Audvik Labs


Predicting the future isn’t magic, it’s artificial intelligence

Our Services

ML Model Building
Forecasting using ML models
CNN & RNN Implementation
BOT implementation

About Machine Learning

Machine Learning is a field of artificial intelligence that focuses on leveraging data to improve the performance of a set of tasks by training the machine. It is an act of applying mathematical models to data that assists a computer in learning without explicitly providing instructions. Machine learning is almost 60 years old, however, it is only when cloud technologies became more sophisticated, we were able to leverage the power of mathematical models to train the machines using the vast amount of data generated by billions of users worldwide. The disruption caused by cloud technology and data fuelled the growth of machine learning in the past 2 decades. The innovation in the field of machine learning has unlocked tremendous opportunities and has given hope to solve the problems that were once considered impossible.

Machine Learning is used by various industries across the globe to augment their performance and unlock trends using data. A few examples are:

  • In 2006 the OTT giant Netflix launched a competition to find a program to better predict user preferences and improve the recommendation algorithm by 10% – it was won by a few scientists from different organizations in 2009.
  • Since 2014 machine learning is being used to identify how the medical field would be evolved, and how the healthcare systems would be more available to people at fewer costs.
  • It is applied in the field of fine arts to study the intricacies and has uncovered previously unrecognized influences among artists.
  • It is being used in the smartphone industry to optimize thermal performance based on user interaction.

All these are powerful examples of how machine learning is playing a vital role in predicting and shaping the future of many industries.

Our Areas Of Expertise


  • Collaborative Filtering- uses similarities between users and items simultaneously to provide recommendations.
  • Content-Based Filtering- uses item features to recommend other items similar to what the user like
  • Hybrid Recommendation Systems- A combination of Collaborative and Content-Based Filtering Methods.


  • Customer support
  • Answer consumer queries
  • Product related queries


  • Economic Outlook
  • Sales Forecasting
  • Inventory Planning
  • Workforce Planning
  • Weather Forecasting


  • Style Transfer
  • Object Detection
  • Gesture Recognition


  • Speech recognition
  • Translation
  • Text Mining


Apply sentiment analysis models on the data obtained through various social media sites to diagnose issues and prompt to the users.

Benefits of MLaaS Platform

  • Data Management: With workloads migrating from on-premise to cloud storage systems, these data loads needs to be organized to be used for various operations. As MLaaS is a service provided by CSPs they also offer ways to manage the workloads that enable easy data pipeline, perform ML experiments and make work of the data engineers easy.
  • Access to ML Tools: MLaaS offer tools that can be used for predictive analysis and data visualization. These service providers also have APIs which can be readily used for healthcare, sentiment analysis, face recognition and others.
  • Cost efficiency: Building an ML workstation from scratch can be very expensive and maintenance of these systems is not easy. The GPU costs a lot of money compared to the TPU provided by google or other CSPs and hence using the ML services becomes easy and budget-friendly.
  • Ease of use: With MLaaS, the service provider’s data center handles the actual computation, making it very convenient for any business to use.

Why Choose AudvikLabs?

  • We have Subject Area Experts in ML who are well trained in different platforms for ML development and have had the experience of working on countless ML App Development projects.
  • Availability of a wide array of MLaaS tools for clients to choose from depending on their custom requirements.
  • Dexterity with regards to the choice of platform and the method of approach for your project.
  • Budget-friendly/ Cost-effective medium for your ML program as opposed to other premium IT firms.
  • A quick and easy mode of delivery, which is backed by our on-time and successfully completed ML projects.

Get Started Now

MLaaS offers a great number of tools and services that will help you to work more efficiently and tackle multiple problems faced by business. It is a crucial part of building the future of your organization

For more information please write to us at  or give us a call +91 80-43779824

Get Started Now

For more information, please write to us at or give us a call +91 80711 76992

Frequently Asked Questions

Machine learning algorithms are classified into four types:  

  1. Supervised algorithms: A set of algorithms for learning from labelled data, such as images labelled with whether or not a human face exists in the image. To learn from data, algorithms rely on supervisors (labelled data), such as regression, classification, object detection, segmentation, and so on.
  2. Non-supervised algorithms: A collection of algorithms for learning from data that lacks labels or classes, such as a set of images given to group similar images. These algorithms do not require supervisors for training and attempt to represent the same data in various ways, such as dimensionality reduction, clustering, and so on. 
  3. Semi-supervised algorithms are those that fall somewhere in the middle and use both labelled and unlabelled data. The majority of the data used for these algorithms is unlabelled, but a portion of it is, and the algorithms attempt to detect anomalies in the data, for example, anomaly detection. 
  4. Reinforcement learning algorithms: A collection of algorithms that learn the best actions to take given a given scenario in order to maximise overall reward. In this case, the agent is trained to investigate previously unknown options and scenarios using existing knowledge, such as Q-learning, Deep Q networks (DQN), and so on.

Machine Learning as a Service (MLaaS) is a catch-all term for a collection of cloud-based tools. These tools aim to support data scientists’ and data engineers’ daily work in the same way that cloud-based office suites have revolutionised the office environment. Collaboration, version control, parallelization, and other processes that would otherwise be difficult are made possible by MLaaS tools. When it comes to developing a model though, there are numerous tools to use and processes to monitor. Business reality is never as simple as development theory.

AudvikLabs is a development firm, which caters to various needs of a modern day business model. Our services range from Machine Learning as a Service, Quality Assurance Services, Cloud Implementation, DevOps and Application development. We use some of the most up to date tools to provide a satisfactory outcome to our clients. Some of the tools used by us for MLaS programs are as follows:  

  1. Amazon Web Services Machine Learning 
  2. Google Cloud Machine Learning 
  3. Microsoft Azure Cloud Services 

We provide our customers with a broad range of options to select for depending upon their specific business plans and needs.

During the prototype stage, we host projects (via AWS) to allow us to quickly deliver a solution to you, allowing your developers to begin product integration. Our customers frequently request a quick transition to production, which we handle seamlessly in our hosted environment. We also provide a local cloud hosting facility, since our platform is built entirely on easy-to-manage Docker containers. 

While AI/ML is undeniably a powerfully transformative technology that can provide enormous value in any industry, getting started can be daunting. The good news is that you can begin slowly. Adopting AI/ML into your organisation without a large upfront investment allows you to get your feet wet and begin to figure out how and where AI/ML can benefit your organisation in smaller, easier to manage pieces. The entire process can be briefly summarised in the following steps:  

  1. Select a proper pilot project. 
  2. Avail proper consultation services for your proposed model. 
  3. Prepare, structure and clean your data. 
  4. Propose metrics for the project. 
  5. Explore data with the experts, perform experiments and thus train your model. 
  6. Integrate the model with the end-end lifecycle. 
  7. Push your model into production. 
  8. Roll out patch updates frequently to keep your model up to date.