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diff --git a/content/_index.md b/content/_index.md index c57b808..7fbe911 100644 --- a/content/_index.md +++ b/content/_index.md @@ -1,6 +1,6 @@ --- title: "Gustaf Rydholm's Webpage" -description: "tbc" +description: "Landing Page" --- ## Introduction @@ -14,6 +14,7 @@ book reviews/notes, cv, and digital contact information. ### On this website... - [Projects](/projects) +- [Notes](/notes) - [CV](/cv) - [Contact](/contact) diff --git a/content/cv.md b/content/cv.md index bf01d14..812cbf8 100644 --- a/content/cv.md +++ b/content/cv.md @@ -134,51 +134,50 @@ tbc... *Keywords: Microservices, Kubernetes, Infrastructure, Helm, CI/CD, Kotlin, Spring, AWS, Terraform* -- Machine Learning Engineer. Saab AB. Aug 2018 -- Aug 2021 - - Intelligence Application. Aug 2020 -- Aug 2021 - - Created data mining pipelines for extracting, cleaning, and creating - datasets for machine learning models, i.e. train/test sets. - - Was a driving force in making the system more asynchronous using - message passing between microservices, by deploying and maintaining a - Kafka instance. This improve the architecture by allowing multiple - services to subscribe to incoming sensor data and process the - information, instead of fetching the data from databases in batches. - - Developed a modular pipeline for training and evaluating deep learning - models with different architectures and/or losses. Automatic extraction - of the best model based on user defined metric, ready for serving. - - Built and deployed deep learning models for multi-modal trajectory - predictions in production. - - Took initiative and deployed and maintained a private Python Package - Index (PyPI) for all developers. Greatly improving the development - workflow, e.g. forcing versioning, and reducing/eliminating cross - dependencies between locally developed Python packages. - - Developed a graph algorithm for sensor fusion. Deployed it as a - microservice listing to incoming sensor data. This enabled more complex - pattern analysis in downstream services. - - Reduced the docker image size of the Python microservices from ~2 GB - to ~73 MB by utilizing multistage builds and alpine base images. - - Built pipelines for CI/CD and packages deployment in Tekton. - - With my docker images and pipelines we where able to reduce the - average build times from ~10-30 minutes down to seconds, mostly thanks - to improved caching capabilities. - - *Keywords: Deep Learning, Python, Microservices, Kubernetes, - Infrastructure, Helm, CI/CD* - - - Radar Warning Receiver. Aug 2018 -- Aug 2020 - - Built simulation software for generating realistic signal - environments with both radar and/or communication signals. Implemented - the most common signal encoding for communications, as well as - basic to SOTA radar modulations. This enabled the team to develop and - evaluate different machine learning models and ideas. - - Researched machine learning models in different stages of the radar - warning receiver, with regards to compute and data limitations. - - Held in several presentations of machine learning papers in a company - reading group. - - Shared the knowledge of implementing and using machine learning to - multiple business areas within Saab. - - *Keywords: Deep Learning, Signal Processing, Python, VHDL* +- Machine Learning Engineer (Cyber Security). Saab AB. Aug 2020 -- Aug 2021 + - Created data mining pipelines for extracting, cleaning, and creating + datasets for machine learning models, i.e. train/test sets. + - Was a driving force in making the system more asynchronous using + message passing between microservices, by deploying and maintaining a + Kafka instance. This improve the architecture by allowing multiple + services to subscribe to incoming sensor data and process the + information, instead of fetching the data from databases in batches. + - Developed a modular pipeline for training and evaluating deep learning + models with different architectures and/or losses. Automatic extraction + of the best model based on user defined metric, ready for serving. + - Built and deployed deep learning models for multi-modal trajectory + predictions in production. + - Took initiative and deployed and maintained a private Python Package + Index (PyPI) for all developers. Greatly improving the development + workflow, e.g. forcing versioning, and reducing/eliminating cross + dependencies between locally developed Python packages. + - Developed a graph algorithm for sensor fusion. Deployed it as a + microservice listing to incoming sensor data. This enabled more complex + pattern analysis in downstream services. + - Reduced the docker image size of the Python microservices from ~2 GB + to ~73 MB by utilizing multistage builds and alpine base images. + - Built pipelines for CI/CD and packages deployment in Tekton. + - With my docker images and pipelines we where able to reduce the + average build times from ~10-30 minutes down to seconds, mostly thanks + to improved caching capabilities. + + *Keywords: Deep Learning, Python, Microservices, Kubernetes, + Infrastructure, Helm, CI/CD* + +- Machine Learning Engineer (Surveillance). Saab AB. Aug 2018 -- Aug 2020 + - Built simulation software for generating realistic signal + environments with both radar and/or communication signals. Implemented + the most common signal encoding for communications, as well as + basic to SOTA radar modulations. This enabled the team to develop and + evaluate different machine learning models and ideas. + - Researched machine learning models in different stages of the radar + warning receiver, with regards to compute and data limitations. + - Held in several presentations of machine learning papers in a company + reading group. + - Shared the knowledge of implementing and using machine learning to + multiple business areas within Saab. + + *Keywords: Deep Learning, Signal Processing, Python, VHDL* ### Institutions diff --git a/static/style.css b/static/style.css index 87b1b38..d2df580 100644 --- a/static/style.css +++ b/static/style.css @@ -1,7 +1,7 @@ body { - background: #073642 ; - color: brown ; - max-width: 950px ; + background: #151515 ; + color: #E1E1E1 ; + max-width: 750px ; margin: auto ; padding: 0 16px ; margin-bottom: 500px ; @@ -10,23 +10,15 @@ body { } main { - max-width: 950px ; + max-width: 750px ; margin: auto ; } main > article { - background: #EEE8D5 ; - color: #151515 ; + background: #151515 ; + color: #E1E1E1 ; margin-bottom: 0 ; - padding-top: 4em ; - padding-left: 8em ; - padding-right: 8em ; - padding-bottom: 4em ; - border-color: #1e4e76 ; - border-width: 0.25em 0.25em 0.25em .25em ; - border-style: solid ; text-align: justify; - border-radius: 0 ; margin: auto ; } @@ -57,20 +49,16 @@ ul { footer { max-width: 300px ; - background: #EEE8D5 ; + background: #151515 ; margin-left: auto ; margin-right: auto ; - margin-top: -4px ; + margin-top: 6px ; text-align: center ; clear: both ; - border-color: #1e4e76 ; - border-width: 0em 0.25em 0.25em 0.25em ; - border-style: solid ; - border-radius: 0 0 0 0 ; } code { - color: #373737 ; + color: #F4BF75 ; overflow-wrap: break-word ; font-size: 10pt ; } |