Machine LearningSolutions

Machine learning brings new insights every day across a broad range of industries and research worldwide. Be part of it and explore the best of what happens when human and machine intelligence is combined. The ever-increasing usage of electronic means of interaction and commerce, as well as IoT devices producing an incredible volume of data and statistics which is impossible for humans to analyze manually. Machine learning technology helps combine all the data gathered from myriad touch points for delivering useful insights to enterprises that contribute to the various strategic outcomes.

 

For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you are aiming at building another recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start a machine learning initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit.

 

It is an integrated, end-to-end data science and advanced analytics solution. It enables data scientists to prepare data, develop experiments and deploy models at cloud scale. Faststream technologies Machine Learning services fully support open source technologies.

Our Machine LearningSolution factors

Algorithms

There is a strong bias towards algorithms used for the most prevalent supervised machine learning problems you may encounter.

  • Regression Algorithms
  • Instance-based Algorithm
  • Bayesian Algorithms
  • Decision Tree Algorithms
  • Regularization Algorithms
  • Artificial Neural Network Algorithms

Technologies

Our Machine Learning solutions are built on top of the open source technologies:

  • Docker
  • Python
  • Apache Spark
  • Kubernetes
  • Jupyter Notebook
  • Conda

Frameworks

Implement and maintain machine learning systems, generate new projects and create new impactful machine learning systems. We use some of the top open source machine learning frameworks.

  • TensorFlow
  • Apache Spark MLlib
  • Accord.NET
  • scikit-learn
  • Spark ML
  • Shogun
  • Microsoft Cognitive Toolkit

Stages of our Machine Learning Solutions

 

Classify the problem: Build your problem taxonomy that describes how to classify the problem or business question to solve.

 

Acquire data: Identify where the data exists to support the problem you’re trying to solve. Data used in Machine Learning can come from a variety of sources, such as ERP systems, IoT edge devices or mainframe data.

 

Process data: Identify how to prepare data for ML execution. Steps here include data transformation, normalization, and cleansing, as well as the selection of training sets.

 

Model the problem: Determine the Machine Learning algorithms to be used for training or clustering. A range of algorithms can be acquired and extended to suit different purposes.

 

Validate and execute: Validate results, determine the platform to execute models and algorithms, and then execute the Machine Learning routines. The execution process likely comprises many cycles of running the Machine Learning routine and tuning and refining results.

 

Deploy: Finally, the output of the Machine Learning process is deployed to provide some form of business value. This value may come in the form of data that will inform decisions, feed applications or systems, or be stored for future analysis.

 

The core functionalities offered by Faststream Technologies Machine Learning Solutions

 

  • Python SDK for invoking visually constructed data preparation packages.
  • Automatic project snapshots for each run and explicit version control enabled by native Git integration.
  • Integration with popular Python IDEs.
  • Data preparation tool that can learn data transformation logic by example.
  • Data source abstraction accessible through UX and Python code.
Many of our day-to-day activities are powered by machine learning algorithms, such as:

 

  • Real-time ads on web pages and mobile devices.
  • Web search results.
  • Natural Language Processing.
  • Network intrusion detection.
  • Pattern and image recognition.
  • Text-based sentiment analysis.
  • Credit scoring and next-best offers.
  • Fraud detection.
  • Deep Learning.
  • Prediction of equipment failures.
  • Email spam filtering.

Benefits of Our Machine LearningSolutions

Massive Data Consumption from Unlimited Sources: Machine learning virtually consumes the unlimited amount of comprehensive data. The consumed data can be used to constantly review and modify your sales and marketing strategies based on the customer behavioural patterns.

Rapid Analysis Prediction and Processing: The rate at which ML consumes data and identifies relevant data makes it possible for you to take appropriate actions at the right time. For instance, Machine learning will optimize the best subsequent offer for your customer.

Improves Precision of Financial Rules and Models: It is a significant impact on the finance sector. Machine learning benefits in Finance include portfolio management, algorithmic trading, loan underwriting and most importantly fraud detection.

Interpret Past Customer Behaviors: Machine learning will let you analyze the data related to past behaviours or outcomes and interpret them. Therefore, based on the new and different data you will be able to make better predictions of customer behaviours.