Machine Learning
Automation
with continuous deployment

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Our Services

We can help you manage and automate any step of your data/machine learning pipeline or the entire pipeline.






Question Formulation and Stating the Hypothesis

The very first step in any machine learning project is formulating the question and stating the hypothesis that will be tested or what results are sought and what predictive capabilities are sought from the machine learning model that will ultimately be deployed. As this step requires knowledge of the business, we work closely with the customer to determine what questions they seek to answer or predictions they seek to make using their data assets.





Data Extraction and Engineering

Data extraction involves sourcing the relevant data from different data sources for the ML task in question. The most crucial part of this step is to ensure the following

Data Availability

Is there data available that answers the question being asked?

Data Location

Where is that data located?

Data accessibility

How can that data be accessed?

We understand that data is the most valuable asset most organizations possess so we take the utmost care in securing the data and making sure all data assets are safe-gaurded during all steps in the process.

Data Cleaning and Sanitization

Data cleaning involves cleaning up the data and looking for and addressing all or some of the following problems that exist in raw data sets.

Data duplication
Null and empty values
Incorrect data format
Missing data points or features

In this step, we prepare the data for the machine learning tasks at hand including splitting the data into training, validation, and test sets. We also apply data transformations and feature engineering techniques to the data that solve the target task.

Data Visualization and Analysis

In this step, we perform exploratory data analysis on the data set to summarize the main characteristics with visual methods and/or using statistical models. This also involves
Understanding/exploring the schema and characteristics of the data that are expected by the model.
Identifying and preparing the data as well as engineering features that are needed for the model.

We hold true to the adage that a picture is worth a thousand words. Using the data, we tell a story using charts and graphs. This aids in our understanding of the data in questions as well as the problem at hand and helps ux explain the nature of the data to the customer.





Model Generation and Evaluation

In this stage, we implement different algorithms with the prepared data to train various Machine Learning models. In addition, we subject the implemented algorithms to hyperparameter tuning to get the best performing models. The output of this step is a trained model. The model is evaluated on a test set to evaluate the model quality and the output of this step is a set of metrics to assess the quality of the model. The model is then confirmed to be adequate for deployment—that is its predictive performance is better than the baseline established by the existing model.

Model Deployment

The validated model is then deployed to a target environment to serve predictions. This deployment can be one of the following:

Microservices

Microservices with a REST API to serve online predictions.

Embedded Model

An embedded model to an edge or mobile device.

Batch Prediction

Part of a batch prediction system.





Model Monitoring

As data storage costs continue to decline and more data becomes available, we continuously monitor the model for its predictive ability and to determine whether a creating new baseline performance metric is necessary or if there is a need to deploy an entire new model. With continuous monitoring, we are able to detect performance degradation and model staleness. This acts as a cue for a new iteration and retraining of the model on new data.

Our Team

Chandra Sabbavarpu

Faisal Akhtar

About Us

Who we are

We provide managed service to companies to help them get the best ROI on their data assets and data science investments.
Simply put, you point us to your problem and your data, we will take care of the rest.

What we Do

We offer fully managed Machine Learning Ops capabilities to our customers. We provide an end-to-end machine learning development process to design, build and manage testable and changeable machine learning power software and solutions.

Our Experience

Customer Churn

Imagine the amount of marketing effort it took your organization to convert a potential customer to a paying customer. Wouldn’t you want to know if and when that customer is likely to stop using your product or worse, switch to a competitor? Now imagine this scenario not for a single ... customer but a set of customers. Perhaps you are experiencing this scenario already. You are losing paying customers and you don’t know why and what’s worse, you don’t know what to do to stop it.
If yours is a subscription based based or if your think that any recent moves by your company have eroded brand loyalty and you want to discover when in the lifecycle a given customer is likely to stop being a customer, we can use machine learning to generate a predictive model for you that will predict when a customer is likely to stop being a customer. Did you know that acquiring a new customer can cost 5 times as much when compared to the cost of retaining and existing customer?>

Fraud Detection

Most businesses have written off a certain number of fraudulent transactions as simply the cost of doing business. Some publications have estimated that as much as 9.7% of annual revenue is lost to fraud. Fraud also impacts the bottom line of the insurance claims industry. ... We create machine learning models that help companies predict whether a transaction is fraudulent and we also help insurance providers predict whether a submitted claim is potentially fraudulent. This minimizes the losses from transaction reversals. For insurance businesses, this minimizes the need to recover lost funds paid to fraudulent or bogus claims.
Fraud detection use cases include cell phone carries, credit card transactions, and government procurement. The machine learning models we develop can help your organizaions augment your existing control systems.

Sales forecasting

Forecasting sales is one of the best applications for using machine learning models. Sales forecasts can be used to benchmark existing sales numbers, plan for resources needed for the future and help measure the impact of marketing efforts. ... Where traditional sales forecasting techniques can fail to take into consideration that many factors that impact sales and future sales numbers, machine learning models can take into account almost any factor that may impact sales and help you improve your bottom line since a machine learning model lacks the biases a human prediction usually contains.

Product Recommendations

Whether you are a subscription business, an online retail store or social media site, having a good recommendation system can mean the difference between staying in business and going out of business. Where many businesses are still using heuristic recommendation systems,
... we can help your organization unlock the power of your data by building a machine learning based recommendation system. A recommendation system is arguably the most valuable implementation of machine learning and your business cannot afford to be left behind. A misnomer that is prevalent regarding recommendation systems is that they only help improve sales. Not only do ML based recommendation systems help improve sales, they also help businesses derive continuous from their customers and their data. These insights include new product development, customer retention and even new market expansion.

Machine/Equipment Failure Prediction

In the manufacturing industry, one very important insight machine learning can provide is predicting mechanical failure. Where many companies are using time as the best measure of whether a given mechanical part will fail, the problem is that time is only a limited predictor of possibility of
... mechanical failure. Other factors that come into play are such as sound and vision can help with machine/equipment failure prediction. While mechanical failures can cause production down time and even result in personal injury, machine learning models can help you predict the possibility of the failure of the mechanical part in your manufacturing business and reduce downtime as well as prevent injury.

Customer Segmentation

A key challenge that every marketing team must solve is resource allocation. Customer attention spans have dwindled and will continue to dwindle in the age of information overload. In a world with limited marketing budgets, customer segmentation can help marketing teams ... minimize marketing waste, reduce cost of new customer acquisition and increase marketing efficiency and ROI. Not every customer is the same and certain types of customers are more valuable over their lifetime than others. Therefore, businesses cannot serve the same ads, products and experiences to every customer. Customizing the experience results in better customer retention and provides business insights into which customer segments are more valuable than others. We can help your business unlock these insights from your data.

Contact Us

3104 Lord Baltimore Dr Suite #207 Windsor Mill, MD. 21244

Info@asciilabs.com

+1 4437610655