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Machine learning Solutions bring 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, produces an incredible volume of data and statistics which is impossible for humans to analyze manually. Our technology helps combine all the data gathered from myriad touchpoints for delivering useful insights to enterprises that contribute to the various strategic outcomes.


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 on a cloud-scale.


In today’s rapidly evolving business landscape, harnessing the power of data is essential for staying ahead of the competition. Faststream, a pioneer in innovative solutions, is proud to present cutting-edge Machine Learning Solutions designed to revolutionize the way businesses operate.


Why Machine Learning?


Machine Learning (ML) is a transformative technology that empowers systems to learn from data, identify patterns, and make intelligent decisions without explicit programming. Faststream’s ML Solutions enables businesses to extract valuable insights, automate processes, and make data-driven decisions for enhanced efficiency and competitiveness.

Our Machine LearningSolution factors


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


Our Solutions are built on top of the open-source technologies:

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


Implement and maintain machine learning systems, generate new projects and create new impactful 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 Machine Learning 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


  • 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 is 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.


Massive Data Consumption from Unlimited Sources:


It can virtually consume an unlimited amount of comprehensive data. The consumed data can be used to constantly review and modify your sales and marketing strategies based on customer behavioral patterns.

Rapid Analysis Prediction and Processing:


The rate at which Machine Learning 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 will make a significant impact on the finance sector. Its 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 behaviors or outcomes and interpret them. Therefore, based on the new and different data you will be able to make better predictions of customer behaviors.