- July 1, 2021
- Posted by: BRACHIN LLC
- Category: Uncategorized
Big Data Genie™ and AI Genie™
AI Genie™ – All in One AI, Big Data Engine
The Fastest and Most Economical AI, Big Data Engine in the Market
AI Genie™ Value Proposition – Fastest, Lowest TCO. All in One AI, Big Data, IoT and Other Applications. IoT applications using machine data for Genius Manufacturing, Genius IT, Genius Cities, Genius Vehicles with AI Genie™ for IoT
SAP Leonardo Machine Learning: Why Machine Learning and Why Now?
Machine learning is no longer just for smartphones or game shows. Here’s how to develop a strategy that will change the basis of competition in your industry. BRACHIN LLC uses SAP Leonardo to deliver Intelligent Business Processes, Intelligent Infrastructure, Digital Assistants and Bots.
Big Data is a natural partner of Artificial Intelligence. More data leads to accurate forecasts and enhanced machine learning models. The value of Big Data and Artificial Intelligence comes from making decisions and business insight. Data by itself has no value. Value is derived by leveraging the data-driven-action.
BRACHIN LLC uses an Artificial Intelligence Development Platform. We control the full life cycle from Data Extraction, Data Transformation, Training and Optimizing to AI Model Deployment. Big Data Genie™ and AI Genie™ are the ideal tools for Data Scientists. BRACHIN LLC has experience delivering the following AI Use Cases:
PREDICTIVE SALES and SALES FORECASTING
Artificial Intelligence enables predictive sales. You can forecast sales accurately based on all customer contacts and previous sales results. We have built Sales Dashboards with the scores of the leads and Sales Pipelines. The Dashboards help your Sales Team to concentrate on selling to the right people. This lowers expenses by channeling the resources to the right lead. AI improves forecasting accuracy by 10 percent to 20 percent. This typically translates into a 5 percent improvement.
AI Sales Forecasting also enables to adjust inventories and have the right amount of stock all the time. This reduces customer churn and increase the customer loyalty.
MARKETING RECOMMENDATION ENGINE
AI models use customer data to retain and upgrade customers with personalized recommendations via email, site search or other channels.
MARKETING SOCIAL ANALYTICS & AUTOMATION
AI models analyze social media content to analyze and create marketing campaigns.
MARKETING PRICING OPTIMIZATION
Dynamic pricing, demand pricing enable companies to optimize pricing while maximizing revenue and profitability.
OPERATIONS – PREDICTIVE MAINTENANCE
Maintenance of a company’s equipment is essential to the optimal operations. Predictive maintenance reduces unplanned downtime due to unexpected failure and the subsequent economic loss that it results in. It leads to max performance time, increased productivity, and improved planning.
OPERATIONS INVENTORY & SUPPLY CHAIN OPTIMIZATION
AI models help companies streamline their most time-intensive back-office processes. Our technologies automatically extract data from ERP systems, then we use AI models to forecast and create trend analysis. AI algorithms help improve material flow, fleet management, warehouse administration, logistics processes, and freight processing.
Artificial Intelligence (AI) and Big Data turn large amounts of structured and unstructured data into logistics and supply chain insight and allow companies to optimize their supply chains. The benefits are accurate time of delivery estimates, optimize vehicle routes and sequence deliveries, efficient shipment consolidation, insight into damage claims and returns and traceability, accountable sourcing.
HR – HIRING
Leverage AI to attract, screen, engage and hire the best employees. Hiring is a challenging. Which candidate, starting at a specific position, will contribute more to the company? AI augments HR employees in various parts of the hiring process such as finding qualified candidates, interviewing and selecting the best candidates.
Performance Management: Manage your workers’ performance without harming their motivation. Pursue their KPI’s on your dashboard and give continuous input. This would build the representative fulfillment and lower your organization’s worker turnovers.
HR – Retention Management
Predict which employees are likely to leave and improve their job satisfaction to retain them. Detect the underlying reasons for their motive for seeking new opportunities. By keeping them at your organization, lower your human capital loss.
FINANCE FRAUD DETECTION
Leverage AI to detect fraudulent and abnormal financial behavior, and/or use AI to improve general regulatory compliance matters. Lower your operational costs by limiting your exposure to fraudulent documents.
FINANCIAL ANALYTICS PLATFORM
Banks and Financial Institutions use AI prevent fraud and detect suspicious activity. AI can also be used for apps that scan checks and make deposits to system that automate the movement of funds, based on interest rates. Quants are using AI algorithms for trading using array of factors, information and variables. Leverage machine learning, Natural Language Processing and other AI techniques for financial analysis, algorithmic trading and other investment strategies or tools.
FINANCE CREDIT LENDING / SCORING
Use AI for robust credit lending process.
FINANCE REGULATORY COMPLIANCE
Use AI model Processing to scan legal and regulatory text for compliance issues.
CYBERSECURITY
Our models analyze vast amounts of data about attacks and responses to uncover more effective methods for responding to different scenarios. We also have experience with UEBA – User and Entity Behavioral Analytics. AI algorithms move protection beyond increasingly ineffective whitelists, blacklists and firewalls by detecting unusual activity and patterns, including the movement of data packets.
HEALTHCARE
Researchers and healthcare providers are increasingly using machine learning, deep learning and other types of AI to go over data and, using analytics, spot patterns that help healthcare providers treat at risk groups more effectively. Pharmaceutical companies and biomedical device makers are tapping AI to develop algorithms that produce more effective drugs. Patient Data Analytics: Analyze patient and/or 3rd party data to discover insights and suggest actions. Greater accuracy by assisted diagnostics. Lower the mortality rates and increase the patient satisfaction by using all the diagnostic data available to detect the underlying reasons for the symptoms.
