AI News, Learn and practice Machine Learning with BigML

Learn and practice Machine Learning with BigML

The world and the global workforce cannot afford to stay behind the curve on this key technology enabler, so we urgently need to produce a much larger group of ML-literate professionals such as developers, analysts, managers, and subject matter experts.

To meaningfully contribute on this matter, the BigML Team holds Machine Learning crash courses throughout the year, ideal for advanced undergraduates as well as graduate students and industry practitioners seeking a quick, practical, and hands-on introduction to Machine Learning.

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.

10 Offbeat Predictions for Machine Learning in2017

As each year wraps up experts pull their crystal balls from their drawers and start peering into it for a glimpse of what’s to come in the next one.

All five share the common traits of large scale network effects, highly data-centric company cultures and new economic value-added services built atop sophisticated analytics.

However, the trillion dollar question is how legacy companies (i.e., non-tech firms with rich data plus smaller technology companies) can counteract and become an integral part of the newly forming value chains to be able to not only survive, but thrive in the remainder of the decade.

Today, these firms are stuck with rigid rear view mirror business intelligence systems and archaic workstation-based traditional statistical systems running simplistic regression models that fail to capture the complexity of many real life predictive use cases.

They will keep investing in algorithm-based startups with the marketable academic founder resumes, while perpetuating myths and creating further confusion e.g., portraying Machine Learning as synonymous with Deep Learning, completely misrepresenting the differences between Machine Learning algorithms and Machine-learned models or model training and predicting from trained models1. A deeper understanding of the discipline with the proper historical perspective will remain elusive in the majority of the investment community that is on the look out for quick blockbuster hits.

Legacy company executives that opt for getting expensive help from consulting companies in forming their top-down analytics strategy and/or making complex “Big Data” technology components work together before doing their homework on low hanging predictive use cases will find that actionable insights and game-changing ROI will be hard to show.

This is partially due to the requirement to have the right data architecture and flexible computing infrastructure already in place, but more importantly outperforming 36 years of collective achievements by the Machine Learning community with some novel approach is just a tall order regardless how relatively cheap computing has become.

In its current form, think of it as the Polo of Machine Learning techniques, a fun time perhaps that will let you rub elbows with the rich and famous provided that you can afford a well-trained horse, the equestrian services and upkeep, the equipment and a pricey club membership to go along with those.

Early examples of smart applications will emerge in certain industry pockets adding to the uneven distribution of capabilities due to differences in regulatory frameworks, innovation management approaches, competitive pressures, end customer sophistication and demand for higher quality experiences as well as conflicting economic incentives in some value chains.

Teams of doers not afraid to get their hands dirty with unruly yet promising corporate data will completely bypass the “Big Data” noise and carefully pick low hanging predictive problems that they can solve with well proven algorithms in the cloud with smaller sampled datasets that have a favorable signal to noise ratio.

No longer bound by data access issues, complex, hard to deploy tools these practitioners not only start improving their core operations but also start thinking about predictive use cases with a higher risk-reward profiles that can serve as the enablers of brand new revenue streams.

2017 will be the year, when developers start carrying the Machine Learning banner easing the talent bottleneck for thousands of businesses that cannot compete with the Googles of the world in attracting top research scientists with over a decade of experience in AI/Machine Learning, which doesn’t automagically translate to smart business applications that deliver business value.

The developers will start rapidly building and scaling such applications on MLaaS platforms that abstract painful details (e.g., cluster configuration and administration, job queuing, monitoring and distribution etc.)  that are better kept underground in the plumbing.

10 Enterprise Machine Learning Predictions for2018

With our 2018 Machine Learning predictions, we’re taking another shot at Machine Learning clairvoyance with some brand new calls while also upping the ante to serious “double dog dare you”

As such, we’re less concerned with predicting the twists and turns in the heady world of Machine Learning research and more concerned with the experience of the typical enterprise when looking to leverage the technology to reach its quarterly, annual or longer-term strategic business goals.

MGI’s report also reveals that the lion’s share of VC, Private Equity, and M&A activity has gone towards core Machine Learning technologies ($7b) with computer vision ($3.5b) a distant second and other niche AI areas like natural language ($0.9b), autonomous vehicles ($0.5b), smart robotics ($0.5b), and virtual agents ($0.2b) taking in much more modest sums.

Having realized a big part of failed ML/AI initiatives have to do with expensive data lakes of dubious value-add, CIOs and Chief Data Officers will pull the plug and instead accelerate data engineering efforts to create feature engineering repositories to support high-value predictive use cases.

Fortune Global 2000 technologists will realize neither expensive consultants nor bringing in top academic talent will be a replacement for subject matter expertise in the form of a detailed understanding of both the business context and the value chain dynamics in their industries.

The advantageous cost structure of such platforms as compared to expensive consultancy and custom applications combined with their right level of abstraction (i.e., ML building blocks and primitives at the right level atomization to achieve the Lego effect) will lend these platforms well for developers and ML engineers to design and deploy point applications at scale and much faster.

Developers will have a wealth of tools to leverage and yet little in the way of meaningful benchmarks, which will create some confusion and interoperability issues causing tensions with ML-specialists if and when they are available in the organization.  There is no winner in this argument as the much-required learning process continues.

The trend will be further accelerated by the availability of a growing number of specialized toolkits and SDKs optimized for vertical solutions (e.g., IoT meets ML with lightweight local predictions favoring simpler models e.g., anomaly detection, reinforcement learning) that will appear to get developers closer to an end-to-end smart app deployment experience with less and less handholding.

as they are simply cloud versions of the exact desktop or on-prem artifacts they are accustomed to.  Unfortunately, this myopic stance will fail to usher in the era of truly collaborative and inclusive enterprise Machine Learning due to its inherent complexity.

Depending on the type of data you work with and the specific predictive use case, Deepnets may be the only game in town or an unnecessary and costly roundabout.  At BigML, we are of the opinion that Deepnet models should be part of the Machine Learning arsenal, thus the support for it in the platform.  Nevertheless, in 2018, the undeniable hype about DL research will likely do enterprise early adopters a disservice by pulling their attention away from more efficient and cost-effective baseline models and causing them to pour resources into specialized hardware and complex and/or unproven neural network architectures that are hard to operationalize and difficult to maintain even if access to rare DL expert is secured.

But we predict that a more diverse group of Machine Learning technology and service providers that have been long overshadowed by the big tech nerve centers such as Silicon Valley, NYC, Boston and the Chinese megapolises will heat up the global competition against the likes of IBM and Accenture with more straightforward approaches able to deliver ROI quicker in their respective geographies.  This, in turn, will raise the global Machine Learning awakening for all types of organizations in Asia, Europe, and Latin America.  A resulting effect will be the ability to slow down and partially turn the brain drain tide to tech nerve centers that are suffering from affordability crises of their own.

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