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Combine Machine Learning with Human Intelligence

Making Sense of data with ML

Organizations today generate enormous amounts of data. Transforming this raw data into actionable intelligence and insights is a challenge. Machine Learning and AI tools help you analyze this data at scale.When integrated in to your applications and services, they can help companies make smarter business decisions, listen and act on their customer voice, reduce operational costs and truly gain a competitive advantage.

However, without the aid of established processes and tools, operationalizing a Machine learning project can be fairly time taking experience for many organizations.

Bitwave solutions can help your organization focus on the core processes of your Machine learning projects by helping you speed up the data preparation and classification stages as well as aid with iterative model tuning with human-in-the loop approach.

Any machine learning project requires cleansing and healing of raw data, human labeling, set of NLP and ML tools, Model selection and evaluation, and tuning the model for accuracy through an iterative process before it gets deployed in production

HITL approach to ML

In many cases, Ml model accuracy is not high enough to meet the demands of the business scenario. In such cases the models need to be truned and calibrated on a frequent basis using human judgement. The Human-in-the-loop(HITL) approach, lets the confidence score of the model is below the accuracy threshold, humans classify the sample again and feed back into the model to improve prediction accuracy

Why Bitwave Solutions

We bring a team of Machine Learning experts, data modelers and team of intelligent and smart individuals who quickly understand the context of the data and the labelling requirement; and create a data set which can be directly used to train and test the Machine Learning models.

Our team brings in years of collective expertise in variety of business domains such as manufacturing, automotive, steel, distribution and retail to name a few.

Steps in a successful machine learning Project

  • Data Labeling, Tagging, or Annotation(Text, Image, or Video)
  • Content Creation (Text, Image, or Video)
  • Content Moderation (Harmful Content,Nudity etc)
  • Document Reviews or Creation
  • Speech Recognition
  • Face Recognition