AI News, Data Science Platforms Seen as Difference-Makers

Data Science Platforms Seen as Difference-Makers

Based on today’s trends and a new survey by Forrester, it seems likely that much of the work that data scientists do will revolve around centralized platforms that help to organize not just the data and the tools, but data scientists themselves.

that can centralize the tools and processes needed to integrate and explore data, develop and deploy advanced analytic models, and to streamline communication and collaboration among principals—all while adhering to security and governance principles.

While 99% of the 208 people who participated in the study agreed that data science is important to their companies, not all of the companies were equal in their capability to squeeze business advantages out of their data (which also should come as no surprise).

While only 26% of firms have invested in a single data science platform to manage their data science work, the Forrester study found that Leaders were nearly twice as likely to have already implemented a data science platform or to be planning to deploy one in the next two years (85% for Leaders, 47% for Laggards).

“A lot of them have been spending massive amounts of time and money and resources building data infrastructure, but they’re not yet doing data science work and actually applying models from that data infrastructure,”

An enterprise data science platform helps data scientists get the most value out of their data by managing the big data analytics lifecycle and standardizing routine processes while enforcing security and governance.

“It’s allowing them to do things like reuse work that teams are doing, to share knowledge across teams, and to accelerate what they’re doing in terms of applying the models they develop in their actual business.

While we’re not likely to see a repeat of the rigid software change management (SCM) environments that enterprise IT have been laboring under since the passage of the Sarbanes-Oxley Act so many years ago, there is a definite trend to rein in some of the more wild and wily aspects of one-off data science projects, and toward more management and reproducibility.

“We’re seeing this shift away from closed, legacy systems to leveraging the power of open source, but still making sure it’s secure, making sure there’s data governance, making sure there’s best of class engineering practices like code reusability and reproducibility,”

But as the big data analytics market matures and new categories of software tools are established, it’s clear that enterprise capabilities will be near the top in terms of importance, and to that end, the rise of data science platforms makes sense.

What Is A Data Science Platform And How Do I Choose One?

In fact, the new data science platform market is predicted to become a $385.2 billion global business in less than a decade, and Forrester named data science platforms a top emerging technology just last year.

That work usually includes integrating and exploring data from various sources, coding and building models that leverage that data, deploying those models into production, and serving up results, whether that’s through model-powered applications or reports.

There are many reasons that some companies pull ahead — including bigger analytics budgets and an established data science roadmap — but one that is often overlooked is the reproducibility of the work being done.

But that isn’t the case for teams that are collaborating in a shared workspace with features that are designed to notify users of updates, track changes, and monitor the health of your projects.

Plus, because there are a lot of moving parts to every data science project — the data itself, code, models, and outputs — a good platform will offer solutions for organizing these pieces intuitively.

(You can read more about how trends in open-source development are transforming enterprise data science in our latest white paper.) Ultimately, a data science platform will better serve your needs if you can use the packages and languages you want.

Closed platforms that rely on proprietary solutions will always be limited — by slow-moving updates, a lack of innovation, and finite integrations with other tools — plus, the data scientists you hire will need to learn new skill sets to work within them.

It’s a common data science scenario: A data scientist has built a model that will power a product recommendation engine, and now the outputs of that model need to be served up to customers shopping on your website.

(We let your data scientists build that model in whatever language they prefer, whether that’s Python, R, or Spark, and deploy it instantly behind an API.) Your engineering team can then take that API and integrate it anywhere, without recoding.

Other features to look for: tools that help your data scientists monitor the health of their models (such as drift detection or scoring) and the ability to deploy multiple versions of the same model for testing.

19 Data Science and Machine Learning Tools for people who Don’t Know Programming

This article was originally published on 5 May, 2016 and updated with the latest tools on May 16, 2018.

Among other things, it is acknowledged that a person who understands programming logic, loops and functions has a higher chance of becoming a successful data scientist.

There are tools that typically obviate the programming aspect and provide user-friendly GUI (Graphical User Interface) so that anyone with minimal knowledge of algorithms can simply use them to build high quality machine learning models.

The tool is open-source for old version (below v6) but the latest versions come in a 14-day trial period and licensed after that.

RM covers the entire life-cycle of prediction modeling, starting from data preparation to model building and finally validation and deployment.

You just have to connect them in the right manner and a large variety of algorithms can be run without a single line of code.

There current product offerings include the following: RM is currently being used in various industries including automotive, banking, insurance, life Sciences, manufacturing, oil and gas, retail, telecommunication and utilities.

BigML provides a good GUI which takes the user through 6 steps as following: These processes will obviously iterate in different orders. The BigML platform provides nice visualizations of results and has algorithms for solving classification, regression, clustering, anomaly detection and association discovery problems.

Cloud AutoML is part of Google’s Machine Learning suite offerings that enables people with limited ML expertise to build high quality models. The first product, as part of the Cloud AutoML portfolio, is Cloud AutoML Vision.

This service makes it simpler to train image recognition models. It has a drag-and-drop interface that let’s the user upload images, train the model, and then deploy those models directly on Google Cloud.

It also provides visual guidance making it easy to bring together data, find and fix dirty or missing data, and share and re-use data projects across teams.

Also, for each column it automatically recommends some transformations which can be selected using a single click. Various transformations can be performed on the data using some pre-defined functions which can be called easily in the interface.

Trifacta platform uses the following steps of data preparation: Trifacta is primarily used in the financial, life sciences and telecommunication industries.

The core idea behind this is to provide an easy solution for applying machine learning to large scale problems.

All you have to do is using simple dropdowns select the files for train, test and mention the metric using which you want to track model performance.

Sit back and watch as the platform with an intuitive interface trains on your dataset to give excellent results at par with a good solution an experienced data scientist can come up with.

It also comes with built-in integration with the Amazon Web Services (AWS) platform. Amazon Lex is a fully managed service so as your user engagement increases, you don’t need to worry about provisioning hardware and managing infrastructure to improve your bot experience.

You can interactively discover, clean and transform your data, use familiar open source tools with Jupyter notebooks and RStudio, access the most popular libraries, train deep neural networks, among a a vast array of other things.

It can take in various kinds of data and uses natural language processing at it’s core to generate a detailed report.

But these are excellent tools to assist organizations that are looking to start out with machine learning or are looking for alternate options to add to their existing catalogue.

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