It is more important to implement & integrate Artificial Intelligence especially in a highly effective manner within the specific business environment to ensure optimum results out of the whole process.
Developing a unique strategy for AI integration helps to optimize your existing business and also identify the places where AI can bring the most value.
While planning to begin with your AI project, you need to build a deep technical expertise team for developing Artificial Intelligence-Driven products.
Predictly here plays a very critical role for businesses of various domains by offering comprehensive expertise in providing customized AI services like Artificial Intelligence-based Services for building Chatbots, Custom AI solutions, and Face detection Applications.
Using Artificial Intelligence, the data collection process becomes easy and fast, which often requires days or even months in a manual process. Starting from the stage of data collection to the stage of data extraction, verification, fraud detection everything can easily be done by using AI.
Market Intelligence: With the help of AI, we can build a system where we can track the sentiments, what people are talking about, how they react to the products, etc. To build a market intelligence system we will have to incorporate multiple models that will give us different pieces of information.
Vehicle Damage Detection: We can use an object recognition model to give better solutions to this problem. The object recognition model should be an instance segmentation model that permits us to distinguish pixel-wise areas for our classes or labels.
AI ChatBot provides a solution for this such that it can easily understand human speech and provide a direct solution to client or customer issues by using Natural Language Processing techniques.
AI-Based models will help companies by analyzing already existing or past
customer information and then apply it to new customers in a faster way and with accurate results.
AI-based automatic call transcription models or systems are essential in telecommunication industries to better understand customer emotions towards their services and products.
o Data Collection/Extraction
o Data storage
o Data Versioning
o Data Labeling (Annotated data)
o Data Processing
o Frameworks (Tensorflow, Pytorch, Fast.ai, Keras)
o Software Engineering (Git, Python, Jupyter Notebook, VS Code type editor, Design Pattern, Algorithm and Data Structure)
o Machine Learning/Deep Learning
o Cloud Services (GCP, AWS, etc)
o Hyperparameter Tuning (Optimization techniques, parameter-searching techniques, Ray tune, SigOpt)
o Experiment Management (Tensorboard, Comet, MLflow Tracking)
o REST API Deployment
o Model Serving
Our ability to use the data for the decision making process is either lost or not maximized at all too often, despite the recent increase in computing power and access to data over the last couple of decades. A number of businesses seem to not have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. Predictly starts with why data science and what methodology needs to be followed in every specific situation.
Our focus here at Predictly is to produce a clean, high-quality dataset whose relationship to the target variables is understood and highly compatible. We aim to locate the dataset in the appropriate analytics environment so your business is ready to model. We develop a solution architecture of the data pipeline that refreshes and scores the data regularly.
Predictly helps in determining the optimal data features for the machine-learning model for each specific business domain and create an informative machine-learning model that predicts the target most accurately. We also create a machine-learning model that’s suitable for production.
Predictly works in the direction to deploy models with a data pipeline to a production or production-like environment for final user acceptance. Once you have a set of models that perform well, you can operationalize them for other applications to consume. Depending on the business requirements, predictions are made either in real time or on a batch basis.