AI Developer for ML Models

@AwaisChaudhry

Hi there, I’m Muhammad Awais. I can lead end-to-end time-series ML work from clean-up to production. I will start by engineering a clean, well-documented dataset, detailing every transformation. Then I’ll design and iteratively improve forecasting or anomaly-detection models using Python (pandas, NumPy, scikit-learn, TensorFlow or PyTorch). The model will beat a naive baseline and be packaged for deployment as a RESTful FastAPI service or a Docker image, with clear setup steps and inline, well-commented code (notebooks plus modular scripts). I’ll include a brief feature-importance report and relevant performance metrics (MAE, RMSE, etc.). Finally, I’ll provide a one-click deployment guide and ensure the solution slots into your automation pipeline. What is your current data ingestion pipeline and preferred deployment stack for the initial rollout? What I’ll deliver: - Reproducible, clean notebooks and modular scripts - Production-ready API or containerized image - Documentation on data transformations, features, and performance - Short deployment guide and a basic CI-friendly setup Proposed bid and timeline: bidAmount: 20, duration: 7 days. Best regards,

@Microlent

Hello! Your project focusing on time-series forecasting and anomaly detection using Python ML tools is clear. I have worked on similar pipelines where the process moves from raw data preparation to a deployable model integrated with APIs. My typical workflow includes: • Data preparation and feature engineering using pandas and NumPy, documenting transformations so the dataset remains reproducible • Building forecasting or anomaly-detection models using scikit-learn, TensorFlow, or PyTorch, depending on the problem • Evaluating models using metrics such as MAE, RMSE, and baseline comparisons to ensure meaningful improvement • Delivering clean notebooks and modular scripts with clear documentation • Packaging the model for deployment via FastAPI or a Docker container for integration into larger systems Deliverables will include model training code, performance reports, feature importance analysis, and a deployment-ready service with setup instructions. Quick question: what type of time-series data are you working with (financial, operational metrics, sensor data, etc.), and how frequently is the data sampled? Best regards Jasmin

@ZohaibRoy

⭐⭐⭐⭐⭐ Lead AI Engineer for End-to-End Machine Learning on Time-Series Data ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and noticed you're looking for an AI engineer to lead your machine learning projects. Look no further; Zohaib is here to assist you! My team has successfully completed 50+ similar projects in machine learning. I will prepare time-series data, build predictive models, and ensure everything is well-documented and easy to maintain. ➡️ Why Me? I can easily handle your machine learning project as I have 5 years of experience in Python, time-series analysis, and model development. My expertise includes data cleaning, model training, and performance evaluation. Additionally, I have a strong grip on libraries like TensorFlow and scikit-learn, ensuring a robust solution for your needs. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Python Programming ✅ Time-Series Analysis ✅ Data Cleaning ✅ Model Training ✅ Feature Engineering ✅ Performance Metrics ✅ API Development ✅ Docker Deployment ✅ Data Visualization ✅ Scikit-Learn ✅ TensorFlow ✅ FastAPI Waiting for your response! Best Regards, Zohaib

@neerajdhameliya

Hello, I’ll transform your raw time-series feeds into a production-ready engine by focusing on robust feature engineering, addressing seasonality and stationarity, to ensure your model consistently outperforms any naive baseline. I’ll provide modular Python scripts using pandas and scikit-learn for reproducible cleaning, followed by iterative forecasting using XGBoost or PyTorch LSTMs, fully documented via SHAP and MAE metrics. For deployment, I’ll deliver a containerized FastAPI service with a one-click Docker setup. Having recently built an anomaly detection system that reduced errors by 22%, I can offer fluent communication in English. Is your data feed currently static, or will we need to handle real-time streaming? Best, Niral

@OutsourceMan

⭐⭐⭐⭐⭐ Leverage CnELIndia’s expertise in end-to-end AI project delivery to clean, preprocess, and document time-series datasets efficiently, ensuring model-ready data. Utilize Raman Ladhani’s experience in Python ML libraries (pandas, NumPy, scikit-learn, TensorFlow, PyTorch) to design, train, and optimize forecasting and anomaly-detection models that outperform naive baselines. Implement modular, well-commented code with clear notebooks and scripts, including detailed feature importance analysis and performance metrics (MAE, RMSE). Package solutions for deployment via RESTful FastAPI or Docker with one-click setup, enabling seamless integration into larger automation pipelines. Ensure reproducible, maintainable workflows with strong version control, API integration, and multilingual communication (English and Serbian). Optionally advise on operationalizing LLM components to complement classic ML pipelines, accelerating project readiness and production deployment.

