AI News, Infrastructure and Development for DataScience

Infrastructure and Development for DataScience

Coming from a classical IT background in terms of software development it took us a while to arrive at an architecture that was capable of fulfilling our needs for Data Science projects.

Major differences: From a classical software development perspective Data Science tasks are not pure programming tasks in a sense, that programming is more deterministic, you usually know what you want to achieve and you only look for an optimal way among the possible solutions.

So the whole process becomes a two step process: The second point is already classical software development, while the first is more of an investigation where the output of the individual tasks needs to be stored in order to come back to them in subsequent iterations if needed.

To achieve both in a single environment architecture we need to enable users to have read access to the production data and to have a simple tool for extracting the data without affecting the performance of the production database.

Depending on the specifics of your environment you might opt for having two development environments, in our case our infrastructure is Data Science oriented and the software oriented development takes place in a separate environment (other team), so we have a single environment shared for our internal software and data science development as most changes are driven by the modelling needs.

Looking at the two steps in the above process our repositories are constructed accordingly, with the first aimed at the data science development where the review is more logical in terms if the assumptions make sense, if all requirements to use a given method/model are satisfied etc..

There is certainly no single infrastructure that suits all the requirements perfectly, although the standard development, acceptance, production infrastructure works well, nonetheless you have to be aware that the requirements a Data Science team has on the environment infrastructure differ from a software development team and adjust your infrastructure accordingly.

Data Science Data Architecture

It is intended for various audiences: for IT admins to better understand the needs of data scientists, for data scientists to better articulate their needs and in general for companies who are looking to setup a data science work stream.

In both worlds production environment means the same: a stable, audit-able environment that interfaces with the business under known conditions (workload, response time, escalation routes, etc.).

Table 1 spells out the criteria for the different environments and shows that the data science model development environment is neither an IT development environment nor an IT production environment.

model development environment needs to have development status in the following aspects: The need for a separate model development and production environment Not all analytical models are intended to make it to a production environment, although, the models that are most valuable are not one-time executions, but are embedded, repeatable scoring generators that the business can act upon.

The model development takes place in a relatively unstructured environment that gives the possibility to play with data and experiment with modeling approaches.

Note that developing the model in the same environment as the scoring, frequently implies that a new version of the model needs to be ready for the upcoming scoring moment, i.e.

Top Tools for Data Scientists: Analytics Tools, Data Visualization Tools, Database Tools, and More

Data scientists are inquisitive and often seek out new tools that help them find answers.

Overall, data scientists should have a working knowledge of statistical programming languages for constructing data processing systems, databases, and visualization tools.

however, not all data scientist students study programming, so it is helpful to be aware of tools that circumvent programming and include a user-friendly graphical interface so that data scientists’

This tool turns raw data into real-time insights and actionable events so that companies are in a better position to deploy machine learning for streaming data.

An iterative graph processing system designed for high scalability, Apache Giraph began as an open source counterpart to Pregel but adds multiple features beyond the basic Pregel model.

A framework allowing for the distributed processing of large datasets across clusters of computers, the software library uses simple programming models.

This tool is a data warehouse software that assists in reading, writing, and managing large datasets that reside in distributed storage using SQL.

Data scientists use this tool to build real-time data pipelines and streaming apps because it empowers you to publish and subscribe to streams of records, store streams of records in a fault-tolerant way, and process streams of records as they occur.

An open source Apache Foundation project for machine learning, Apache Mahout aims to enable scalable machine learning and data mining.

Mesos abstracts CPU, memory, storage, and other resources away from physical or virtual machines to enable fault-tolerant, elastic distributed systems to be built easily and run effectively.

platform designed for analyzing large datasets, Apache Pig consists of a high-level language for expressing data analysis programs that is coupled with infrastructure for evaluating such programs.

A wide range of organizations use Spark to process large datasets, and this data scientist tool can access diverse data sources such as HDFS, Cassandra, HBase, and S3.

BigML makes it simple to solve and automate classification, regression, cluster analysis, anomaly detection, association discovery, and topic modeling tasks.

Python interactive visualization library, Bokeh targets modern web browsers for presentation and helps users create interactive plots, dashboards, and data apps easily.

Users can solve simple and complex data problems with Cascading because it boasts computation engine, systems integration framework, data processing, and scheduling capabilities.

robust and fast programming language, Clojure is a practical tool that marries the interactive development of a scripting language with an efficient infrastructure for multithreaded programming.

