✚ What is Machine learning?

➢ Machine learning is a part of computer science that applies analytical approaches to making computer mechanisms contend with information, without setting them up directly.

✚ What are the main goals of Machine learning?

➢ The primary goal of machine learning research is to develop general purpose algorithms of practical value.

✚ Where is ML used?

➢ Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc..

✚ Which cloud services are best for ML?

➢ AWS, Google Cloud, IBM Cloud & Microsoft Azure are the most reliable.

✚ Where can I look at your cases?

➢ You can look at our cases here. Our technical support is ready to answer all your questions.

MACHINE LEARNING AND OTHER SOLUTIONS

Machine Learning Solutions

Generally, machine learning development aims to make machines replicate or simulate how individuals interact and work with one another as well as deal with complex tasks and perform a wide range of activities. Not only can they learn and create their own program without the intervention of people, but they also deal with tons of complex information and deliver completely accurate results. The primary goal of ML is to produce computer models that represent a real-world system or behavior of humans.

When processing and analyzing different types of information such as numbers/images/texts, including bank transactions statuses or detailed sales reports, pictures of celebrities, or car items, computer models train themselves to identify patterns or put forward suggestions. The truth is the more data they elicit and handle, the completely accurate a created solution is.

Machine Learning: Core Functions

Basically, ML-powered systems provide the following functions:

To set out what happened based on a vast data set analyzed
To forecast what will happen by analyzing datasets
To put forward a suggestion about what activities or actions to take when analyzing vast datasets.

Creating ML-driven algorithms for the machines to direct is complicated and time-consuming. However, our ServReality machine learning team is well-versed in training machines on how to perform the required tasks.

ML Application Development

The system may also be used for:

Classification

Inputs are allocated into 2 groups. The student should create a pattern that appoints the unknown data to one class.

Regression

The outputs are always ongoing.

Clustering

The series is arranged into some classes, but they are unknown. It is an uncontrolled responsibility.

Quantity estimation

The sharing of inputs in some places.

Measure cut

Measure cut makes the inputs easier by placing them in the distinctive spatial area.

Theme prototyping

The issue is established on the program, in which there is the language knowledge. The main goal is to detect what information concerns the same themes.

Image & Speech Recognition

Medical Diagnosis & Treatments

Prediction & Statistical Arbitrage

Classification

Self-taught analytical tools

Learning Associations

Fraud detection

Extraction & Regression

Natural language processing tools

Machine Learning Frameworks

Let's discover what frameworks ServReality machine learning studio applies when creating their ML-driven products:

H20.ai:

development of machine learning solutions in any environment with this open-sourced and cloud-based tool simplifies and accelerates the process.

TensorFlow:

not only does this free-based ML tool help build machine learning models for mobile and web development, desktop and cloud, but it also makes them highly scalable and versatile.

Weka:

applied to provide the implementation of ML algorithms to serve any client's needs.

Anaconda:

packed with machine learning tools, it allows ML engineers to create a variety of ML software solutions.

Oryx 2:

focusing on extensive ML algorithms, this tool helps development teams create and implement ML projects with ease.

Keras:

being a high-level API, it enables to extremely easy implement ML-fueled projects.

Knime:

powered with a set of ML algorithms, this platform allows teams of developers to work in an intuitive environment when dealing with complex tasks.

RapidMiner:

used as a data science tool for creating ML models in a visually organized flow.

Databricks:

as an integrated ml environment, it allows development teams to build, deploy, and manage state ML models at all stages from ideation and experimentation to production.

Apache Spark MLlib:

known as ML library, it provides software engineers with a set of ML algorithms to turn any idea into a robust ML-driven solution.

Machine Learning Languages

The list of the popular languages for Machine Learning solutions is demonstrated below:

MATLAB/Octave

The language is the best way to deal with matrices and conclusions

R Programming

Good for the mathematical examination. This is the best way to observe the interaction of the data by applying stats and graphical schemes.

Python

Popular language and easy to work with. Productive.

Java

Deep implementation of the main algorithms is available.

Julia

Lisp

Scala

TypeScript

Machine Learning Development: Limitations

Let's know the limitations on the development of machine learning solutions:

High level of errors: There is a significant amount of time to identify and correct mistakes in terms of inaccurate data analyzed.
Time and resources: ML-driven products require more time as well as a significant amount of resources to be developed and implemented.
Large datasets: ML-driven solutions need massive datasets to train on.

Software with ML Algorithms

Deeplearning4j
H20
OpenNN
Amazon ML
Oracle AI Platform
MATLAB
IBM Data Science

ML Hardware

Below you can find some options of hardware for machine learning solutions:

Processing units: allows to boost machine learning operations and enhance distribution of training processes
Memory: enhanced memory features allows ML-powered solutions to process the increasing workloads while maintaining efficiency.
Storage: as ML-based solutions require massive data sets to be trained, focusing on storage environments is a "must-have".

We at ServReality machine learning projects company can help you turn any idea into an ML project or service.

FAQ

What is exactly machine learning technology?

➢Aiming to make machines simulate/replicate human behaviors, machine learning (ML) technology is here to help a plethora of businesses perform complex tasks with ease.

What's the key goal of Machine learning (ML) technology?

➢The ultimate goal of machine learning (ML) technology is to deliver practical value by extracting insights, patterns and relationships applied to make wise decisions.

Where is ML applied?

➢Nowadays, ML-powered solutions can be utilized in such sectors as finance and government, healthcare and retail, oil and gas, transportation and education, etc.

Which cloud-based computer platforms are a good fit for ML-backed solutions?

➢When developing an ML-driven development platform, opting for Amazon Web Services, IBM Cloud & Microsoft Azure, AWS and Google Cloud is an attractive option.

Where can I look through your cases?

➢Tap here to learn more about the cases of our web services studio. Don't hesitate to drop us a line if there is a need to turn an idea into a successful ML solution.

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