Due to not needing customized code for data integration and processing, you can get your streaming data analysis and machine learning solutions the way you want in the shortest possible time.
The traditional way to develop data analysis solutions involves several parties, such as domain experts, data scientists, software engineers, and sometimes business analysts. Domain experts and data scientists work together to form solution methods. Data scientists prototype them and test using sample data. Then, the software engineers implement them in a high-performance programming language as robust production-ready code. They may also use open source tools and algorithms in the production implementation. The results of this production implementations must be verified by the data scientists and domain experts. Any issues or bugs have to be solved iteratively. Overall, these development and deployment cycles take a long time.
MLP provides a far faster time-to-solution. MLP allows data analysts to develop and test methods using simple, highly configurable drag-and-drop programming in the MLP environment. This allows the use of components that are developed in high-performance code, compiled, debugged and optimized for production. There is no need to hire a software engineering team to write production-ready applications anymore. The data analysts' data models can be deployed directly in production environment with just one click in the GUI. This process eliminates tedious develop-debug-deploy cycles as well as unnecessary software engineers. The overall result is that data scientists and domain experts can get what they envision with a faster, more robust and cheaper implementation.
The MLP platform is made with the goal of ease of use without sacrificing any power. The innovative platform includes a data collection system, a parsing system, numerous components to build data processing models and algorithms, a reporting system and an alerting system. All of these are seamlessly integrated and supported with a user-friendly GUI. The creation of new data parsing methods, data models or reports use drag-and-drop programming. These items can also be exported and shared with your colleagues who can import them into their personal environment to collaborate with you.
Connect data collectors, build parsers, data streaming models, database tables, reporting and alerting -- all in a drag-and-drop GUI environment. Then start getting insights right away. Find anomalies and drill down into their root causes in the raw data with just a few clicks. Alternatively, let our data solutions team make this for you.
You don't need strong in-house experts in developing data analysis and machine learning software or applications with MLP. Just use our drag-and-drop capability or let us implement it for you. The pre-built and tested, high-performance data processors replaces the work an expert would need to do.
MLP is designed to make machine learning and algorithm implementations as painless as possible. Various algorithms in machine learning as well as supporting algorithms such as filtering, aggregating, joining, transforming, etc. are all built-in with convenient ways to program and use them. All of this is accomplished by drag-and-drop graphical programming. Replace expertise with well-tested, powerful components used in data models. Your data analysts or users do not have to be experts in data science.
Open source tools such as Hadoop, Spark, etc. are so diverse, having so many versions. Having to write code and integrate them is a software nightmare. Maintaining them will cost you millions of dollars per year, and endless headaches. MLP provides you with a fully graphical drag-and-drop environment for you to easily get the results you want.
Since some large data streaming and algorithm components are available for free as open source software, several companies attempt to use them to create their own stream data analysis platforms by themselves. At first, this task appears easy. However, after spending a long time putting together such an environment, they often realize that their environment is not flexible enough to solve all their data analysis needs, is often tuned to solve only one problem, and is hard to maintain due to version conflicts and open source API incompatibilities. After spending many millions of dollars, and losing valuable time and resources, they come to the realization that the direct utilization of open source technologies has a high hidden cost. After this lengthy development period, they end up with an unsuccessful project and get blamed by the management.
MLP is a fully integrated, seamless, end-to-end platform with a highly intuitive graphical drag-and-drop programming feature. MLP is primarily made using proprietary software with some open source components integrated seamlessly into it. It is designed with ease, flexibility and power as goals. MLP eliminates the need to write code for data parsing, analysis and display. This approach saves a lot of agony and cost for the customer in the near term as well as in the long term. Smart customers see the struggle others go through building solutions directly using open source, and readily go the intuitive and powerful MLP route.
Develop and test your data models, and immediately deploy them for production use with just one click. Share your models with others, or alternatively let us make and deliver the solution for you.
The traditional way of implementing machine learning and data analysis begins with writing prototype code to analyze sample data and to develop the algorithms. Then, production quality code is written to process production data at high performance and volume. The production code has to implement the same methods as the prototype, but in a more robust way. This two-step approach is lengthy and prone to bugs and longer time-to-solution. MLP uses a common platform and algorithm bank to realize both production and development needs. Methods developed can be tested on sample data and then run on production data with a simple mouse click. This brings the power to the data analyst or solution engineer to deliver solution faster and more accurately. It also enables collaboration among customers, support engineers, consultants or anyone else involved. They can share their MLP data parsers, models and reports as files, and collaborate.
MLP is made of micro-services. The core micro-services are developed in C++ to reduce executable and memory foot print. MLP uses a lightweight plug-in architecture to add algorithm and data analysis capability. They do not require a JVM. They are perfectly suited for edge computing in IoT environments. The core MLP service implements full stream processing engine, algorithms , cluster management, fault-tolerance, data flow API, etc. MLP services can seamlessly interoperate between edge and cloud. Please click here to read more about edge computing capabilities of MLP.