You will get AWS Sagemaker ML-OPS Data Science services
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Project details
I am happy to have you here:
I am a veteran Data Scientist and I will do Machine Learning/ Data Science/ Deep learning related tasks on AWS Sagemaker.
The tasks that I do include, but are not limited to:
NLP: Transfer learning & fine tuning transformers (BERT/GPT) for zero shot, speech recognition, sentence completion, text generation, Q&A, summarization, sentiment analysis, NER, paraphrase detection, translation, etc.
Time Series Analysis & Forecast: Statistical modeling, econometrics (BJ- ARMA, ARIMA, ARCH, GARCH, VAR, etc. ), & Deep Learning (LSTM, GRU, RNN, Transformers, ANN, RF, SVM, etc.) to make well-informed business forecasts.
Computer Vision: Object detection & tracking, OCR, image processing, classification, segmentation, etc. using CNN, VGG, ResNet, MobileNet, YOLO, etc.
ML & DL: Data cleaning, transformation, EDA, modeling (supervised & unsupervised).
AutoML
Note:
Please message me before ordering:)
The client must provide AWS credentials and the quoted price is exclusive of the training and the deployment cost, if any.
#SAGEMAKER #DATASCIENCE #ML #DL #MACHINELEARNING #NLP #COMPUTERVISION #TIMESERIES #AWS #SAGEMAKER #AWS SAGEMAKER #MLOPS
I am a veteran Data Scientist and I will do Machine Learning/ Data Science/ Deep learning related tasks on AWS Sagemaker.
The tasks that I do include, but are not limited to:
NLP: Transfer learning & fine tuning transformers (BERT/GPT) for zero shot, speech recognition, sentence completion, text generation, Q&A, summarization, sentiment analysis, NER, paraphrase detection, translation, etc.
Time Series Analysis & Forecast: Statistical modeling, econometrics (BJ- ARMA, ARIMA, ARCH, GARCH, VAR, etc. ), & Deep Learning (LSTM, GRU, RNN, Transformers, ANN, RF, SVM, etc.) to make well-informed business forecasts.
Computer Vision: Object detection & tracking, OCR, image processing, classification, segmentation, etc. using CNN, VGG, ResNet, MobileNet, YOLO, etc.
ML & DL: Data cleaning, transformation, EDA, modeling (supervised & unsupervised).
AutoML
Note:
Please message me before ordering:)
The client must provide AWS credentials and the quoted price is exclusive of the training and the deployment cost, if any.
#SAGEMAKER #DATASCIENCE #ML #DL #MACHINELEARNING #NLP #COMPUTERVISION #TIMESERIES #AWS #SAGEMAKER #AWS SAGEMAKER #MLOPS
Machine Learning Tools
Apache Spark, Apache Spark MLlib, BERT, ChatGPT, Databricks Platform, GPT-3, H2O, Keras, Microsoft Excel, NLTK, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, R, RapidMiner, scikit-learn, SciPy, SQL, Stanford CoreNLP, Tableau, TensorFlow, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$95
|
Standard
$135
|
Advanced
$185
|
|---|---|---|---|
| Delivery Time | 4 days | 5 days | 6 days |
Number of Revisions | 0 | 0 | 0 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code |
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Farhan is gifted and resourceful; he worked his magic to make this project work as expected. I highly recommend Farhan!
About Farhan
AI Solutions Architect | AI-First SaaS | Multi-Agent | Full-Stack
100%
Job Success
Karachi, Pakistan - 10:20 am local time
I architect and deploy production-grade AI systems end-to-end. Expert in agentic AI, Model Context Protocol (MCP), and agent-to-agent (A2A) orchestration. Recent work: HIPAA-compliant healthcare platforms (thousands of patients), multi-tenant trading SaaS (millions in volume), voice AI agents, legal document intelligence, energy trading systems.
Core Strengths:
- AI-First Approach: Select optimal solution per problem—classical ML for latency, fine-tuned models (Unsloth/LoRA/QLoRA: Qwen, Gemma, LLaMA) for privacy/control, managed APIs for scale. Decisions grounded in real tradeoffs, not hype.