@divumanocha

Hello, I came across your project and found it truly interesting. With over eight years of hands-on experience in this field, I have successfully delivered high-quality solutions to clients worldwide. My dedication to excellence is reflected in the 180+ positive reviews from satisfied clients. I’d love to bring this expertise to your project and ensure outstanding results. However, I do have a few important points I’d like to clarify to align perfectly with your vision. Let’s connect via chat so I can share relevant examples of my past work. I look forward to hearing from you. Best Regards, Divu.

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Software Architecture, CUDA, Machine Learning (ML), NumPy, Model Deployment, Time Series Analysis, AI Development and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@Rekhathakur

Dear Client, Drawing on my years of experience as a versatile AI developer, I have delivered numerous end-to-end Machine Learning (ML) solutions focusing on time-series data similar to your project's needs. My competency lies in curating clean, well-documented code that is not only easy to maintain but exceeds clients' expectations. Reproducibility and maintainability are essential in every project scenario. In line with your requirement for clear documentation and MAE or RMSE focused performance metrics, it is central to my workflow to provide clean code (packaged as a notebook plus modular scripts) alongside comprehensive reports reflecting feature importance and performance measures - this approach simplifies future comprehension and seamless integration into larger pipelines, emphasizing my core ML craftsmanship. Additionally, my Full Stack AI/Machine Learning developer profile empowers me with the skills essential for your project including API familiarity that will be critical for aligning this model to a larger automation pipeline. My fluency in English and Serbian, coupled with strong communication skills ensure there will not be any cultural or linguistic hitches. I'm particularly a pro-active problem solver who can work independently but also believes in the importance of regular communication throughout a project!!! Thank you!!!

@kamran2012

Hi there, I will build your time-series pipeline from raw data ingestion through to a deployed, production-ready predictive model. This covers data cleaning and feature engineering with full transformation documentation, model training and iteration (forecasting or anomaly detection), and a packaged FastAPI service with Docker for one-click deployment. For time-series work like this, I will structure the feature engineering around lag features, rolling statistics, and calendar-based encodings before model selection. Starting with a LightGBM baseline often gives a strong benchmark quickly, then layering in LSTM or Transformer-based approaches where the data warrants it. This makes the iteration loop faster and keeps the final comparison honest against that naive baseline. All code will be delivered as modular Python scripts alongside a notebook walkthrough, with a performance report covering MAE, RMSE, and feature importance breakdowns. The API layer will be designed with clean input/output schemas so it plugs directly into your automation pipeline without rework. Questions: 1) What is the approximate size and frequency of the time-series data (e.g., minute-level, daily, number of features)? 2) Is the primary goal forecasting future values or detecting anomalies, or both? Looking forward to discussing further. Thanks and best regards, Kamran

@Julian29

Hello, I have reviewed your requirements and can take the lead on end-to-end Machine Learning for time-series data. I have 10+ years of experience in Python, ML pipelines, and deploying predictive models. Approach: Clean, preprocess, and engineer the time-series dataset, documenting all transformations for reproducibility. Design, train, and iterate on forecasting or anomaly-detection models using pandas, NumPy, scikit-learn, TensorFlow, or PyTorch, ensuring performance surpasses naive baselines. Deliver clean, modular, and well-commented code with notebooks, scripts, and a brief report on feature importance and evaluation metrics (MAE, RMSE, etc.). Package the model for deployment via FastAPI or Docker, including a one-click setup guide for easy integration into your automation pipeline. Optional: Incorporate API endpoints for real-time predictions or downstream automation. I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE, WE WILL WORK WITH AGILE METHODOLOGY AND WILL GIVE YOU ASSISTANCE FROM ZERO TO PUBLISHING ON STORES. With my experience building production-ready ML systems, I can deliver a robust, maintainable solution efficiently. I eagerly await your positive response. Thanks.

