You will get AI, ML & RAG solutions in Java using Weka, DL4J and Spark
Project details
Looking to build intelligent systems, tackle complex optimization problems, or unlock the power of machine learning and big data in Java?
With deep expertise in Java, Weka, Deeplearning4J, Apache Spark, and Elasticsearch, I design and implement intelligent systems that deliver scalable insights, automation, and real business value.
What I Offer
• Machine Learning Models: classification, regression and multi-label classification.
• Clustering and Association Learning: meaningful patterns in complex datasets.
• Optimization Algorithms: heuristic and meta-heuristic methods for smarter decisions.
• Dimensionality Reduction and Feature Selection: PCA, LDA, and attribute selection.
• Forecasting Models: time series and correlational forecasting.
• Big Data Pipelines: batch and real-time data processing at scale with Apache Spark.
• Search and Analytics with Elasticsearch.
• Generative AI and RAG Applications: LLM integration and vector databases.
What You Can Expect
• End-to-end solutions built for accuracy, scalability, and performance.
• Solutions aligned with your business goals.
• Transparent communication throughout.
Let's discuss your requirements before you place an order.
With deep expertise in Java, Weka, Deeplearning4J, Apache Spark, and Elasticsearch, I design and implement intelligent systems that deliver scalable insights, automation, and real business value.
What I Offer
• Machine Learning Models: classification, regression and multi-label classification.
• Clustering and Association Learning: meaningful patterns in complex datasets.
• Optimization Algorithms: heuristic and meta-heuristic methods for smarter decisions.
• Dimensionality Reduction and Feature Selection: PCA, LDA, and attribute selection.
• Forecasting Models: time series and correlational forecasting.
• Big Data Pipelines: batch and real-time data processing at scale with Apache Spark.
• Search and Analytics with Elasticsearch.
• Generative AI and RAG Applications: LLM integration and vector databases.
What You Can Expect
• End-to-end solutions built for accuracy, scalability, and performance.
• Solutions aligned with your business goals.
• Transparent communication throughout.
Let's discuss your requirements before you place an order.
Machine Learning Tools
Apache Mahout, Apache Spark, ChatGPT, Deeplearning4j, OpenCV, SQL, Stanford CoreNLP, Tesseract OCR, Weka, Word2vec, XGBoostWhat's included $140
These options are included with the project scope.
$140
- Delivery Time 14 days
- Number of Revisions 3
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
About Emad
Java | Spring | Angular.
Karachi, Pakistan - 1:43 am local time
I started programming in 2017 with C, but it was the Swing Framework (the real culprit) that initially drew me into the Java ecosystem. Interestingly, that was the only time I worked with Swing. Back then there were no LLMs, and that turned out to be a gift. I learned by experimenting, breaking things and finding solutions. Even now, working with libraries like Weka and Deeplearning4J (which I've been using since 2019), I'm reminded how fortunate I was to build a strong foundation before that era. Like any creative craft, whether filmmaking, music, writing or engineering, growth comes from dedication, passion and persistence.
My primary stack revolves around Spring Framework and Angular for web applications and JavaFX for desktop development. Over time, I developed a deep interest in artificial intelligence and machine learning: implementing optimization algorithms (heuristic and meta-heuristic), building ML models and integrating intelligent systems into real products. Along the way, I expanded my expertise into cloud infrastructure and deployment, ensuring the systems I build are scalable, flexible and resilient.
Steps for completing your project
After purchasing the project, send requirements so Emad can start the project.
Delivery time starts when Emad receives requirements from you.
Emad works on your project following the steps below.
Revisions may occur after the delivery date.
Consultation
Discuss the client's business needs, gather relevant datasets or data sources, and define the scope across ML, big data, or RAG requirements.
Data Analysis & Preparation
Evaluate and preprocess the data, including cleaning, normalizing, and handling missing values. For big data pipelines, configure Apache Spark for batch or real-time processing at scale.