You will get a well executed Data Science project and Demo
Project details
You will get a full-stack (demo) application that will help non-technical audiences or users to easily interact with or utilize your machine learning model. The application will be deployed on popular platforms such as Heroku, Vercel, or AWS, or whichever one you choose. In my five years of experience building applications for machine-learning models, and creating demonstrations, my clients have indicated an overwhelming boost in their ability to communicate the usefulness of their AI solution more easily to their audience through the help of the application I built for them. Often, a usable application is what differentiates one great ML engineer from another. I will build you a custom application that is tailored to the story you hope to sell to your audience. Don't remain stuck in Jupyter Notebooks or VS code, or pycharm, instead put your solution (whether it is computer vision, NLP, or traditional ML) out there quickly by developing a web application for it today. I am just a message away!!
Machine Learning Tools
Amazon SageMaker, Azure Machine Learning, ChatGPT, Microsoft Excel, NLTK, NumPy, pandas, Python, Python Scikit-Learn, SAS, Scrapy, SQL, Tableau, TensorFlow, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$500
|
Standard
$600
|
Advanced
$750
|
|---|---|---|---|
| Delivery Time | 14 days | 12 days | 7 days |
Number of Revisions | 2 | 3 | 3 |
Number of Model Variations | 2 | 2 | 3 |
Number of Scenarios | 1 | 1 | 3 |
Number of Graphs/Charts | 1 | 1 | 3 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | |||
Source Code | - | - |
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SE
Spyridon E.
Jul 4, 2025
30 minute consultation
CS
Christopher S.
Dec 19, 2024
60 minute consultation
AH
Andrew H.
Nov 16, 2024
Datascraping Project and Advise on DB Refactor
Mustapha is incredible to work with. He assisted in an ocr solution that works perfectly. Communication was stellar, and he went above and beyond to bug test making the end result work flawlessly. Will be working on future projects with him soon.
FR
Frantz R.
Nov 14, 2024
Machine Learning Engineer
Highly skilled ML engineer who built us an video model from scratch.
AH
Andrew H.
Jul 12, 2024
30 minute consultation
Great meeting with Mustapha today. He delivered a very comprehensive list of options to solve our problem. Looking forward to working in the future.
About Mustapha
Machine Learning Engineer | Data Scientist
100%
Job Success
Waterloo, Canada - 9:57 am local time
In my 7+ years of career, I have worn many hats, including Data Scientist, Machine Learning Engineer, and Generative AI Engineer.
My recent experiences:
1. Content recommendation: I have trained and deployed recommender systems to personalize users' experience mostly with Amazon Personalize and Vertex AI Applications (google discovery engine).
2. Experimentation: Worked on Causal Inference (RDD and Propensity Score Matching), A/B testing, Interleaving Recommendations Experiments, and Multi-armed Bandit experiments.
3. Built Generative AI applications, including RAG-based chatbots, Text2SQL, and Text-to-Speech applications. Fine-tuned and deployed open-source LLMs.
4. Document Processing with OCR, ETL, and RPA.
5. Time Series Analysis and Forecasting.
Projects succeed when proper timelines, realistic scopes, and appropriate budgets are set early. Thus, I make it a point to understand the project thoroughly and ensure my client's expectations align with the timeline and budget. The time invested in this planning stage has always paid off. Let's connect today!
Interested in a quick 30-minute or 1-hour consultation to discuss your project before starting a contract? Click on "Book a Consultation" below. During the session, I will share more detailed information about your intended project. We will also discuss the feasibility, cost, and tentative timeline. Relevant documents will also be shared after the meeting.
DETAILED EXPERIENCES and SKILLS:
Data Science | Machine Learning | Deep Learning Expertise:
Text extraction and Processing (OCR): Google Document AI, Azure AI Document Intelligence, Amazon Textract.
Data Engineering: Databricks, Apache Spark, SQL databases
Regression - Univariate and Multivariate
Time series analysis and forecasting: ARIMA, SARIMAX, VAR, VECM, LSTM, and Facebook Prophet.
Classification: Logistic Regression, Decision Trees, Random Forest, Xgboost, Naive Bayes, SVM, Shallow, and Deep Neural Networks, and Ensemble of Models.
Unsupervised Learning: K-means clustering (Scikit and FAISS), Topic Modeling (including NMF Frobenius norm and KL divergence, LDA, and LSA), Dimensionality reduction (including MDS, UMAP, PCA, t-SNE, LSTA, LDA, and Hessian, and Truncated SVD)
Tools and Frameworks: Python (Numpy, Pandas, Scikit-Learn, Keras, PyTorch, Tensorflow, and BeautifulSoup)
Data Visualization: D3.js, Python (Matplotlib, Seaborn, Plotly), and Tableau
Platforms: Amazon Sagemaker, Google Cloud (Vertex AI), Azure ML
Tools: Docker, Amazon Glue.
Generative AI Expertise:
I have built and integrated conversation AI applications into existing applications for businesses. Some of the specific cases include:
1. A generative AI application for a beauty brand that generates product images, captivating product descriptions, and beauty models before and after transformation on the landing page of the client's website. The generated content is automatically tailored to the customer's profile, especially demographics and purchase history.
⚙️Tools and services used: Google Cloud Platform, Vertex AI, Stable diffusion, PaLM-2 (text-bison)
2. An intelligent shopping assistant for a famous retail brand. The assistant leverages a combination of agents to achieve various tasks, including fetching inventory information from the knowledge graph (Neo4j on a Neptune Instance) based on a user query, responding to FAQs by leveraging context from the Kendra database, simultaneously extracting possible lead entities from the conversation and enriching the knowledge graph, driving the conversation towards making a sale.
⚙️Tools and services: Amazon Bedrock, Amazon Kendra, Amazon Neptune, AWS EC2, and Sagemaker.
Others include a content generator bot for Atlassian confluence, LLM powered telegram bot, etc.
NLP and GENERATIVE AI (Image Text, and Multimodal):
Prompt Engineering for LLMs including both open and closed source: Mixtral, Open AI GPTs, Google PaLM-2, LLaMa, Claude, Amazon Titan, Claude, Stable diffusion, DALLE-3, and Imagen.
Text Preprocessing and Sentiment Analysis, Intent Classification, Document Classification: NLP toolkit, BERT
Topic Modeling and Text Classification
Named Entity Recognition, Entity Extraction: Python Pydantic, Open AI function calling, and SpaCy, etc.
Speech-to-Text, Text-to-Speech, and Machine Translation,
Tools, Frameworks, and Techniques: LangChain, HuggingFace, Chain of thought (COT) Reasoning, and Retrieval Augmented Generation (RAG).
Store and Knowledge base: 🛢️Vector Databases: Amazon Kendra, Chroma, and Pinecone; Knowledge graph: Amazon Neptune (Neo4j).
Others:AWS+GCP+Azure
Steps for completing your project
After purchasing the project, send requirements so Mustapha can start the project.
Delivery time starts when Mustapha receives requirements from you.
Mustapha works on your project following the steps below.
Revisions may occur after the delivery date.
Receive requirements from client
The requirements could include your clear specification of what is needed, model files (e.g .pkl, .sav, etc), and/or sample dataset.
Provide feedback to client and discuss deliverables
Here, the client and I will discuss the project timelines in light of the requirements, costs, milestones, and meeting schedule.