MIL.MVP team helps business to get ML-decision in a limited time, according to expectations and staying within the budget for the development of a prototype

WE HELP OUR PARTNERS
1
CHECK BUSINESS AND TECHNOLOGICAL HYPOTHESES
2
QUICKLY CREATE PROTOTYPES (MINIMUM VALUABLE PRODUCT)
3
ADD ML FUNCTIONS TO DEVELOPED PRODUCTS FOR RAPID GROWTH
Our team knows how to create ML-product from "a cold start". How to develop technological MVP without a massive amount of data. How to collect and select the first bunch of data. How to come to the first prototype which will bring value.
Our team has developed decisions of various tasks "from scratch", so we know how to quickly solve the task. We know the difference in technologies used in the classical process of solving machine learning tasks from technologies used in the zero phases of the project development when we don't have data at all. We know how to use both expert knowledge and specific technological decisions, know how to build an effective process of collecting and annotating data on crowd-source platforms.
POPULAR PROBLEMS AND REQUESTS
THE LACK OF EXPERTS
To get the first solution, you need a competent team. Without testing hypotheses, a business cannot offer itself to expand its staff and makes a decision to cooperate with our team.
THE LACK OF DATA
It is necessary to develop an ML solution, but the data collection process hasn't been adjusted yet, so the data is not available. It is not clear what type of data needs to be collected, what is the format and how to annotate it without losing the money.
NEED A QUICK START
A team of experienced professionals may not be flexible enough to create a prototype in a couple of weeks. And the new team is storming at the start of work. Therefore, to quickly implement prototypes and new ideas, you need a well-coordinated team specializing in MVP implementation.
NO UNDERSTANDING OF WORKING METHODS FOR MVP
The models that can be successfully used to quickly create MVPs with a small amount of available data are usually very different from the models that are used in production systems, where there are no complexities with data volumes. You need a team that knows how to use them.
EXAMPLES OF IMPLEMENTED WORKS
WHAT TECHNOLOGIES WE USE
What we use to code
Main programming language: Python
Backend: Python Flask, Django
Frontend: React, vue.js
Working with data:
PostgresSQL, MongoDB
Distributed computing: Spark, Hadoop
Notebooks: Colab, Jupyter, H2O;
Model packaging and data flow control: Kubernetes, Docker, Airflow;
Profile frameworks
Neural network frameworks: PyTorch (prototyping), TensorFlow (production), TensorFlow lite (device import);
Working with data: pandas, numpy;
Profile libraries: BigARTM, TopicNet, gensim, nltk, DeepPavlov, SpaCy, OpenCV, scipy, etc;
Experiment management: Wandb, MLFlow, Tensorboard;
Code Style and solution delivery
Styling your code in python scripts using pep-8
High level of code readability


Delivery options:
  • Docker + REST API;
  • Web-service + Frontend;
  • Python-scripts;
  • Python libs.
OUR PRICE POLICY
Time & materials
  • Estimation of the cost in work hours of a specialist
  • Movement in short iterations (1 or 2 weeks)
  • Development time reporting
$50
Per Hour
FIXED PRICE
  • Techical requirement by stages
  • Acceptance of the result according to the scenario and criteria
  • It is possible to flexibly change the stages of the technical assignment by agreement
$6000+
Per Project
HOW WE WILL WORK TOGETHER
1
Problem statement and business context
Defining a set of tasks, the solution of which is relevant for business.
Discussing the wishes and limitations that the company has.
2
NDA and cooperation agreement
Signing a non-disclosure agreement.
Signing a framework cooperation agreement.
3
Assessment and project objectives
Defining the business goals in solving the problem and the indicators of the project's success.
Preparing a technical task and a commercial proposal for the stages of solving the problem.
4
Contract
Agreeing on the terms of the contract and sign the contract on both sides.
5
Task data and questions
Preparing a data slice to solve the problem.
Discussing the questions that arose during the analysis of the problem.
6
Solution and delivery
Carrying out several iterations of solving the problem.
Delivering ready-made results according to the agreed project roadmap
7
Delivery and acceptance of the project
Want to order a project
Hey! Our team is ready to create a solution for your company. Just describe your task and leave your contacts. We will contact you.
Waiting for your tasks.