Data Engineer | ML Engineer
Starting at
$
100
/hrAbout this service
Summary
Machine Learning Services I can provide:
- Predictive modeling: Building models to make predictions about future events or trends based on historical data.
- Image and video analysis: Developing models to recognize objects, detect patterns, and classify images and videos.
- Natural language processing: Building models to understand and process human language, such as sentiment analysis, text classification, and named entity recognition.
- Anomaly detection: Developing models to identify and flag unusual or unexpected data patterns.
- Recommender systems: Building models to make personalized recommendations to users based on their behavior and preferences.
- Fraud detection: Developing models to detect and prevent fraudulent activities such as credit card fraud, insurance fraud, and money laundering.
- Time series forecasting: Building models to predict future values in a time series based on historical data.
- Optimization: Developing models to optimize processes and systems, such as scheduling, resource allocation, and logistics.
- Risk assessment: Developing models to assess and quantify risks in various domains, such as finance, insurance, and healthcare.
- Decision-making: Developing models to support decision-making processes in various domains, such as marketing, sales, and operations.
Data Engineering Services I can provide:
- Data ingestion and extraction from various sources
- Data storage solutions such as databases and data warehouses
- Data processing and transformation using tools such as Apache Spark and Apache Flink
- Data security and privacy, including encryption and access control
- Data visualization and reporting
- Performance tuning and optimization of data system
As an ML Engineer, I have worked on various projects:
=>Experience in Machine Learning Area (Supervised learning Unsupervised learning, Reinforcement learning)
=>Experience in Time Series Forecasting Area (RNNs LSTM Others)
=>Experience in Deep Learning Area (ANN CNN RNN GAN Others)
=>Experience in Computer Vision Area (object detection and Others)
=>Classification Related Problems
=>Regression Related Problems
=>Clustering Related Problems (KMeans, DBSCAN HDBSCAN Hierarchical Clustering and Others)
=>Data Analysis (Pandas Numpy Others)
=>Data Visualisation ( Matplotlib Seaborn)
=>Dimensionality Reduction ( PCA LDA )
=>RL (Policy, Reward and Others)
=>Prediction Related Problems
Can predict your data with different models that are given below:
=>Linear Regression Logistic Regression
=>Decision Tree SVM
=>Naive Bayes KNN
=>KMeans
=>Random Forest
=>Gradient Boosting
=>Others
What's included
Data Engineering Deliverables
The deliverables may vary depending on the project requirements. However, some common deliverables I can provide are: - Data pipelines for the ingestion, processing and storage of data from various sources. - Data warehousing solutions for large-scale data storage and retrieval. - Data quality reports to ensure the accuracy and completeness of data. - Data security and privacy policies to ensure the protection of sensitive data. - Performance metrics and monitoring systems to monitor the performance of data systems. - Documentation of the data architecture, processes, and systems. - Technical support and maintenance of data systems.
Machine Learning Deliverables
The deliverables for a machine learning project can vary depending on the scope and requirements of the project. However, some common deliverables I can provide are: - A well-defined and trained machine learning model - Performance metrics to evaluate the accuracy and effectiveness of the model - A user-friendly interface for interacting with the model - Documentation of the machine learning process, including data pre-processing and feature selection - Presentation of the results and findings of the model, including visualizations and reports - Code and scripts for integrating the model into an existing or new system - Maintenance and technical support for the model - Recommendations for further improvement and optimization of the model - Training and educational materials for users of the model.
Natural Language Processing Deliverables
The deliverables for a natural language processing (NLP) project can vary depending on the scope and requirements of the project. However, some common deliverables I can provide are: - A well-defined and trained NLP model, such as a text classifier, named entity recognizer, or sentiment analysis model. - Performance metrics to evaluate the accuracy and effectiveness of the model. - A user-friendly interface for interacting with the NLP model. - Documentation of the NLP process, including data pre-processing, feature selection, and model training. - Presentation of the results and findings of the model, including visualizations and reports. - Code and scripts for integrating the NLP model into an existing or new system. - Maintenance and technical support for the NLP model. - Recommendations for further improvement and optimization of the NLP model. - Training and educational materials for users of the NLP model. - Examples and demonstration datasets to showcase the capabilities of the NLP model.
Data Integration and ETL Pipelines
The deliverables for Designing and Implementing pipelines for extracting, transforming, and loading (ETL) data from various sources into a centralized data warehouse or data lake can vary depending on the scope and requirements of the project. However, some common deliverables I can provide are: 1. Data Architecture Design - Designing scalable, reliable, and secure data architectures. Selecting the appropriate database systems (relational, NoSQL, time-series, etc.) and storage solutions (data lakes, data warehouses). - Architecting data pipelines for both batch and real-time processing. 2. Data Integration - Developing ETL (Extract, Transform, Load) pipelines to consolidate data from multiple sources into a centralized repository. - Implementing data ingestion frameworks for streaming data and batch data processing. - Creating data APIs for seamless integration of data across systems. 3. Data Quality Management - Establishing data quality frameworks to ensure accuracy, completeness, and consistency of data. - Implementing data validation, cleansing, and deduplication processes. - Monitoring data quality and generating quality reports. 4. Data Governance and Compliance - Developing data governance policies and procedures. - Ensuring data compliance with regulatory requirements (e.g., GDPR, HIPAA). - Implementing data security measures, including encryption, masking, and access controls. 5. Data Warehouse and Data Lake Development - Designing and implementing data warehousing solutions. - Building and managing data lakes for storing structured and unstructured data. - Optimizing data storage for performance and cost efficiency. 6. Data Analytics and Reporting Infrastructure - Setting up analytics platforms and tools. - Developing reporting databases, OLAP cubes, and data marts. - Creating dashboards and reports for business intelligence (BI) purposes. 7 . Cloud Data Engineering - Migrating data infrastructure to the cloud. - Leveraging cloud-native services for data processing, storage, and analytics (AWS, Google Cloud, Azure). - Implementing serverless data processing architectures. 10. Performance Tuning and Optimization - Analyzing and optimizing data storage and retrieval processes. - Tuning ETL processes and database queries for performance. - Implementing caching and indexing strategies to improve system performance. 11. Data Disaster Recovery and Backup - Designing and implementing data backup and recovery strategies. - Ensuring high availability and fault tolerance of data systems. - Conducting disaster recovery drills and maintaining recovery documentation.
Recommendations
(5.0)
Client • Jul 13, 2024
Recommended
Christine is the best contracted software engineer I’ve ever worked with in the last 10 years. Her effort, transparency, and efficiency is far beyond many I’ve worked with before. Would absolutely work with her again and recommend to work with her.
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