@quan9824

As an experienced AI developer and Machine Learning (ML) expert, I would be an excellent fit for your project. Over the years, I have spearheaded multiple end-to-end ML projects focusing on time-series data, similar to what you need. My proficiency lies in efficiently preparing and engineering datasets, designing and training robust predictive models, and delivering clean, well-commented code just as your project demands. I am well-versed in the core tools and libraries of Python such as pandas, NumPy, scikit-learn, TensorFlow, and PyTorch - all of which would be central to your project. Alongside this familiarity with the core stack, I also bring a knack for introducing new tools where necessary without compromising reproducibility or maintainability. Furthermore, I am skilled in packaging solutions for deployment through RESTful FastAPI services or Docker images with ease. Given your requirement for strong API familiarity and my previous experience operationalizing similar ML models, I assure you that I understand not only the art of building quality models but also how they integrate into larger automation pipelines. My turnaround time has always been swift while maintaining top-notch quality work that delights my clients. Let's discuss further how we can make your ML project have an edge!

@toriquldev123

Hello Dear, I am Engineer Toriqul Islam, a B.Sc. graduate in Computer Science & Engineering from Rajshahi University of Engineering & Technology (RUET) with over 10 years of experience in full-stack and Python-based development. I reviewed your time-series ML project and can prepare and engineer datasets, build forecasting or anomaly detection models using Python libraries such as pandas, NumPy, scikit-learn, TensorFlow/PyTorch, and deliver a production-ready solution packaged via FastAPI or Docker for seamless integration. Why Choose Me: • Strong Python & Machine Learning development experience • Expertise in time-series data preprocessing and feature engineering • Experience with model training, evaluation (MAE, RMSE), and optimization • API-ready deployment using FastAPI or containerized Docker services • Clean, modular, well-documented code and technical reports • Reliable communication and timely delivery You are cordially welcome to discuss your dataset, modeling approach, and deployment requirements. Thank You! Best Regards, Toriqul Islam

@avinashdubeyg209

Hi there, I am an ML engineer. I can start right away and deliver within the deadline. So, Let’s have a quick conversation. I can be more specific once we get all the requirements and information required to execute the project. Thank you!!

@vishal1145

Your time-series model will fail in production if you don't account for data drift and retraining triggers. I've seen forecasting systems degrade from 92% accuracy to 68% within three months because no one built monitoring into the pipeline. Before architecting the solution, I need clarity on two things - what's your data refresh cadence (hourly, daily, batch uploads), and are you expecting this model to retrain automatically or flag when performance drops below a threshold? These decisions fundamentally change the deployment architecture. Here's the technical approach: - PYTHON + PANDAS: Build a feature engineering pipeline with lag variables, rolling statistics, and Fourier transforms for seasonality - all versioned in modular scripts so your team can reproduce results six months from now. - PYTORCH + NUMPY: Train LSTM or Transformer-based models with early stopping and cross-validation on time splits (not random splits, which leak future data). I'll benchmark against ARIMA and Prophet to prove the neural approach justifies the complexity. - FASTAPI + DOCKER: Package the inference endpoint with Pydantic validation, health checks, and logging middleware. The container will include model versioning so you can roll back if a new deployment underperforms. - CUDA OPTIMIZATION: If you're processing high-frequency data (tick-level or sub-minute intervals), I'll implement GPU-accelerated preprocessing to cut batch inference time from minutes to seconds. I've built three production time-series systems - one for fraud detection that processed 2M transactions daily, another for demand forecasting that reduced inventory costs by 18%. I don't take on projects where the data quality is unknown. Let's schedule a 20-minute call to review your dataset structure and discuss edge cases like missing timestamps or irregular intervals before committing to a timeline.

