Advances have been made in applications of self-driving vehicles, automated drones, and embedded sensors. Uses of data are requiring more efficiency from existing infrastructure and challenging the industry to evolve infrastructure for the future. As the industrial internet embeds sensors across a range of products and equipment, companies have been expanding opportunities to react to, service interruptions quickly and access data to develop long-term strategic improvement. You can read more at : https://www.oreilly.com/ideas/the-coming-tipping-point-in-data-driven-logistics

Scaling databases for enterprise require to have to integrate wildly disparate data sources, satisfy stakeholders with competing expectations, and find the structure hidden in unstructured data.One has to carefully consider tradeoffs between data integrity and constant uptime, between.You may have a legacy system that stores data in tab-delimited files, unstructured text files coming from handwritten notes, and one or more conventional database management system and data from all of these sources needs to be read by and integrated into a single system.Read full article at : https://www.oreilly.com/ideas/insights-on-scaling-and-integrating-databases

Hadoop, Pig and Hive, HBase and other NoSQL point solutions onto Spark, Flink, Drill, and Kafka were built to handle individual aspects of the three V’s of big data (volume, variety, and velocity).If a storage system can scale linearly, then we can put the applications on top of the storage platform. If the application runs where the data is stored, then we don't have to worry about moving the data later to perform analytics.Model of messaging delivered via Kafka and MapR Streams can achieve rates about one million events per second with a minor investment. These technologies take a little time to understand and get comfortable with, but may be worth the investment.You can read more at : https://www.oreilly.com/ideas/using-microservices-to-evolve-beyond-the-data-lake

Adoption of Internet of Things rose dramatically in the year 2016. Factors like the increased numbers of sensors and connected devices, a growing pool of IoT developers, and real-time data and analytics support for IoT systems are a few of the major reasons for its expansion. "The Internet of Things Market", by Aman Naimat, presents a snapshot of IoT culture. It  describes a data-driven analysis of the companies, industries, and workers using IoT technologies. As the volume of data sets grow and more robust computation power evolves and scalability will lead to more IoT breakthroughs which in turn will lead to more  business investment in the future. You can read more at : https://www.oreilly.com/ideas/all-grown-up-the-iot-market-today

Data matching is the task of identifying, matching, and merging records that correspond to the same entities from several source systems. These entities may be people, places, publications or citations, consumer products, or businesses. The major hurdle that encounters while solving this problem is lack of common entity identifiers, easily available information like name, address, etc. that may change over time is usually of low quality and produce poor results with high error rate. Technological advancements in the last decade have made it possible to scale data, matching on large systems that contains millions of records and improved accuracy. You can read more at : http://www.datasciencecentral.com/profiles/blogs/data-matching-entity-identification-resolution-linkage

A very little time is spent on thinking about how to deploy a data science model into production. As a result, many companies fail to earn the value that comes from their efforts and investments. In production environment data continuously comes, result are computed and models are frequently trained. The challenges faced by companies fall into four categories:  Small Data Teams: They mostly use small data, often don’t retrain models and business team is involved in a development project. 

Packagers: Often build their framework from scratch and practice informal A/B testing , generally not involved with the business team

Industrialization Maniacs: These teams are IT led and automated process for deployment and maintenance , business team are not involved in monitoring and development

The Big Data Lab : Uses more complex technologies , business teams are involved before and after deployment of data product

Companies should understand that working in production is different than working with SQL databases in development , moreover real time learning and multi-language environments will make your process complex. Also a strong collaboration between business and IT teams will increase your efficiency. Read more at : http://dataconomy.com/2017/02/value-from-data-science-production/

Data science platform has become a buzzword of the decade. So, what is it? The sole purpose of a data science platform is to encapsulate all off-data science work by incorporating tools required to visualize, deploy, collect, analyze data, build models, generate reports. This toolkit makes it convenient to maintain, reproduce and scale up the project and produce results dynamically. Adoption of data science platforms is expected to grow almost double by 2018 as more companies realize its potential benefits. Many data driven business faces the challenge of effectively utilizing data science tools and lack integrated approach to their data science technology stack to find value in the data. While on the other hand, companies who have already established data science platforms are excelling in the field.

Read more at : http://dataconomy.com/2017/02/tech-wave-data-science-platforms/

The three critical steps involved in deployment of machine learning algorithm and exposing it to real world are :

Define a goal based on a metric : Decide if you want human level intelligence or an acceptable one as this decision will affect time and engineering cost of your system. Also define a metric to measure performance of your model.

Build the system : Build a minimum viable system without worrying much about accuracy. Then build an incremental strategy to improve your system by solving problems you face in each iteration.

Refine the system with more data : Initial metric values are not the indicators of real life, your data and users might change , so regularly monitor the system performance. Update it with new data and fine tune the model accordingly.

Read more at : http://www.erogol.com/short-guide-deploy-machine-learning/

Sometimes even the Machine learning algorithms behave so dumb that an image recognition model can be confused by generating an adversarial instance, i.e. by changing few pixels by either taking derivative of model output or exploiting genetic algorithms. Adversarial instances lie in low probability regions which is in contrast with limited instances of high probability regions from which the model was trained. A possible approach to solve this problem is ensemble training - To let multiple models back each other. As we look forward to developing more artificial intelligent systems it would become common to encounter such problems.