- End-to-End Ownership: Full stack—AI/ML models, microservices, React/Next.js, cloud infrastructure (AWS/Azure/GCP), DevOps. Database design to production, no handoffs.
- Agentic AI & Multi-Agent Systems: Design sophisticated multi-agent workflows using LangGraph, CrewAI, OpenAI Agents SDK, or custom orchestration. Expert in agent coordination, state management, A2A protocols, seamless handoffs.
- MCP Mastery: Architect MCP-native systems for dynamic tool discovery and integration. Leverage universal standard (97M+ downloads) for rapid integration, reduced deployment friction (11 min vs. 3 days), production reliability.
- Tool Calling & Dynamic Reasoning: Implement context-aware tool invocation for APIs, databases, services. Real-time slot booking, insurance verification, trade execution via intelligent tool chains.
Production Systems Built:
- Healthcare Orchestration: 7+ agents (registration, triage, diagnostics, documentation, compliance) with MCP integration, EMR (Cerner/Oracle), HIPAA-compliant, thousands of interactions. Stack: Python, FastAPI, React, MS SQL, LangGraph, MCP, Azure.
- Voice AI Appointment System: Inbound/outbound calling agent, eligibility screening (40+ dynamic questions), real-time slot booking via tool calls, insurance verification, authorization checks. Stack: Pipecat AI, LiveKit, Twilio, Deepseek, LangGraph, FastAPI, RabbitMQ, React, MS SQL, Azure.
- Legal Intelligence Platform: AI contract analysis, clause extraction, risk flagging, compliance detection. Multi-modal (PDFs, images), RAG semantic search, domain fine-tuning, multi-agent workflow (ingest → classify → extract → analyze → summarize). Stack: Python, FastAPI, React, PostgreSQL, Pinecone, LangGraph, Claude, LoRA.
- Algorithmic Trading SaaS: LSTM/GRU/Prophet forecasting, real-time signals, agentic market analysis, sentiment processing, trade recommendations. Kubernetes auto-scaling, sub-second latency. Stack: Python, FastAPI, React, PostgreSQL, Redis, Kafka, LangGraph, Kubernetes, AWS.
- Energy Trading System: Position limits, surveillance, ICE/CME OTC integration. Agentic risk assessment, pattern recognition, compliance monitoring. Stack: .NET Core, React, PostgreSQL, Kafka, Redis, LangChain, Pinecone, Azure.
Technical Foundation:
Backend: Python/FastAPI, Node.js, .NET Core | Frontend: React, Next.js | Databases: PostgreSQL, MS SQL, MongoDB | Caching: Redis, Kafka | Vector: Pinecone, Milvus, Elasticsearch, ChromaDB | AI Frameworks: LangGraph, CrewAI, OpenAI SDK, MCP, A2A | LLM Fine-Tuning: Unsloth, LoRA, QLoRA, PEFT | LLMs: Claude, Gemini, Deepseek, OpenAI | Cloud: AWS, Azure, GCP + Terraform, Docker, Kubernetes | ML/AI Platforms: Azure AI Foundry, AWS Bedrock, Vertex AI, Hugging Face | Vision/NLP: YOLOv11/v12, BERT, spaCy | Integrations: Twilio, Slack, Airtable, Make, n8n, EMR, trading platforms.
Work Style:
Measure, iterate, optimize. Balance business requirements with technical constraints. Help clients understand tradeoffs. Fluent in technical depth and business translation. Trusted by founding teams, CTOs, architects for validation and time-to-market acceleration.
MS in Data Science + 7 years across healthcare, finance, trading, energy, SaaS. Solve problems holistically—scalability, compliance, performance, UX from day one.
Proven track record: Production-grade, revenue-generating AI systems. Top Rated Plus on Upwork.
Steps for completing your project
After purchasing the project, send requirements so Farhan can start the project.
Delivery time starts when Farhan receives requirements from you.
Farhan works on your project following the steps below.
Revisions may occur after the delivery date.
Train the model and deliver source code
Delivery of the source code