learning Enhancements: Implement machine learning algorithms for improved decision-making.
- Use Case Adaptation: Tailor models to fit different applications and scenarios.
- Performance Monitoring: Continuously monitor and adjust models for optimal performance.
- Scalability: Ensure models are scalable and adaptable to varying data sizes and complexities.
Achievements :
- Model Accuracy: 98% improvement in classification and identification accuracy.
- 99% enhancement in model performance through deep learning techniques.
- 99% increase in model adaptability to various use cases.
- 15% more efficient performance tracking and adjustments.
- 20% boost in model scalability and data handling capabilities.
Project Scope :
- Model Tuning: Optimize various LLMs for specific use cases such as image classification and object identification.
- Deep Learning Integration: Apply deep learning techniques to enhance model accuracy.
- Machine Learning Enhancements: Implement machine learning algorithms for improved decision-making.
- Use Case Adaptation: Tailor models to fit different applications and scenarios.
- Performance Monitoring: Continuously monitor and adjust models for optimal performance.
- Scalability: Ensure models are scalable and adaptable to varying data sizes and complexities.
Achievements :
- Model Accuracy: 98% improvement in classification and identification accuracy.
- 99% enhancement in model performance through deep learning techniques.
- 99% increase in model adaptability to various use cases.
- 15% more efficient performance tracking and adjustments.
- 20% boost in model scalability and data handling capabilities.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Apache MXNet, Azure Machine Learning, BigDL, deeplearn.js, MATLAB, MLflow, NVIDIA AI Platform, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$25,000
|
Standard
$40,000
|
Advanced
$50,000
|
|---|---|---|---|
| Delivery Time | 30 days | 40 days | 60 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | |||
Detailed Code Comments | - | - | |
Knowledge Graph | - | ||
Model Documentation | - | - | |
Ontology | - | - | |
Source Code | - | - | |
Taxonomy |
Frequently asked questions
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About Chandan
AI/ML/LLM Architect | Deep Learning | Computer Vision | Generative AI
68%
Job Success
Noida, India - 1:04 pm local time
🛠️ We are the group of Technology Enthusiastic Engineers. We provide all technology solutions to your business. We provide multiple type of skilled resources for every Time zone on immediate basis.
Areas of expertise:
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🧠 AI Components: ML, DL, NLP, LLM, Neural Network, Machine Vision, Voice Recognition, Human Intelligence etc.
🤖 LLM Models: chatGPT, Bard/Gemini, PaLM, LLaMA 2, LLaMA 3, Falcon 180B, Bloom, Claude, Cohere, Mistral, t5-flan etc.
🧠 AI Type : Generative AI, Conversational AI, Cognitive AI.
🤖 AI ChatBot: AWS Lex, Google Dialogflow, RASA, IBM Watson Assistant etc.
📡 LLM Serving Framework: vLLM, openLLM, TGI, Triton, Ray Serve, MLC LLM, CTranslate2 etc.
📚 AI Libraries: TensorFlow, Keras, PyTorch, NLTK, Spacy, openCV, coreNLP, Textblob, Scipy, scikit-learn, matplotlib, scikit-image, COCO, YOLO, HRNet etc.
🔧 Frameworks: Langchain, Django, Flask, FastAPI, Spring Boot 2, Microservices, Hibernate etc.
🗄️ Databases: FAISS, Chroma, Pinecone, Milvus, Oracle, MySQL, MongoDB, Sybase, Neo4J, GraphDB etc.
☁️ Cloud Services: Amazon Web Services (AWS), Microsoft Azure, Google Cloud, Oracle Cloud.
🌐 Languages Language: Python, Java, GO, JavaScript, Typescript, ReactJS, AngularJS, Nodejs, JQuery, HTML, CSS.
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Skills Summary:
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• 12+ Years of experience in AI & ML/DL & NLP, LLM Development using various technologies.
• Experience in Cognitive AI, Generative AI, Conversational AI solution implementation.
• Expertise into AI algorithm implementation for supervised/Unsupervised/Re-enforcement Learning and other AI implementation etc.
• Expertise in ML, NLP, LLM, Deep Learning, Online/Incremental learning etc.
• Expertise in opensource LLM tuning and deployment, Model like LLaMA2, Mistral, Bloom, MPT, Gemma etc.
• Expertise in LLM model quantization and Hardware specification (GPU/VRAM/CPU/CORE) for fast LLM inference.
• Expertise in implementation of Large multimodal like Gemini etc. into application solution.
• Expertise in implementation of LLM models like Bert, chatGPT (OpenAI and other model), PaLM, Bard/Gemini AI, LLaMA, Falcon, Bloom, Claude, Cohere etc.
• Expertise in Transfer Learning by using Google Bert Model and another custom model.
• Expertise in ML pipeline and ML cluster on AWS/GCP/Azure.
• Expertise in Model hyper parameters tuning and Model Training.
• Expertise in Unsupervised Topic modeling like LDA, LSA, NMF etc.
• Expertise in Model Training based big data using GPU/TPU Support etc.
• Expertise in Ray cluster for running ML or Non-ML tasks on multiple nodes parallel.
• Experience in dimensionality reduction techniques using PCA and PLS technique.
• Proficiency into Detect patterns using PCA-SVM technique.
• Expertise in Artificial Intelligence Libraries and Modules like NLTK, Spacy, coreNLP, Textblob, Scipy, scikit-learn, Tensorflow, Keras, pytorch, gensim, SymPy, nose, pypiwin32, scikit-image, COCO, YOLO, HRNet etc.
• Expertise in Python Libraries and Modules like matplotlib, numpy, pandas, openpyxl, docx, pptx, csv, linecache, pyautogui, pyhook, pywinauto, scipy, html5lib, py-translate, sys, os etc.
• Proficiency in latest Python and Django and Flask framework.
• Strong database skill using FAISS, Chroma, Pinecone, Milvus, Oracle, MySQL, Mongo DB, Sybase, Neo4J etc.
• Proficiency in Python 2/3 and its Implementation like Jython, Cython etc.
• Expertise in Core Java, J2SE (Swing, AWT, Event), J2EE (Servlet, JSP, JSTL), Struts, Spring, Hibernate, JDBC Technology.
• Expertise in latest Spring Boot framework and Microservices.
• Expertise in AWS Platform like EC2, RDS, VPC, S3, Outposts, ELB, Sagemaker, Security Groups, Lambda Function etc.
• Expertise in Ontology & Semantic Web Data Storage and RDF/OWL mapping.
• Expertise in schema and design semantic data model on OWL, Triples, RDF etc.
• Expertise in Apache Jena Fusuki Server and GraphDB and protege etc.
• Proficiency in latest PySpark framework
• Proficiency in latest Spark and Dask.
• Expertise in Restful and Soap Web Services.
• Expertise in writing the any application architecture from scratch.
• Expertise in code review and code sanitization & code refactoring and testing/debugging etc.
• Expertise in object-oriented programming (oops) and design pattern and design principal implementation.
• Expertise in core and framework level programing for best solution.
• Good knowledge of code optimization, reducing code redundancies and writing clear & loosely coupled code & logic.
• Expertise in writing stories and their points into any sprint and applying agile methodology implementation.
• Have experience in technical team lead and Individual technical contributor as well.
Steps for completing your project
After purchasing the project, send requirements so Chandan can start the project.
Delivery time starts when Chandan receives requirements from you.
Chandan works on your project following the steps below.
Revisions may occur after the delivery date.
Gather Use Cases:
Collect details from the client on use cases and application needs.
Deep Learning Integration
Apply deep learning techniques to enhance model accuracy and decision-making.