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Human intervention in critical processes is getting reduced gradually with the increased popularity of Machine Learning (ML). Machine Learning is the employment of scientifically designed ML models to perform anything and everything by analyzing real-life and real-time patterns. However, these models need adequate training. Better the training, more efficient the performance of the Machine Learning Models. The first and foremost thing is to identify the purpose of designing the model. Keeping the purpose in mind, the model has to be built, trained and finally deployed. Cloud providers, including AWS, Google and IBM, provide Machine Learning services through API.
AWS and Machine Learning

Amazon Web Services (AWS) have incorporated Machine Learning (ML) in an effort to manage services in an automated, fast and seamless manner. The versatile tools provided by Amazon ML have made it possible to create Machine Learning models without having the compulsion to learn extremely complex ML Algorithms.
Businesses should employ AWS ML for multifold reasons:

  • Building a simplified Machine Learning (ML) model
  • Using the ML model to generate real-time predictions for various applications
  • High scalability
  • High throughputs
  • Assisting developers to perform astutely with techniques like problem-solving, speed recognition, language analysis, chatbox, etc
  • Supports all the major Machine Learning frameworks like TensorFlow, Caffe2, and Apache MXNet
  • Accessing Big Data through Data Lake
  • High security with strong encryption of sensitive data and detection of fake and fraudulent users
  • Analyzing customer patterns
  • Detecting device locations and positions
  • Facilitates Optical Character Recognition
  • Facilitates pay-as-you-go with no or minimum investment in hardware and software

Google Cloud and Machine Learning

With Google’s state of the art transfer learning and Neural Architecture Search technology, Cloud AutoML has enabled the creation of advanced Machine Learning models assisting developers to manage applications over the cloud with exceptional speeds and unparalleled accuracy. The pre-trained ML models analyze complex patterns of data and negate the compulsion of the developers to have advanced knowledge of ML.

Google Cloud transforms the way of managing businesses. The reasons are as follows:

  • Building a simplified and compatible Machine Learning (ML) model
  • Using the ML model for both online and batch predictions
  • High scalability
  • Easy to use
  • Exceptional speeds
  • Unparalleled accuracy
  • Excellent performances for TensorFlow workloads by employing Google Cloud TPUs
  • High portability
  • Efficient job searching facilities by automated correlation of job titles and skills
  • Creating automated chat boxes with Dialogflow Enterprise Edition which employs ML to create automated responses based on user interaction
  • Distinguished video analysis and image analysis
  • Distinguished text analysis
  • Superfast language translation through highly responsive Google Cloud Translation API
  • Supports Multiple Frameworks

IBM and Machine Learning

Modern businesses generate a huge amount of data. We often miss out the important parts from the substantially large data chunks. Scrutinizing and extracting crucial data is not an easy task to take up. Thanks to Machine Learning, now we can easily extract these data with customized ML models. IBM Machine Learning helps to create self-learning models in real-time.

IBM ML should be incorporated due to the following reasons:

  • Allows step-by-step building of model through the model builder
  • SPSS Modeler or SparkML nodes employed by flow editor to create graphical representations
  • Predicts customer needs
  • Predicts business needs
  • Predicts customer behavior
  • Runs experiments with automation
  • Allows management of work from a single place
  • Automatically integrates from related data sets, articles, notebooks, etc
  • Simplified visualizations with tools like PixieDust and Brunel
  • Interactive dashboards
  • Creating neural architectures with Neural Network Modeler by employing deep learning

At Intelebee, we employ these Machine Learning Models to highly optimize businesses and seamlessly drive products and services from conception to deployment.