lll

WHO WE ARE
Welcome to anmerkung,your full-ai specialist
Incorporated in 2019.Anmerkung Is A Platform Agnostic, Scalable Workforce Partner For Some Of The Largest Names In AI. We Quietly Power Your ML Life Cycle With A Highly Skilled, Trained Workforce. Over 20+ years of industry experience in Automotive domain.

Highly Scalable Workforce
Trained Workforce That Can Scale Based On Your Needs. Need 500 People On A Project Immediately?

Platform Agnostic
We Understand You Might Have Your Own Tools Or Have Partners Of Choice Based On Expertise, Our Mighty Elastic Workforce Is Trained On The Best Tools In The Industry For A Minimum of 2000+ Hours.

Partner Of Choice
Anmerkung Is The Services Partner Of Choice For Some Of The Largest Platform Providers, OEMs, Tier 1 Suppliers, Retailers & Document Processing Companies.

Certified For Security & Quality
We Understand You Need Your Data To Be Of The Highest Quality & Highly Secure. We’re Optimized For Cloud & On-Prem And Scale Based On Your Needs. ISO 27001, GDPR, Access Controlled Environments?

OUR STRENGTH

Our strengths

We have Strong Network of experience professionals in India, Singapore, Germany, Estonia, Ukraine, Serbia.
Deep and Strong Network with Automotive Embedded SW Professionals

What Do We Offer ?

We only hire people who strive to push their idea

What do we offer?

Recruitment Services
Temporary Staffing
Engineering Services thro Echo System
TIER 2 Near Site ODC’s
BOT Partnership’s
Training Services.

Our Value Proposition

We have Strong Network of experience professionals in India, Singapore, Germany, Estonia, Ukraine, Serbia.

Our Value Proposition

Experienced team of recruitment professionals, with domain specialisation to Automotive Embedded SW requirements Short TAT.
Pre-Screening Done by relevant automotive industry experience professionals.
Team Structure
Process
Estimation
Quality Assurance
Acceptance criteria

Discover our different services

Step 1: Gather the data

As with any machine learning exercise, we first need to gather our data on which we will train the model. The simulator images look something like this:

Step 2: Label and annotate the images

The next step is to manually annotate the images for the network. There are many open source tools available for this like LabelIng, Sloth, etc. The annotation tools create a yaml file that looks something like this:

Step 3: Training the Model

For training the model with the API, we first need to convert our data into the TFRecord format. This format basically takes your images and the yaml file of annotations and combines them into one that can be given as input for training. The starter code is provided on the tensorflow’s Github page.