Machine learning

Leverage data-rich services to solve complex business challenges with machine learning

Delivering best-of-breed

We deliver and develop advanced machine learning solutions to help enterprises solve many key business challenges. Our services help you achieve data-driven decision-making with ML-powered applications.


Delivering best-of-breed

With a heavy focus on R&D as a service, we help companies to bring their brainchild to life by creating a working POC and make it mature enough for the full scale.

  • Predictive Analysis
  • Deep Learning
  • Data Engineering
  • Computer Vision
  • Natural Language Processing
  • Recommender Systems

Platforms we use

Amazon Web Services. Highly scalable, complete cloud platform. Microsoft Azure. IaaS and PaaS computing for development, deployment, and management. Google Cloud Platform. Developer products and cloud technologies hosted by Google.

With Bpointer Technologies, you will get onsite, onshore, nearshore and offshore resource with highly customized engagement covering your entire spectrum of Machine Learning requirement.

Why choose us

  • Machine Learning on AWS
  • Machine Learning on Google Cloud
  • Machine Learning on Azure
  • Open Source Machine Learning Frameworks
Platforms we use

Subfields of machine learning

Machine Learning has a wide range of applications in today's business, and it will only grow and improve over time.

Artificial intelligence

AI entails an agent interacting with the environment to learn and take actions that maximize its chances of success.

Data mining

ML focus on prediction based on properties learned from training data, DM focus on the discovery of unknown properties in the data.


Statistics draws population inferences from a sample, whereas machine learning finds generalizable predictive patterns.


Optimisation algorithms minimize loss on a training set, ML algorithm are concerned with minimising loss on unseen samples.

machine learning


Depending on the type of "signal" or "feedback" that is provided to the learning system, machine learning systems are generally categorized into three major groups that correlate to learning paradigms:

  • Supervised learning: A "teacher" presents the computer with example inputs and desired outputs, and the goal is to learn a general rule that maps inputs to outputs.

  • Unsupervised learning: The learning algorithm is not labeled, leaving it to find structure in its input on its own. Unsupervised learning can be a standalone goal (finding hidden patterns in data) or a means to an end.

  • Reinforcement learning: Occurs when a computer program interacts with a dynamic environment in order to achieve a specific goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.