Out-staff

Amazinum - Data Science Outstaffing Services

Amazinum will provide a team of reliable and flexible specialists in Data Science, AI and ML, who adapt to your business needs and help you achieve the best results. Whether you’re looking to scale your AI initiatives, accelerate data-driven decision-making, or take on complex analytics projects, our experts will turn your ideas into reality.

Accelerate Your Success with Amazinum AI & ML Outstaffing

Effortlessly Build Your Expert Data Science and AI Team, Accessing Top Eastern European Talent While We Handle All Operational Logistics for a Seamless Integration

You Save the Expenses

Office Place

Hardware

Education

Accounting

Employment

Specialist in reserve

We Provide Specialists in Such Expertise

ML & AI

Let Amazinum provide you with specialists in computer vision, NLP, prediction, or sentiment analysis solutions. Based on AI implement Machine Learning and NLP solutions in your industry and gain a competitive advantage.

Data Analysis

Analyze data to uncover insights, trends, and patterns that help drive informed decision-making and optimize business performance.

Data Engineering

Data collection, creation of pipelines and data architects, ETL and their literacy – all this is assigned to Data Engineering. Amazinum specialists create a data product qualitatively and professionally.

Cloud Solutions

We use GCP, and AWS technologies to develop and configure cloud infrastructure. Amazinum experts will help you, train models, create data, and manage cloud infrastructure.

ML & AI

Data Analysis

Analyze data to uncover insights, trends, and patterns that help drive informed decision-making and optimize business performance.

Data Engineering

Cloud Solutions

We use GCP, and AWS technologies to develop and configure cloud infrastructure. Amazinum experts will help you, train models, create data, and manage cloud infrastructure.

Industry Expertise

Healthcare

Predictive analytics, personalized medicine, and operational efficiency improvements.

Manufacture & Supply Chain

Predictive maintenance, inventory optimization, and demand forecasting, enhancing operational efficiency and reducing costs.

E-commerce & Retail

AI-powered recommendation engines, inventory management, and personalized marketing.

SEO & Advertising

Actionable insights, optimize campaigns, and drive targeted traffic for maximum impact

Ecology

Data-driven solutions for climate modeling, biodiversity monitoring, and sustainable resource management

FinTech

Predictive analytics to detect fraud, optimize trading, and assess credit risk

Mental Health

Personal treatments, improving telemedicine platforms, and developing early intervention tools

Pet Care

Intelligent pet care systems, personalized recommendations for pet owners, product recommendations

Energy & Utilities

Data-driven solutions for climate modeling, biodiversity monitoring, and sustainable resource management

Media Entertainment

AI & ML drive data-driven solutions for content personalization, audience analytics, and optimizing production workflows to enhance engagement and streamline creative processes.

FinTech

Predictive analytics to detect fraud, optimize trading, and assess credit risk

Mental Health

Personal treatments, improving telemedicine platforms, and developing early intervention tools

Pet Care

Intelligent pet care systems, personalized recommendations for pet owners, product recommendations

Energy & Utilities

Data-driven solutions for climate modeling, biodiversity monitoring, and sustainable resource management

Media Entertainment

AI & ML drive data-driven solutions for content personalization, audience analytics, and optimizing production workflows to enhance engagement and streamline creative processes.

Why Should You Hire Amazinum?

Direct interaction

The specialist works directly with the client as part of their team, not as a third party. This allows for better communication and avoids possible problems.

HR-processing

We take care of employee productivity, motivation, satisfaction, and loyalty, covering corporate events, gifts, medical insurance, equipment, compensation, and leave.

Education and professional development

Hiring an Amazinum specialist ensures they have the skills needed for your tasks, with education and professional development, we take care of technical courses, English classes, and performance reviews.

Operational support

Amazinum offer a diverse pool of specialists, ensuring you select a highly qualified candidate with relevant experience at competitive rates

The Voice of Happy Clients

Data Engineering For an American Clothing Company PVH

Our Client

Our clients are a successful American apparel company Phillips-Van Heusen Corporation, that owns the Tommy Hilfiger, Calvin Klein, Warner’s, and True & Co. brands. PVH is one of the world’s largest lifestyle companies. It works with consumers in more than 40 countries.

Business Context

The goal was to reduce the amount of code and optimize its performance for better performance.

Value Delivered

We staffed 2 talents to optimize the existing code to solve performance issues and optimize the data pipeline for the client, providing optimal performance and scalability even more than expected for the business needs.

Challenges Faced

A large amount of duplicate code that was run with Airflow, which negatively affected processing performance, code readability, and the ability to modify it for further work with it.