You can read more at: http://www.erogol.com/ensembling-against-adversarial-instances/

The emergence of massive unstructured data sources like Facebook and Twitter has created a need to develop distributed processing systems for Big Data Analytics. Hadoop (A Java based programming framework) has become the first choice of developers and industry experts mainly because its: Highly scalable, flexible, and cheap. An application is broken down into various small parts which runs on thousands of nodes to achieve fast computing speed and reduce overall operation time. Hadoop architecture continues to operate even if a node fails. Its incredible design allows you to process large volumes of data and extract computationally difficult features of users/customers.

Read more at : http://www.datasciencecentral.com/forum/topics/how-to-use-hadoop-for-data-science

Often the outputs of data models developed by data, scientists end up in a report which summarizes the state of business and used by stakeholders to make decisions. But it is necessary to achieve a system that can predict the future outcomes in real time. This can be done by integrating the model in a production environment, however, it requires advance engineering skills and data scientists cannot do it alone. The process of deployment follows broadly 7 steps :  1.Refactor the model code

2. Walk through the code and determine how it slots into the engineering cycle

3.Re-write into a production stack language or PMML

4.Implement it into the tech stack

5. Test performance

6. Tweak the model based on test results

7.Slowly roll out the model.

Today many companies are adopting tools to make this process faster to reap the benefit of data driven decision making.

Read more at : https://www.datascience.com/blog/navigating-the-pitfalls-of-model-deployment

E-Book business communities generate a lot of revenue everyday but sometimes it is difficult for author(s) to earn decent amount because of lack of preparation and research. No matter how unique and interesting your content is, if it doesn't appear on the first or second page of search results, it's highly unlikely that a visitor would ever read it. The story doesn't end here, one must cleverly select the title and cover which attract the reader as it changes the way we think. A neural network approach for the determination of most titles using Doc2Vec can be adopted to increase revenue. It involves training a thin two-layer neural network, which operates in unsupervised mode and form clusters of most similar words (using cosine similarity metric) based on context.

To read more about the technical implications here: http://www.datasciencecentral.com/profiles/blogs/use-neural-networks-to-find-the-best-words-to-title-your-ebook

We all have access to the internet these days! Either we want to shop something, visit some place or some other stuff we want to do. We give a surf over the internet. How smart we are.

What if we tell you, your pet, car, lights or any other daily life object you interact with can be smarter? In this era of the internet and computing just being smart isn’t enough, we have to make things smart we should deal them in a smarter way.

Internet of things aka IoT is the technology which deals with the ever-growing network of objects or living being or any other thing that feature an IP Address for internet connectivity and the communication that occurs between these objects and other internet enabled devices/systems.

Sigmaway has designed IoT Sigma Workshop which gives you an introduction towards how to make smart devices and how to let anything be on internet in a very friendly and easy way.

Get Trained in IoT……Build the future.

Contents: IoT Introduction; Raspberry Pi Session; Web Technologies Intro, Live Projects; Online access to key reference material.

Our Key Differentiator: Post training access to online discussion forums and periodic Webinars

Eligibility: No pre-requisites. Students need ID proof to avail discount

Certification: IoT Sigma Certification - On 100% Attendance of Classroom Session and completing task given.

Certification means that he/she is competent in basic Concepts of IoT techniques and tools necessary for development of an IoT Platform. (If you complete our training and then do a live project, we also provide IoT Certification, on review of project report and Affidavit from your organization).

Register at: http://www.gosigmaway.com/events/robotics-iot/iot/29-iot-sigma-workshop-delhi  or email: trainings@gosigmaway.com

Date: 27-28 December, DELHI, India

Address: Unit - 603, DDA Building, District Centre, Plot No. 4, Laxmi Nagar, New Delhi - 110092

 Investment for Students: Rs. 1,500 + 15% taxes

Investment for Professionals: Rs. 5,000 + 15% taxes

Early bird discounts also available! And Progressive Discounts for Groups (conditions apply)

Every entrepreneur wants to expand their business and while doing so, one need capital, smart strategy, passion and more importantly to invest in technology. But, one need to invest in the right tech strategies to be successful. This article focuses on the right tech strategies that business owners should concentrate on. These are: Focus on Mobile, Leverage on Social Media, Become a Third-Party Seller, and Harness the Power of Big-Data. Read more at: http://it.toolbox.com/blogs/understanding-crm/5-tech-strategies-for-commercial-business-expansion-75037

Nowadays, companies are using the Internet of Things (IoT) as a source of data. But, most companies are not using IoT data for their sales and marketing departments. We need to know the reason of this discrepancy. The gap is generally technical, and two different technical groups need to work together to tie the IoT to customer relationship management systems. This article explores the ways IoT data streams will improve the quality and impact of CRM data. They are : Usage rate, preventive maintenance and cross marketing. Read more at: http://it.toolbox.com/blogs/insidecrm/3-iot-data-streams-that-improve-the-quality-of-your-crm-data-74960

