BoberIT
Benefits working with a small team
JUNE 2, 2023
IT consultancy is very competitive business. A lot of big players with great expertise, lots of human resources with wide variety of skills dominate the market. So why you should choose BoberIT
as your software engineering vendor with only 6 engineers? Let's see below.
as your software engineering vendor with only 6 engineers? Let's see below.
In this short essay I'll list 4 benefits of working with small consultancy. By small company I mean team under 10 engineers. Everything above is considered medium or large. Also in the beginning I assume that both small and bigger companies have roughly same level of expertise in your domain, tech stack, same price range, same jurisdiction, same roughly same communication skills at least on presale stage.
So why working with company size of 10 is better, than working with company size of 30 if every other aspect is approximately same or close.
1. You save money on management
Big companies require funds to support their HR, middle managers, sales, accountants, bench staff. Who is paying for all of that in the end? The client. We dont have any of that.
2. Direct communication
That follows from previous point. You communicate to the people who do the job. There is no "chinese whispers" in between us. You get full information directly from the field. And people in that field are getting their feedback and requirements directly from the customer. It makes communications fast and clear. Which is a key to project success.
3. Only senior and lead level engineers
In a big or medium consultancy there is always a risk that person who is doing your job costs less than you pay for him. Sales have sold you 15 years expertise, lead and architect level rock-star engineer and you even interviewed him. But then it happens that this rock-star engineer is shown to every customer and he supervises 5 projects at the same time, but real job is being done by junior engineers with 1-2 years of experience who probably are still in college. You cant physically get that from us.
We cannot afford junior devs as we
4. Cant afford to loose client
Being small enterprise we dont have much clients. We cannot manage our risks of not having a revenue. Each client is our blood and soul of our company. So be sure if you are working with us, you'll get everything possible. Up to margin of sanity of course.
So why working with company size of 10 is better, than working with company size of 30 if every other aspect is approximately same or close.
1. You save money on management
Big companies require funds to support their HR, middle managers, sales, accountants, bench staff. Who is paying for all of that in the end? The client. We dont have any of that.
2. Direct communication
That follows from previous point. You communicate to the people who do the job. There is no "chinese whispers" in between us. You get full information directly from the field. And people in that field are getting their feedback and requirements directly from the customer. It makes communications fast and clear. Which is a key to project success.
3. Only senior and lead level engineers
In a big or medium consultancy there is always a risk that person who is doing your job costs less than you pay for him. Sales have sold you 15 years expertise, lead and architect level rock-star engineer and you even interviewed him. But then it happens that this rock-star engineer is shown to every customer and he supervises 5 projects at the same time, but real job is being done by junior engineers with 1-2 years of experience who probably are still in college. You cant physically get that from us.
We cannot afford junior devs as we
- Dont have resources to mentor anybody
- Dont work with anyone we dont know or trust personally. That has to be years of proven experience
4. Cant afford to loose client
Being small enterprise we dont have much clients. We cannot manage our risks of not having a revenue. Each client is our blood and soul of our company. So be sure if you are working with us, you'll get everything possible. Up to margin of sanity of course.
MLOps in Maritime
JUNE 1, 2023
We are team of #Arrival alumni proficient in #ComputerVision and #MLOps. We are experienced in Automotive, Navigation and MedTech. In this article we will tell you what is MLOps and your business can benefit from it.
We do not only provide academia-level R&D in computer vision field, we also provide infrastructure development and maintenance for implementing, testing and integrating ML algorithms into production.
What is MLOps and how MLOps can help Your project.
MLOps stands for Machine Learning Operations. MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them.
- Aims to unify the release cycle for machine learning and software application release.
- Automates testing of machine learning artifacts, such as data validation, ML model testing, and ML model integration testing
- Enables supporting machine learning models and datasets to build these models as first-class citizens within CI/CD systems.
- MLOps with us is a language-, framework-, platform-, and infrastructure-agnostic practice.
We provide custom end-to-end machine learning solution including Computer Vision algorithms implementation and AWS-based infrastructure to run your data.
Our combined experience in the field around 100 years. Minimum education degree is masters in Computer Science. One of our colleagues is PhD in Physics and Mathematics.
What is MLOps and how MLOps can help Your project.
MLOps stands for Machine Learning Operations. MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them.
- Aims to unify the release cycle for machine learning and software application release.
- Automates testing of machine learning artifacts, such as data validation, ML model testing, and ML model integration testing
- Enables supporting machine learning models and datasets to build these models as first-class citizens within CI/CD systems.
- MLOps with us is a language-, framework-, platform-, and infrastructure-agnostic practice.
We provide custom end-to-end machine learning solution including Computer Vision algorithms implementation and AWS-based infrastructure to run your data.
Our combined experience in the field around 100 years. Minimum education degree is masters in Computer Science. One of our colleagues is PhD in Physics and Mathematics.
MLOps on autonomous ice navigation project
Working with Icebreaker ship-building enterprise, we helped them to implement system for ice-conditions monitoring with help of cameras and LiDARs.
What we did:
1) Implementation of classification problem solution. (ice/water)
2) Getting real-time data from LiDARs
3) Pipeline to run Computer Vision algorithms for classification problem
4) Pipeline to analyze data from LiDAR
5) Reporting service to provide data analytics report to information system of the vessel
Steps 1-4 are in fact implementation of MLOps practices. They make it possible to build a flow of non-stop, continuous data analysis process that helps people in cockpit in front of screen with ship information system to make better informed decisions on the course and navigation. In future development it would be a part of autopilot for ice navigation.
Picture: Federal news screen
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