- Role type: ML Tech Lead (non-people management) Work location: Remote
- Position overview:
- Play a critical role in shaping the strategy, defining metrics and counter-metrics, and setting goals and priorities for the team ML system design, ML excellence, and identify and develop need new metrics plan.
- Lead projects and teams accordingly including develop custom/novel ML architectures.
- Function as go-to person to escalate the most complex online / production performance and evaluation issues beyond modeling and how the machine learning system interacts with the other systems around it.
- Re-evaluate the tradeoffs of already shipped features/ML systems and drive large efforts across multiple teams.
- Significantly level up the quality of ML work in the broader team around you
- Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules-based models.
- Code deliverables in tandem with the engineering team
- Adapt standard machine learning methods
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This game pad has been around quite a while, so I thought I would try to use it on the latest version of Windows 11, here is what I found (08/01/2023). | |
Windows 11 doesn't recognise this device when you plug it in (no Plug'n'Play). | |
It will work quite well eventually, but I found I needed to do the following. | |
Before you start, make sure the F710 is in 'X' mode NOT 'D' mode (little switch on the front) | |
1. Download the Logitech F710 Connect Utility: | |
https://download01.logi.com/web/ftp/pub/techsupport/joystick/F710ConnectUtility_2.15.264.exe | |
2. Use this utility to get the F710 installed |
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from pymavlink import mavutil | |
# Set the connection string to your autopilot. This can be a serial port or a network connection. | |
connection_string = 'udp:127.0.0.1:5800' # Replace with the appropriate connection details | |
# Create a MAVLink connection | |
master = mavutil.mavlink_connection(connection_string) | |
while True: | |
msg = master.recv_match(type='COMMAND_LONG', blocking=True) |
This guide shows how to deploy an uncensored DeepSeek R1 Distill model to Google Cloud Run with GPU support and how to perform a basic, functional fine-tuning process. The tutorial is split into:
- Environment Setup
- FastAPI Inference Server
- Docker Configuration
- Google Cloud Run Deployment
- Fine-Tuning Pipeline (Cold Start, Reasoning RL, Data Collection, Final RL Phase)
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