During the summer of 2024 I embarked on a journey of successive and intensive projects with tight schedule and limited time to learn as much as possible
Automated tests, Amazon Web Services Machine Learning model, Postman and custom tests
Shell scripting, REST APIs, Kafka producer, Flink data processing and aggregation, developer-api, made a great effort documenting the project
Uses Python, pandas, numpy, Plotly and linear regression to visualize the units sold or sales of a company
June 9th
All projects with their respective completion dates
Machine Learning model to predict customer demand in relation to the price
June 12th
Uses a Flask server and a client, a few Amazon Web Services to provide the user with an interactive frontend to play around with values and predictions of the ML-model
June 15th
10th July
Composed of 6 practical mini-projects that covers different Kubernetes components and command line interface
AWS Sagemaker coupled with Jupyter Notebook to create a ML-model, which is then used for predictions. The testing is automated tests with Bash and Postman
26th July
3rd August
Provides an interactive dashboard that is divided into sections of charts. The data displayed is connected to MySQL database.
With PostgresSQL, Kafka, Zookeeper, and Debezium as docker containers, real-time data is managed and stored in real-time
June 17th
June 30th
Utilizes Kafka as the producer and Flink processor to aggregate the produced data, and then store it in PostgresSQL database
7th August
Reads data of large volumes and systematically process them using Apache Spark, AWS EMR, EC2, Cloud9 Shell and ETL operations
13th August
Takes a string constituting the 'blog topic' as input and generates a blog, of multiple cohesive parapgrahs specific to the blog topic. This was made possible with AWS Bedrock, Cloudwatch, Lambda, API Gateway
Contact the Coder
If you like what you see and believe there's potential for a deal, don't hesitate to reach out to me :)
+46 760338965
joel.mattsson@hotmail.se
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