You will get a scalable machine learning pipeline
Project details
Machine learning pipelines implement and formalize processes to accelerate, reuse, manage, train, retrain, and deploy machine learning models.The pipeline should include steps that:
• Version your data effectively and kick off a new model training run
• Validate the received data and check against data drift
• Efficiently preprocess data for your model training and validation
• Effectively train your machine learning models
• Track your model training
• Analyze and validate your trained and tuned models
• Deploy the validated model
• Scale the deployed model
• Capture new training data and model performance metrics with feedback loops
• Version your data effectively and kick off a new model training run
• Validate the received data and check against data drift
• Efficiently preprocess data for your model training and validation
• Effectively train your machine learning models
• Track your model training
• Analyze and validate your trained and tuned models
• Deploy the validated model
• Scale the deployed model
• Capture new training data and model performance metrics with feedback loops
What's included
| Service Tiers |
Starter
$2,000
|
Standard
$4,000
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 10 days | 20 days | 30 days |
Number of Revisions | 1 | 4 | 7 |
Number of Graphs/Charts | 20 | 30 | 50 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$200
Additional Model Variation
(+ 7 Days)
+$500
explonatary data analysis
(+ 3 Days)
+$200About Zakaria
Python developer - Machine Learning engineer
Casablanca, Morocco - 9:00 am local time
● Build, train, deploy and monitor machine learning pipelines on different clouds
using TensorFlow extended
● Build Natural Language Processing models using RNNs and Attention
architecture and Transformer architecture
● Build Computer Vision models using CNNs and Transfer learning
● Build Representation learning and generative learning using autoencoders and
GANs
● Build Classification and Regression models using Random forest algorithm,
Xgboost, SVM…..
● Data visualization and Data cleaning
● Data ingesting, Data scraping, Data versioning, Data preprocessing and Data
validation
Technologies and Programming Languages
● Python as a programing language
● Tensorflow and ScikitLearn as a deep learning and machine learning
frameworks
● Pandas, Matplotlib, Seaborn as a data visualization Python libraries
● Numpy as a numerical computing library
● Pyspark as a Large-scale data processing library
● Tensorflow Extended, Apache Beam, Apache Airflow, Kuber Flow
Pipelines as a orchestration and pipelines monitoring tools
● Tensorboard and WIT as a models analyser
● Tensorflow Serving as a models deployer
● GCP and AWS as a cloud computing providers
● Django, Flask and FastAPI as a web development frameworks
● Selenium, BeautifulSoup as a web scraping libraries
● Javascript, HTML, and CSS as a web development programing language
● GIT and DVC as a version control and data controls tools
● Docker as a contenairizer tool
● Streamlit and Tkinter as a graphical user interface libraries
● Postgresql and Mongodb as a Databases technologies
I hope we will have a long-term working relationship and thank you for visiting my profile.
Best regards,
Zakaria Oubaid
Steps for completing your project
After purchasing the project, send requirements so Zakaria can start the project.
Delivery time starts when Zakaria receives requirements from you.
Zakaria works on your project following the steps below.
Revisions may occur after the delivery date.
receive business goals
The client must tell me about the business goal from implementing a machine learning pipeline