Machine Learning Solutions Specialist
With the way things have started to progress in Artificial Intelligence and Machine Learning, businesses may now use these areas to gain a competitive advantage.
As a new age CIO or IT Director, a big-bang approach to a relatively new area like machine learning is not recommended; instead, the focus should be on working in an incremental development structure with a global delivery model, which allows Rapid Application Development to bring a working prototype to market faster and use data insights to customize the product to build Scalable Solutions. CIOs can use machine learning to construct precise models in today’s age of massive amounts of data, giving businesses a higher chance of spotting profitable opportunities – or avoiding undiscovered risks.
We use supervised and unsupervised learning, deep learning architectures, and designing, developing, and training deep neural networks at MobiWeb Creations to deliver useful insight on business intelligence and data analytics.
We use machine learning to create models that can analyze more complex data and provide faster, more accurate results — even on a massive scale. We have knowledge in Python NLTK, TensorFlow, and PyTorch, as well as other machine learning technologies. Our machine learning implementations are simple, quick, and API-based.
What is Machine Learning?
Machine learning (ML) refers to a high level of computer and system self-awareness. Unique algorithms can aid devices in comprehending and improving performance based on previous experiences. Machines then apply this information to exponentially improve their ability to function and solve issues with previously unheard-of efficiency.
Machine learning is a subset of Artificial Intelligence (AI) that applies AI to a range of functions. Machines produce highly informed predictions on possible scenarios by creating a mathematical model using sample data.
ML procedures are applicable to practically any industry, from agriculture, banking, and marketing to medical diagnosis, manufacturing processes, and telecommunications, and provide limitless potential in business applications.
Why is Machine Learning Important?
To succeed, you must make well-informed decisions. But how much manual knowledge is enough to have a significant influence on your business through traditional research and analysis? Your systems will optimize with Machine Learning to boost all parts of your business.
Filtering email inboxes, manufacturing equipment optimization, and precision weather forecasting across broad geographical areas are just a few of the applications. Your company will be in a better position since machine learning will provide you with an end-product that responds to market demands more precisely and interacts with customers more efficiently.
Identify trends, optimize corporate processes, or uncover critical revenue streams. There are no limits to what you can do. To improve the present and unleash a safe future, learn from the past.
Machine learning can assist you in a number of ways
Increase Sales:
To enhance your sales, identify future customer behaviors based on their previous purchase history and target them based on supply and demand patterns.
Boost your productivity:
Input large amounts of data into your machine learning system to develop accurate and efficient patterns and insights, boosting company productivity.
Improve Customer Satisfaction:
Improve overall customer happiness by providing customized items and tailored content to your customers with machine learning systems.
Making use of big data:
Master the power of data and turn it into a significant operational advantage for you.
Our approach to building a machine learning solution:
1. Analyse your company’s needs and product specifications
We analyze your tasks, assume the solution, and design the scope of work and development process as soon as you discover the necessity for ML implementation.
2. Collect and analyze data
We analyse your data, show it for better understanding, maybe choose a subset of the most useful data, and then preprocess and convert it to generate a legitimate dataset during this lengthy but crucial step. Following that, we divided the data into three sets: training, (cross)validation, and test. The initial step is to train and specify a model’s parameters. The second step is to fine-tune the model’s settings and parameters in order to get the best results. The third is to assess a genuine model’s ability to accomplish a problem once it has been trained.
3. Feature development
We start adding to data after cleaning it and deleting from it in a crucial data preparation step called feature engineering. Feature engineering, a fundamental component of spot-on model accuracy, is the process of manually creating additional features in a raw dataset using domain knowledge. This necessitates a thorough understanding of a given industry as well as the problem that the model will aid in solving.
4. Development of a model
We’ll train a few models here to see which one produces the most accurate results. We try a variety of models, feature selection, regularisation, and hyperparameter tuning until we find a model that is well-trained — neither underfit nor overfit. For each experiment, we assess model accuracy using the measure that is most relevant for the problem and dataset.
5. Create and deploy a model
The time it takes to deploy a model into production is determined by your business infrastructure, the amount of data you have, the quality of earlier stages, and if you’re employing machine learning as a service.
6. Re-evaluate and revise the model
Even once the model is finished, the project continues. We’ll help you keep track of the metrics and conduct tests to define and improve your model’s performance over time.
Our expertise in machine learning technology
Computer Vision:
With computer vision algorithms, extract relevant information from images and surroundings for face identification, biometrics, transportation, augmented reality, and other applications.
Analyses of customers:
Teach robots to interpret text and speech in the same way as humans do, to extract meaningful information, to locate subjects in text documents, to answer queries to automate customer service, or to create chatbots.
Analytics Predictive:
With the use of historical and current data, see into the future. Remove the guesswork and uncover how your company, customers, or the industry as a whole will develop in the future.
Recommender systems:
Use the same technology that has helped Netflix, Amazon, and Spotify increase conversions. Deliver a tailored customer experience by providing the most relevant material to your users.
Forecasting time series:
To predict trends and seasonal cycles, look for patterns in your historical data. Forecast demand for your items, make adjustments to your strategy or pricing, and forecast competition prices.
Detecting anomalies:
Detects fraud, security issues, data breaches, medical difficulties, structural faults, and other malfunctions by identifying anomalous behavior.
NLP (Natural Language Processing):
Analyze customer behavior, look for data patterns, and create a customer segmentation model to improve targeting, personalization, and the entire customer experience.
Our expertise in machine learning technology
We deliver with the same programme rigour and approach to high quality, without the engagement overheads of larger industry participants, thanks to our team’s background in Top global service providers.
At every stage of the engagement, our worldwide Sales & Delivery presence provides you with local touch-points.
Our expertise in machine learning technology
We deliver with the same programme rigour and approach to high quality, without the engagement overheads of larger industry participants, thanks to our team’s background in Top global service providers.
At every stage of the engagement, our worldwide Sales & Delivery presence provides you with local touch-points.