The Tasks Set For Data Engineers by the Client:

Analyze the code and its functionality, taking into account the principles of refactoring and optimization for maximum efficiency in working with data.

Study and analyze the mechanisms for loading data into the pipeline processing, including methods of integration with GCP BigQuery.

Determine the number and sequence of data processing stages, taking into account streaming and batch processing, as well as the principles of parallelism and distribution of computations.

Conduct testing and validation of the finished solution in the environment and then using the Airflow orchestrator to meet the requirements and quality standards of the project.

Familiarize yourself with the mechanisms and methods of further transferring processed data to databases or other services for analysis and reporting, in particular in the context of storing and transmitting data in a format that meets security and efficiency standards.

Ensure code uniqueness and reusability through the use of design patterns, functional programming, and other methods of structuring and optimizing code.

Optimize the code for optimal performance and data processing speed.

Analyze the used algorithms and data processing methods.

Amazinum Data Engineers In Action

Our team of Data Engineers took on the task of optimizing existing code to address performance issues. We went through each code notebook in detail, identifying areas of duplication and inefficiencies. By applying optimization techniques and using cloud computing resources, we have successfully reduced the code size by more than 10 times, optimized the pipeline, and improved execution speed by more than 80%.

In addition, we used Apache Spark to parallelize data processing tasks, using its distributed computing capabilities to accelerate calculations. By tuning SQL queries and optimizing data processing operations, we have achieved significant improvements in processing speed and efficiency.

Using Google Cloud Platform services such as BigQuery for data processing and analysis, we optimized data loading processes to minimize delays and improve performance. Apache Zeppelin was the primary tool for the task at hand, serving as our collaborative data analytics tool, allowing interactive exploration and visualization of data transformations.

In general, a systematic approach to code optimization and the use of available tools and technologies led to a significant optimization of the data processing pipeline for the client, providing optimal performance and scalability even more than expected for the business needs.

Review

Data Science & Analytics Services for Fashion House

They were good at thinking about solutions and had a high level of expertise in data science.

Thanks to Amazinum’s efforts, the client optimized and created a large number of Zeppelin notebooks. Moreover, the client appreciated the team’s good cooperation and clear communication throughout the project. Amazinum delivered on time and was outstandingly professional.

5.0

★ ★ ★ ★ ★

They were good at thinking about solutions and had a high level of expertise in data science.

Harnessing AI for Video Content Moderation

Our Client

Our client was an international IT company Yellow Media with which we cooperated under the white-label model. The company provides services to clients from various industries and markets, and develops unique, effective, and innovative software products for leading enterprises. Accordingly, they provided our Data Scientists as outsourced specialists for their client.

Business Context

YelowMedia’s client planned to create a certain social network for video content. Accordingly, end users have to upload videos that do not violate social network policies. Such requirements usually include checking for violence, explicit nudity, sexual situations, etc.

Value Delivered

Data scientists of Amazinum performed a white-label task. We staffed 2 talents who build a tool for checking videos for prohibited content such as violence, explicit nudity, sexual situations, etc.

Tasks for Data Scientists:

Among the main tasks that the client set before our specialists were:

The process of checking videos for prohibited content such as violence, explicit nudity, sexual situations, etc.

Setting technical conditions for videos such as: uploading short videos up to a minute

Parallel processing of a large number of videos

Amazinum Data Engineers In Action

Before starting the task, our specialists researched the capabilities of the model to determine whether they could train a custom model or use one of the existing services. Training required data – a labeled video dataset. However, since the client did not provide the necessary data, Data Scientists analyzed cloud services that would provide an opportunity to use the existing proprietary model. In conclusion, specialists chose AWS Rekognition. After gaining access, they made an API that would use this service and provide conclusions on moderation.

After processing, the video is sent to S3 (Amazon bucket storage) and added to the Redis queue waiting for the analysis.

Amazinum Data Scientists also developed a background script that checks the queue every second. Thus, the whole process went like this:

The script looks at the status of jobs for videos processed through Rekognition and checks their completion status. If the analysis is finished, it saves the results in the Redis, frees the queue, and runs the Rekognition Content Moderation job for a video from the waiting queue. In this way, all videos that were added via the API are processed as soon as possible by incoming order.

The solution provided by our Data Scientists consisted of two parts.

To handle the flow of simultaneous requests from the client, our developers have developed an API that works asynchronously. It runs separate preprocessing for video, as AWS recognition has clear standards for video characteristics, to run video moderation jobs. Such characteristics are:

Video codec H.264
Extension of MP4

Additional custom characteristic – maximum resolution of the video (we resize the video for faster analysis since the experiments show that the resolution affects the running time of the job)

Challenges for Amazinum Data Scientists

During the execution of the task, our specialists encountered certain difficulties:

Latency – The process of publishing videos goes a long way and processing speed is a crucial metric for a client. During the processing workflow, we get links to user videos that are stored on local storage, perform preprocessing, upload them to cloud storage, run the analysis in Rekognition, process the results, and save them to Redis. This way we face the latency problem because we get many intermediate steps which reduce the throughput.