In today’s business world, due to tough competition, some organizations find it difficult to control ERP budgets both at the implementation stage and well beyond into the full life cycle of the application. But, organizations need to find some ways to gain control of ERP costs. ERP implementation managers should identify and plan to reduce the chance of overrunning the budget from the beginning of the implementation stage. Read more at: http://it.toolbox.com/blogs/inside-erp/6-ways-to-gain-control-of-erp-costs-74917

Artificial Intelligence or AI is gaining importance in today’s business world. But, we must know what is AI in ERP before proceeding. It is a powerful way to efficiently and accurately extend the capabilities of ERP. One of the most important functions of ERP is to help organizations streamline their activities and take decisions about everything from production to sales. AI extends these abilities by analyzing large historical data sets and organizations can then use these historical data sets to learn past patterns of behavior. Read more at: http://it.toolbox.com/blogs/inside-erp/3-ways-ai-improves-erp-74922

Several times, I come across students, tech enthusiasts, learners, teachers as well as business owners while going through an IoT workshop/seminars. They find it mesmerizing and amazing but they always ask me is there any venture or organization that is working on this technology? Yes, there are many companies that are working in this technology and doing a great job on it. So, here is the least of some global IoT leaders:

Amazon Web Services:

Since, cloud has a very huge role and contribution in IoT, Amazon Web Services is the biggest cloud provider on which you can count on. As we know that cloud is used to store the huge amount of data, AWS offers powerful services to gain insights of that data.

AT&T:

When it comes to the connectivity, AT&T is the one we have in mind. AT&T also is a key enabler of IoT banking on their Broadband network. This company have their own cloud based IoT development portal named as M2X. 

CISCO:

No one can deny that when it comes to the networking devices, CISCO is a global leader in them. CISCO has developed a range of IoT devices. Starting from switches, routers, fog computing services, up to the data management, security services, analysis and automation.

Google:

Massive Search Engine, Self-Driving cars, Cloud functions, and the developer of many more technologies out there. Google have been through a long span of development and already leading the IoT market in form of Google Cloud. Google is in great position to serve IoT consumer applications.

IBM:

IBM is making a big push in the IoT market in form of "Watson", its IoT cloud platform for the development of IoT devices, smart products and much more. IBM is hoping to initiate its cognitive technologies in IoT devices. 

There are many other market leaders like as Microsoft, Intel, Oracle, Samsung, Siemens, Qualcomm, Mediatek, and many more which are also developing for the IoT and giving up a great push to the technology and smart devices these days, so there is a biggest wave of opportunities is on-going in this era of technology for developers, investors, consumers too. "World is digitizing, Are you Ready for it?".

In recent times, marketing has become an integral part of any business. Your business may offer the best products or services in the industry, but without continuous projection of the product to the customers, the chances of your competitors taking over your products is very high.

In the early 1950s and 1960s, marketing was production oriented and the quality of the production was the driving factor of marketing. Later, as new production technologies started to develop, techniques evolved simultaneously to meet the needs of the customers and efforts were made to maximize customization. But the next major advancement in marketing is literally hacking the brain of the customer.

Neuroscience is the field of study where the response to products and consumer decision-making is understood at the level of body and mind. The Neuromarketing concept is based on a model wherein the major thinking part of human activity, including emotion, takes place in the subconscious area that is below the levels of controlled awareness. For this reason, the perception technologists of the market are very tempted to learn the techniques of effective manipulation of the subconscious brain activity.

Neuromarketing is a flexible method to determine customer preferences and brand loyalty, because it can apply to anyone who has developed an opinion about a product or company.

To know more on neuromarketing, read : http://blog.fractalanalytics.com/integrated-marketing-effectiveness/neuroscience-in-marketing/

If you think that the internet is the best invention of all the time, then watch your words, there is more that will amaze you more than internet ever could be, presenting - Internet of Things.

 Internet of Things (IoT) is an arrangement of interrelated registered devices, i.e. they are more advanced machines, objects, that is equipped with interesting identifiers and have the capacity to exchange information over a system without requiring human-to-human alternately human-to-computer communication.

It is basically the network of physical devices, vehicles, buildings and other items—embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data.

The IoT permits things to be sensed furthermore and remotely over existing organizational infrastructure, making chances to a greater amount to regulate the mix of the physical universe under computer-based systems, and bringing about enhanced efficiency, exactness and more investment benefit. When IoT is combined with sensors and actuators then these devices become a system to generate and analyse the life of a human and their interaction with different object that grows up the technology-human relations, which includes innovations for example, advanced mobile grids, advanced mobile homes, smart transportation smart cities. Every relic is particularly recognizable proof through its inserted registering framework anyway can inter-operate inside the existing web foundation.

Some examples of IoT based platforms are:

1. Google Home: Speaker with Google Assistant

If you loved Google Assistant in your android devices or Chromebooks, then this device is for you. It is a home Speaker system that works with the google assistant embedded in it.

2. CUJO: Smart Firewall for the Smart Home

         Is Home Security is your concern??? Then Cujo is the answer for you. It analyses the local network traffic data in real time and sends statistics on that data to cloud for further analysis.