@webmasters486

As a talented machine learning (ML) specialist, I believe I’m the perfect fit for your current AI Developer project. With proven skills in data transformation, model design, and deployment through relevant technologies like TensorFlow and PyTorch, my approach has consistently outperformed naive baselines. Additionally, my command over Python, particularly in using libraries like pandas, NumPy, and scikit-learn, are second to none. This ensures that the product will be both reproducible and easy to maintain. One of my key strengths is my penchant for delivering clean code that's comprehensively commented and comes with detailed reports on feature importance and performance metrics - precisely what you're seeking. What’s more? I'm familiar with operationalising ML models which is a bonus for this project; however, regardless of prior experience or not, I've built my career on 'classic ML craftsmanship' which aptly aligns with the core requirement here. My recent time-series project showcases not only expertise but also deep-dive into your requirements, as well as details on recently preferred libraries & typical turnaround time that will undoubtedly enlighten and assure you of my suitability for the tasks at hand. In a nutshell, as Muhammad, a client-oriented professional dedicated to turning your vision into reality. Looking forward to discussing your project further!

@johnallenvillan1

Hello, I noticed your emphasis on building a fully reproducible pipeline for time‑series forecasting and deploying it via FastAPI or Docker, which tells me reliability matters as much as model accuracy. In my last two ML engagements, I delivered a forecasting model for an IoT platform that cut baseline MAE by 32%, and a Dockerised anomaly‑detection service that plugged cleanly into an existing automation pipeline. The real challenge here isn’t just model performance, it’s creating a documented, traceable transformation flow so that future iterations don’t break, and ensuring deployment artifacts behave consistently across environments. I’ll structure the data engineering in notebooks with mirrored modular scripts, build and iterate TensorFlow or PyTorch forecasting models, benchmark against naive baselines, and package the final model as a FastAPI service with a one‑click Docker setup. Before starting, I’d like to confirm your preferred deployment target and whether GPU acceleration is required. I can begin immediately and deliver a first working model within a few days. Best regards, John allen.

@DigitaSyndicate

Time-series projects often harbour subtle pitfalls in data integrity and pipeline reproducibility that, if overlooked, can lead to erroneous forecasts and costly operational failures. Your requirement to handle end-to-end machine learning workflows in Python, with libraries such as pandas, TensorFlow, and scikit-learn, makes it essential to maintain rigorous documentation and modular code structure to ensure maintainability and clarity. At DigitaSyndicate, a UK-based agency, we don't just write code; we architect infrastructure to protect your investment. Our commitment to local UK accountability guarantees adherence to the highest standards, ensuring your model's reliability and operational stability in production. Have you considered how your current API design manages failure modes during model integration within the broader automation pipeline, especially regarding latency and scalability under production loads? Casper M. DigitaSyndicate

@mkh559a5a1fd66e9

With my extensive knowledge and expertise in Full Stack Development, Machine Learning and Python, I am confident in my ability to excel as your AI Engineer for this time-series project. My 14 years of experience have equipped me with a mastery of relevant tools such as pandas, NumPy, scikit-learn, TensorFlow and PyTorch - indespinsable skillset for the success of this role. Notably, I value data quality and reproducibility - qualities that align with your need for a stack that is not only efficient but also easy to maintain. In addition to building robust applications, I have hands-on experience in operationalising ML models - an addeed competence which can better ensure flawless integration of AI models to your existing automation pipeline.I have often sought code clarity and maintainability- inclusing thorough documentation- skills that will benefit your team greatly. Finally, noting that strong communication in English and Serbian is necessary, I assure you of my fluency in these languages. Having successfully developed and trained various models as part of n8n workflow etc. my turnaround time won't overshadow the quality. Hence, I profoundly believe that my skills, experiences and these examples make me the perfect candidate for your project. Let's collaborate to not just deliver an exceptional AI model but also create a seamless deployment process!

@shakilan4

Hi, I can help build and deploy your time-series ML pipeline using Python with tools like pandas, NumPy, scikit-learn, and TensorFlow/PyTorch. I’ll handle data preparation, model development, evaluation, and deliver clean, well-documented code packaged for deployment via FastAPI or Docker with clear setup instructions. Best regards, Shakila Naz