As a result, the architecture was designed modularly – split into two major parts. As well as parallelizing everything as much as possible. Separate processes for separate modular tasks.

Service Limitations and Scaling – AWS Rekognition has a quota for the number of videos that can be processed simultaneously. The service allows you to launch 20 jobs, but we aim to handle a constant stream of customer requests.

Accordingly, we designed an architecture with queue management that will work around such limitations. We aimed to develop a solution that can scale easily by demand due to the nature of the business domain which is also one of the advantages of using assembly with cloud services. Also, the service allows the client to contact AWS Support to increase the quota.

Monitoring Efficiency – AWS Rekognition starts the analysis in the form of Jobs, accordingly, it does not give information about when the analysis ended

The monitoring problem was taken into account during development. The solution was designed to address this correspondingly, flexibly, and efficiently.

Review

AI Development for IT Company

Their easy solutions to complex problems, pleasant communication, and the team members’ responsibility are impressive.

Amazinum impresses the client with their ability to provide easy solutions to complex issues. Their responsible team manages the project efficiently, ensuring everything is at a high level. They also facilitate pleasant communication through online meetings.

5.0

★ ★ ★ ★ ★

Their easy solutions to complex problems, pleasant communication, and the team members’ responsibility are impressive.

Upwork Clients

React front-end developer

5.0

★ ★ ★ ★ ★

Jan 3, 2023 – Jan 10, 2023

“awesome”

React front-end developer

5.0

★ ★ ★ ★ ★

Jan 10, 2023 – Jan 10, 2023

“awesome freelancer!”

Work on generative AI for circuits

5.0

★ ★ ★ ★ ★

Dec 7, 2022 – Dec 28, 2022

Amazinum Approach
for ML & AI Outstaffing

Customer orientation

The main task of our team is to satisfy the client’s interests. We are attentive to needs and data, focused on supporting communications and constant updates, and focused on quality results.

Clarity of execution

We ensure your team meets deadlines and communicates effectively because your success is our success. With Amazinum, you can trust that every task is managed clearly, fostering collaboration and delivering high-quality results.

Attractive prices and excellent quality

Clients negotiate service prices directly with candidates, while we take a reasonable percentage for operational support. This transparent approach ensures you get the best talent at competitive rates, maximizing value for your investment.

Experience and Reliability

Amazinum has over 5 years of experience in AI and ML development services across various industries, including fintech, healthcare, mental health or retail. We prioritize our clients’ goals, ensuring they find the best expertise to meet their needs. With a team of over 50 specialists, we deliver tailored solutions in Data Engineering, Machine Learning, Artificial Intelligence, and more.

Amazinum Approach for ML & AI Outstaffing

Customer orientation

The main task of our team is to satisfy the client’s interests. We are attentive to needs and data, focused on supporting communications and constant updates, and focused on quality results.

Clarity of execution

Attractive prices and excellent quality

Experience and Reliability

FAQ

How does out-staffing differ from recruitment?

Contrasting the IT Outstaffing Model and the Local IT Recruitment Model. In the outstaffing model, the client collaborates with the vendor to identify suitable candidates and subsequently engages directly with remote workers. Conversely, in the local IT recruitment model, a recruitment agency locates suitable candidates, after which the client integrates these workers into an in-house team.

What is the difference between out-staff and outsourcing?

The main difference between out-staffing and outsourcing is that you hire a specific specialist with the knowledge base you need and he receives your work instructions directly. This strategy most often used by companies, that are engaged in project work for obvious reasons: it is much more convenient, faster, and more transparent than outsourcing, where you do not see a person who exactly fulfills your order. Also, in the outsourcing chain, all information passes through a number of people, which can violate the confidentiality of the project, and can be perceived differently. In the end, the tasks may not be clearly understood and can cause additional problems.
Out-staffing allows you to overcome all these obstacles and is an ideal option for companies with a project type of work.

Is outsourcing good or bad?

Outsourcing has emerged as a valuable strategy embraced by countless businesses. It not only contributes to the reduction of operational expenses but also grants access to specialized services. Businesses can harness the external providers’ expertise to elevate the quality and efficiency of their services.

Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

Hello there!

Amazinum Team assists you through all data science development processes:
from data collection to valuable insights generation.
Get in touch with our CEO and Data Scientist to figure out the next move together