Machine Learning And Other Solutions

MACHINE LEARNING SOLUTIONS

Generally, Machine Learning (ML) 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 users learn and create their own program without human intervention, but they also are able to 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 human behavior.

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

MACHINE LEARNING: CORE FUNCTIONS

Basically, ML-powered systems provide the following functions:

• To determine what happened based on a vast analyzed data set
• 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 machines to utilize 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

These systems may be used for:

• CLASSIFICATION

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

• REGRESSION

  Outputs are constantly produced

• CLUSTERING

  The series is arranged into classes, but they are unlabeled. This is an unsupervised activity

• QUANTITY ESTIMATION

  Obtaining an estimate of a parameter using relevant data

• MEASURE CUT

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

• THEME PROTOTYPING

  An issue is established in a ML program and language input is entered. The main goal is to detect information that concerns the identified theme

• IMAGE & SPEECH RECOGNITION

• MEDICAL DIAGNOSIS & TREATMENT

• PREDICTION & STATISTICAL ARBITRAGE

• CLASSIFICATION

• SELF-TAUGHT ANALYTICAL TOOLS

• LEARNING ASSOCIATION

• FRAUD DETECTION

• EXTRACTION & REGRESSION

• NATURAL LANGUAGE PROCESSING TOOLS

MACHINE LEARNING FRAMEWORKS

Let's discover what frameworks the 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 that simplifies and accelerates the process

• TENSORFLOW:

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

• WEKA:

  Applied to provide 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:

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

• KERAS:

  This is a high-level API that enables extremely easy implementation of ML-fueled projects

• KNIME:

  Powered with a set of ML algorithms, this platform allows development teams 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 ML models at all stages from ideation and experimentation to production

• APACHE SPARK MLLIB:

  Known as an 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 includes:

• MATLAB/OCTAVE

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

• R PROGRAMMING

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

• PYTHON

  Popular and productive language that is easy to work with

• JAVA

• C

  Deep implementation of the main algorithms is available.

• JULIA

• LISP

• SCALA

• TYPESCRIPT

MACHINE LEARNING DEVELOPMENT: LIMITATIONS

Let's examine the limitations of ML solution development: solutions:

• High level of errors: A significant amount of time is required to identify and correct mistakes in terms of inaccurate data
• 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 will find some hardware options for machine learning solutions:

• Processing units: allows machine learning operations to be boosted and enhances distribution of training processes
• Memory: enhanced memory features allows ML-powered solutions to process 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, a Machine Learning projects company, can help you turn any idea into an ML project or service.