An advanced machine learning automation platform, DataRobot helps data scientists build better predictive models faster.

The company strives to make deep learning relevant for finance and economics by enabling investment managers, quantitative analysts, and data scientists to use their own data to generate robust forecasts and optimize complex future objectives.

An experimental app, Fusion Tables is a data visualization web application tool for data scientists that empowers you to gather, visualize, and share data tables.

With ggplot2, data scientists can avoid many of the hassles of plotting while maintaining the attractive parts of base and lattice graphics and producing complex multi-layered graphics easily.

Java is a language with a broad user base that serves as a tool for data scientists creating products and frameworks involving distributed systems, data analysis, and machine learning. Java now is recognized as being just as important to data science as R and Python because it is robust, convenient, and scalable for data science applications.

Its Notebook, an open source web application, allows data scientists to create and share documents containing live code, equations, visualizations, and explanatory text.

The KNIME Analytics Platform is a leading open solution for data-driven innovation to help data scientists uncover data’s hidden potential, mine for insights, and predict futures.

high-level language and interactive environment for numerical computation, visualization, and programming, MATLAB is a powerful tool for data scientists.

Matplotlib is a Python 2D plotting library that produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

GNU Octave is a scientific programming language that is a useful tool for data scientists looking to solve systems of equations or visualize data with high-level plot commands.

pandas is an open source library that delivers high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

A tool for making data science fast and simple, RapidMiner is a leader in the 2017 Gartner Magic Quadrant for Data Science Platforms, a leader in 2017 Forrester Wave for predictive analytics and machine learning, and a high performer in the G2 Crowd predictive analytics grid.

The Scala programming language is a tool for data scientists looking to construct elegant class hierarchies to maximize code reuse and extensibility.

To get the answers, I asked Dr. Nicole Forsgren, director of organizational performance and analytics at Chef Software, and Ohad Assulin, chief data scientist at Hewlett Packard Enterprise Software, to explain what data scientists actually do and how you as a software engineer can work effectively with them—and perhaps add a few of those in-demand data science skills to your own CV.

While the traditional BI role was typically more database-centric, often analyzing offline data, data scientists tend to have a stronger background in statistics, predictive analytics techniques, and the implementation of algorithms on real-time or near-real-time data.

To understand what data science means for software developers, you need to understand the answers to three questions: To make your SDLC process more efficient, Forsgren says, you need to think about your goal and keep in mind that performance and effectiveness are best measured at the team level, rather than at the individual level.

If that data isn’t available, the data scientist will need to work with the developers and the operations engineers to make that data available by getting access to the source code repository (such as Git).

But a good data scientist first takes a step back, asking, “What are the questions I can ask?” and, “What data do I need to answer them?” The data scientistmay need to ask developers to add hooks to capture additional data, if the existing production data is insufficient.

When data scientists are developing software, they could be writing anything from pseudo-code to fully productized code, for things from data collection to number crunching to visualizing and presenting the results.

If you’re asking for insight into the kinds of problems on which they can help or an analysis based on data, you’ll get a report or presentation expressed in plain business language that all stakeholders can understand.

Assulin says that data scientists must give the consumer something that’s easy to work with, whether in the form of a library or microservice, that integrates easily with the main product’s code.

However, Assulin cautions that a lone data scientist may be limited by not having anyone else to bounce ideas off of, and the highly mathematical nature of the code can make code reviews difficult.

Startups with a well-defined data problem should include their data scientists in the teams, whereas larger organizations with a variety of problems and data will do better with a team of data scientists who can support one another while providing data science services to the rest of the organization.

One of Assulin’s roles is to educate business analysts and product owners on techniques and analytical tools that are available to the business, such as explaining how the data scientist can make predictions about the future based on past history, gather insights with data clustering, or make recommendations based on user behavior.

Forsgren agrees and adds that you should also ask business-related questions, such as, “How do you see a data scientist adding value to my business?” The first data scientist to join your business should have initiative and understand what value he or she can bring.

Less experienced candidates should be able to cite at least some contribution to a data science project, for example as part of a data science boot camp or university-level project.

Assulin says that when he’s recruiting data scientists, his baseline is a computer science degree, or at least significant experience in software development, because data scientists are expected to write production-level code that is part of the product.

As a developer with a clear understanding of data science concepts and how data scientists work, you'll be positioned to collaborate with data scientists while expanding your own expertise in this growing discipline.

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