Personalized Medications and Care: Find the best treatment plans according to patient data. Provide custom-tailored solutions for your patients. By using their medical history, genetic profile, you can create a custom medication or care plan.
Drug Discovery: Find new drugs based on previous data and medical intelligence. Lower your R&D cost and increase the output. All leading to greater efficiency. Integrate FDA data and you can transform your drug discovery by locating market mismatches and FDA approval or rejection rates. AI GENIE™ helps healthcare companies to perform Patient-Less or Synthetic Clinical Trials. Companies has vast amounts of data to be leverage for Patient-Less Clinical Trials. AI GENIE™ allow companies to use exiting patient data lakes or data repositories to generate conclusions. Electronic medical records (EMRs) can be used to generate Patient-Less Clinical Trials. AI GENIE™ allows Biopharmaceutical companies to create digital platforms with medical data to conduct synthetic clinical trials and accelerate research activities and reduce cost. The platforms create the basis for Digital Research and Development.
AI Genie™ Performance
With a large amount of data, horizontal scaling, i.e. splitting the data across multiple nodes in a cluster, can ensure reasonable response times only to a certain extent. Even then, the cost of hardware and energy can be prohibitively high. Since data volumes continue growing exponentially, it is clear that such solution cannot be sustained.
BRACHIN LLC’s algorithmic approach to solving the performance and scalability challenges by means of advanced multidimensional indexing is the only way to address the sustainability problem for Big Data analytics. AI Genie™ users benefit from higher performance, reduced hardware and energy requirements.
AI Genie™ can be used in Data Warehouse and IoT environments to drastically reduce total cost of ownership and improve performance of analytic applications and desktop analytic tools such as Tableau, Qlik Sense, Apache Zeppelin, etc. AI Genie™ can run on large powerful machines as well as on edge devices as small as Raspberry Pi. Fog analytics in IoT is gaining momentum, and AI Genie™ can be used to deliver exceptional performance on low power ARM devices.
BRACHIN LLC’s AI Genie™ Analytics System software handles a variety of analytic tasks in a variety of environments. AI Genie™ efficiently runs execution plans arising from queries, ETL, machine learning tasks, etc. Its sweet spot is ad-hoc queries with predicates. AI Genie™ executes them with record breaking speeds on less hardware than any competitor, cutting the cost of performance by orders of magnitude. Cost of performance gains are summarized below.
AI Genie™ technology is based on unique multidimensional indexing techniques.
AI Genie™ Technology
Database technologies have a long history of evolution. Recent trends lead towards specialization of databases rather than attempting to produce a single answer to all challenges and workloads. “One size fits all?” lecture by Michael Stonebraker, MIT professor and well-known database expert, set start to a number of initiatives.
In the analytics domain, columnar data organization became popular because it addressed the requirements of the analytic workloads better than row storage. Columnar organization allows compressing data better and fitting more data into memory. However without indexing, answering a query still requires a full scan of all the data records. Traditional indexing remains expensive; it takes time and space. Analytic workloads usually require combining several individual column indices to obtain an answer to a query, and the number of combinations of columns involved in queries may be huge.
Data in an enterprise originates from certain business processes, such as sales of goods, mobile phone calls, messaging, Internet access, industrial sensor data, etc. Business requirements yield assigning roles, such as dimensions, to attributes that drive the business process whose data is captured in the data warehouse. AI Genie™ Analytics System stores data from multiple dimensional attributes directly in its “AI Genie™” data structure that also serves as an index. Other attributes, playing measure roles, can be stored in columns for efficiency. But most of the business questions restrict one or more dimensional attributes, and the “AI Genie™” data structure plays a central role.
Machine Learning with AI Genie™
AI Genie™ Analytics System offers in-database Machine Learning with a framework that supports algorithms as plug-ins as well as several algorithms out of the box, such as Bayesian, Multilinear Regression, Logistic Regression, Decision Trees, Random Forests, Time Series, and Ensemble Models. More algorithms are in the works.
The framework offers model management (creation, storage, application, etc.) and parallel execution of modeling tasks. Data partitions are units of execution for Machine Learning just as they are for other tasks. For some algorithms (Bayesian, regressions, etc.), models created on different partitions may be combined into a single model; for others AI Genie™ creates ensemble models from partition models. Since most commonly used partitioning schemes in AI Genie™ are unbiased, ensemble models work well for algorithms like Decision Trees, Random Forests, Time Series, etc.
Modeling tasks can be initiated via straightforward command which specify which algorithm to use, on what data and what roles should relevant attributes play in the process: Predictor, Target, Parameter, etc. The rest of the information about attributes, such as data types or some statistics is already captured in the AI Genie™ metadata and does not need to be repeated. Similarly, AI Genie™ keeps model metadata. AI Genie™ builds a separate model for each value of Parameter, so one command may actually build thousands of models. Model data can also be queried.
Some Machine Learning applications on AI Genie™ involve creating a population of models and using genetics algorithms to breed more models with increasing predicting power as well as to prune out weak and faulty ones. Predictions can be invoked via SQL-like or Natural Language statements. They can be called from client tools like Apache Zeppelin using JDBC statements. Users can execute Machine Learning tasks and make predictions from the same environment as queries. The combination of query analytics and predictive analytics makes AI Genie™ a very powerful BI tool.