Blake Byerly
Verified Expert in Engineering
Machine Learning Developer
Seattle, WA, United States
Toptal member since December 11, 2018
Blake possesses both startup and large enterprise experience leveraging machine learning (deep learning and classical techniques) to drive value. He has applied machine learning to a variety of problems, including network event correlation, incident forecasting, resource-constrained scheduling, and eCommerce applications at scale.
Portfolio
Experience
- Machine Learning - 5 years
- Python - 4 years
- Docker - 3 years
- Apache Spark - 3 years
- Java - 3 years
- Kubernetes - 3 years
- Google Cloud Platform (GCP) - 3 years
- Bash - 3 years
Preferred Environment
MacOS
The most amazing...
...enterprise applications (terabytes of data) I've migrated were from Dataproc (Google cloud-managed spark) to a GKE in-house solution leveraging Spark-on-K8s.
Work Experience
Senior Software Engineer
Knock
- Designed a secure and cost-efficient infrastructure supporting various data engineering initiatives.
- Migrated Airflow, data pipelines, and support from AWS-managed solutions to Kubernetes (EKS) infrastructure.
- Designed and implemented an internal VPN solution to enable engineering teams access to sensitive data.
Software Engineer
Amazon Web Services (AWS)
- Was a founding member of a new AWS AI services team supporting low code development initiatives.
- Designed and implemented the business logic for new services.
- Carried out the design and implementation of the API supporting the science team model training and development.
- Designed and implemented the benchmarking platform.
Machine Learning Engineer
Zulily
- Adapted an API for deploying a scalable, cloud-based machine learning model using Go, Kubernetes, and Docker. Developed a mechanism for the scheduled execution of said API with Apache Airflow.
- Worked on the back-end process for writing data to an in-memory Redis cache with Java.
- Developed EDA and validation metrics (Java/H20) for a machine learning model used for a daily email job.
- Migrated Spark jobs to GKE from Dataproc, from GCP-managed Spark to a customized environment managed by the team.
- Integrated Kubecost, Prometheus, and Istio into a GKE cluster for the data science team's Kubeflow capabilities.
- Worked on NLP-based SKUs similarity matching for best price promise relative to competitors.
- Optimized the collection of SKUs with respect to aggregate demand.
- Worked on the on-call rotations and DevOps monitoring of GCP and AWS cloud infrastructure.
Machine Learning Engineer
Boldiq
- Developed an AI-based optimization engine employing deep reinforcement learning for learning strategies for optimized resource-constrained scheduling.
- Maintained and debugged ‘Solver’, company’s proprietary real-time optimization engine (for private aviation scheduling).
Senior Data Scientist
Cisco Systems
- Oversaw AI for optimizing network monitoring. Developed a machine learning pipeline allowing for analysis of Cisco’s unstructured data (through Splunk’s Rest API) using ensemble techniques from the SciKit-Learn library. Initiative resulted in improved event correlations on Cisco CMS’s network management platform.
- Extended the initiative to perform network incident forecasting using deep learning techniques on a customized architecture (NLP, semantic analysis via CNNs) using a TensorFlow backend and Keras (high-level API).
- Architected of “Splunk to Excel," an automated reporting mechanism.
Intern/Research Assistant
Ecole Polytechnique Federale de Lausanne
- Developed embedded DB and SDC-constrained scheduling software in Java for High-Level Synthesis and data-flow programming applications (with applications to embedded systems). Accepted into the doctoral program of the EPFL.
Intern/Research Assistant
Swisscom
- Developed insertion loss and cross-talk cable models for 4th-generation DSL standard, G.fast. The use of vectoring to achieve higher data rates requires an accurate understanding of how the cable manipulates intended signaling. Models were used in the Broadband Forum for standardization purposes.
Experience
Machine Learning Class Definition
Education
Master's Degree in Electrical Engineering and Information Technology
ETH-Zurich - Zurich, Switzerland
Bachelor's Degree in Electrical Engineering
University of Texas at Austin - Austin, Texas
Skills
Libraries/APIs
NumPy, Pandas, SciPy, Matplotlib, Scikit-learn, Dlib, Keras, TensorFlow, Jackson
Tools
IntelliJ IDEA, BigQuery, Apache Airflow, Splunk, MATLAB, Google Kubernetes Engine (GKE), AWS Cloud Development Kit (CDK), AWS CloudFormation, AWS Fargate, Amazon CloudWatch, Terraform
Languages
Python, Java, Bash, C, Processing, Java 8, Python 3, C++, SQL, Go, Scala
Frameworks
Apache Spark, Spring, Spark, Coral Services Framework, JUnit, Mockito
Platforms
Amazon Web Services (AWS), Kubernetes, Docker, H20, Google Cloud Platform (GCP), Ubuntu, Jupyter Notebook, Ubuntu 14.04, Visual Studio 2017, Windows, AWS Lambda, MacOS
Paradigms
Scrum, ITIL, Agile
Storage
MySQL, SQL CE, Redis
Other
Google BigQuery, Machine Learning, Optimization Algorithms, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Windows 10, Optimization, Oscilloscopes & Tester Equipment, CVXOPT, Cloud Architecture, Design
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