Data Analytics Outsourcing Company
Data Analytics Outsourcing Company
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You’ve gathered terabytes of data, and now all you have to do is hire experts to make use of it, right? Well, it’s not all that simple. Finding big data professionals is hard: they’re scarce and expensive. But big data analytics outsourcing just might solve both these issues.

Besides, data-driven solutions are all the rage now. And the faster you deliver them, the sooner your company or startup will benefit. So don’t be afraid to outsource analytics solutions or data science because outsourcing will give you the speed in-house experts won’t. Keep reading for more advantages of data analytics outsourcing.

Big data analytics outsourcing benefits


Source: Unsplash

Nowadays a lot of companies try to solve the problems associated with the adaptation of the solutions designed for standard on-premise servers to work with cloud services. In this case, companies migrate their infrastructure, architecture, and services to the cloud providers like AWS and others. What are the main advantages of clouds over physical on-premise servers, and how is the migration process in large companies going? Keep reading to find out.

Business goals to be solved with the cloud migrations

First off, let’s define that there are 3 global types of data stores: on-premise servers, cloud servers, and hybrid. The last one allows managing IT infrastructure entirely remotely without…


Satellite Imagery by PlanetScope
Satellite Imagery by PlanetScope
Satellite Imagery by PlanetScope

The big data revolution is in full swing, and satellite imagery providers lead the way. With their constellations orbiting the Earth, satellite data is now accessible to everyone. This marks the next step in remote sensing technology for many industries — from construction to agriculture and forestry.

Planet, Sentinel, DigitalGlobe, and other satellite imagery providers offer different resolution options, but how do you choose? Most importantly, how do you know if you need high-resolution images or a lower resolution will be enough? Keep reading to find out.

What is satellite image resolution?

Resolution refers to how easy it is to distinguish an object in a…


Volleyball Serve Detection
Volleyball Serve Detection
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Machine learning and artificial intelligence solutions have propelled mobile app development to a new level. Applications with the integration of machine learning technologies can successfully recognize and classify images, human voices and actions, conduct text recognition from images and translate it. This list can easily go on. However, the main problem for engineers remains transferring huge models with millions of connections to a mobile phone, without losing processing speed and crucially — quality still remains.


Deforestation imagery
Deforestation imagery
Deforestation imagery by Planet.com

Woodlands in Ukraine similar to most forests in other countries are at the risks associated with illegal deforestation. Official statistics of illegal logging in Ukraine show a decrease from 10,000 cases of illegal logging per year to 5,000 for the last 10 years period but independent observations indicate an increase in the number of violations. At the same time, according to state and civilian estimates, the amount of illegal logging is increasing and in 2019 amounted to more than 100,000 cubic meters. …


agritech
agritech

AgriTech is sprouting as an industry that attracts increasingly more investors. It aims to make farming easier, more data-oriented, ecological, and profitable. BIS Research predicts the global smart farming market to reach around $23 billion by 2022 and grow at a compound annual growth rate (CAGR) of almost 20% from 2017 to 2022. Such accelerated development is triggered by the increasing demand for crop yield and food production with the same resources. Technologies help agriculture businesspeople reinvent farming strategies and empower farmers with machine learning, IoT, big data analytics, and computer vision in agriculture.

Smart Farming Technologies as the Industry Driver

Also called the 3rd Green Revolution…


data analytics in healthcare
data analytics in healthcare
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The amount of data that humanity generates is growing exponentially. IDC estimates that in 2021 only, about 75 trillion gigabytes, i.e., 75 zettabytes, of data will be created. With the help of data science, financial, surveillance, and social media companies, among others, analyze this information and derive additional benefits for their businesses for many years to come. Still, not all industries are equally good at adapting to and leveraging the power of data to drive their business.

The healthcare industry generates a massive amount of data that can be used by biotech companies to deliver advanced health tools to hospitals…


2020 has been full of challenges — the global pandemic and lockdown and a recession in the world economy are just some of the shocks faced by many businesses across all industries. Agriculture is no exception. According to FAO, the current crisis stopped farmers from completing sowing, closed borders restricted the movement of seasonal labor for harvesting, and reduced income of the world’s population has led to a decrease in purchasing power, including food.

In recent years, the role of data in agriculture has increased significantly. Advances in technologies such as big data, machine learning, cloud computing, blockchain, IoT, autonomous…


According to research, the global agriculture market is expected to grow 8% annually and reach $12 trillion in 2023. Due to this growth, the biggest challenges of the industry, like the world’s growing population, increasing urbanization, stressed usage of natural resources, and global climate changes, need to be handled. And one of the ways to meet the demand for food is the widespread use of data-driven technologies.


In April 2020, our team participated in the first Agriculture-Vision Challenge. The Challenge, organized by IEEE/CVF CVPR, was aimed to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images.

was aimed to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images.

Having extensive expertise and considerable experience in collecting and analyzing data from satellites, drones, and sensors for our customers within the agritech industry made us accept the challenge and try our hand at innovative pattern recognition.

Because our team of 3 people started the competition a bit…

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