Friday, 27 July 2018

Mastering Machine Learning

Mastering Machine Learning

Artificial intelligence (AI) and machine learning are transforming the global economy, and companies that are quick to adopt these technologies will take $1.2 trillion from those who don’t. Businesses that fail to take advantage of predictive analytics, or don’t have the time or resources – like highly-trained (and expensive) data scientists – will fall behind organizations that embrace AI and machine learning to extract business value from their data.
Enter automated machine learning, a new class of solutions for accelerating and optimizing the predictive analytics process. Incorporating the experience and expertise of top data scientists, automated machine learning automates many of the complex and repetitive tasks required in traditional data science, while providing guardrails to ensure critical steps are not missed. The bottom line: data scientists are more productive and business analysts and other domain experts are transformed into “citizen data scientists” that have the ability to create AI solutions.
As more so-called “automated machine learning” tools are brought to market, often with limited feature sets, there is a need to define the requirements for a true automated machine learning platform. This highlights the 10 capabilities that must be addressed to be considered a complete automated machine learning solution.
1. Preprocessing of Data
Each machine learning algorithm works differently, and has different data requirements. For example, some algorithms need numeric features to be normalized, and some require text processing that splits the text into words and phrases, which can be very complicated for languages like Japanese. Users should expect their automated machine learning platform to know how to best prepare data for every algorithm and following best practices for data partitioning.
2. Feature Engineering
Feature engineering is the process of altering the data to help machine learning algorithms work better, which is often time-consuming and can be expensive. While some feature engineering requires domain knowledge of the data and business rules, most feature engineering is generic. A true automated machine learning platform will engineer new features from existing numeric, categorical, and text features. The system should understand which algorithms benefit from extra feature engineering and which don’t, and only generate features that make sense given the data characteristics.
3. Diverse Algorithms
Every dataset contains unique information that reflects the individual events and characteristics of a business. Due to the variety of situations and conditions represented in the data, one algorithm cannot successfully solve every possible business problem or dataset. Automated machine learning platforms need access to a diverse repository of algorithms to test against the data in order to find the right algorithm to solve the challenge at hand. And, the platform should be updated continually with the most promising new machine learning algorithms, including those from the open source community.
4. Algorithm Selection
Having access to hundreds of algorithms is great, but many organizations don’t have the time to try every algorithm on their data. And some algorithms aren’t suited to their data or data sizes, while others are extremely unlikely to work well on their data altogether. An automated machine learning platform should know which algorithms are right for a business’ data and test the data on only the appropriate algorithms to achieve results faster.
5. Training and Tuning
It’s standard for machine learning software to train an algorithm on the data, but often there is still some hyperparameter tuning required to optimize the algorithm’s performance. In addition, it’s important to understand which features to leave in or out, and which feature selections work best for different models. An effective automated machine learning platform employs smart hyperparameter tuning for each individual model, as well as automatic feature selection, to improve both the speed and accuracy of a model.

6. Ensembling
Teams of algorithms are called “ensembles” or “blenders,” with each algorithm’s strengths balancing out the weaknesses of another. Ensemble models typically outperform individual algorithms because of their diversity. An automated machine learning platform should find the optimal algorithms to blend, include a diverse range of algorithms, and tune the weighting of the algorithms within each blender.
7. Head-to-Head Model Competitions
It’s difficult to know ahead of time which algorithm will perform best in a particular modeling challenge, so it’s necessary to compare the accuracy and speed of different algorithms on the data, regardless of the programming language or machine learning library the algorithms come from. A true automated machine learning platform must build and train dozens of algorithms, comparing the accuracy, speed, and individual predictions of each algorithm and then ranking the algorithms based on the needs of the business.
8. Human-Friendly Insights
Machine learning and AI have made massive strides in predictive power, but often at the price of complexity and interpretability. It’s not enough for a model to score well on accuracy and speed – users must trust the answers. And in some industries, and even some geographies (see the EU’s  GDPR), models must comply with regulations and be validated by a compliance team. Automated machine learning should describe model performance in a human-interpretable manner and provide easy-to-understand reasons for individual predictions to help an organization achieve compliance.
9. Easy Deployment
An analytics team can build an impressive predictive model, but it is of little use if the model is too complex for the IT team to reproduce, or if the business lacks the infrastructure to deploy the model to production. Easy, flexible deployment options are a hallmark of a workable automated machine learning solution, including APIs, exportable scoring code, and on-demand predictions that don’t require the intervention of the IT team.
10. Model Monitoring and Management
Even the best models can go “stale” over time as conditions change or new sources of data become available. An ideal automated machine learning solution makes it easy to run a new model competition on the latest data, helping to determine if that model is still the best, or if there is a need to update the model. And as models change, the system should also be able to quickly update the documentation on the model to comply with regulatory requirements.
Businesses that turn to automated machine learning encompassing these features will save time, increase accuracy, and reduce compliance risk when building out their machine learning models – helping them become a truly AI-driven enterprise.
Stay tuned for a brand new slate of Sessions at Computer Science Meet 2018, August 30-31, 2018, Dubai, UAE

Name: Claire Deschamps | Email:

Friday, 13 July 2018

BIG DATA is the reflection of the changing World

Big data is the reflection of the changing world. Each and every tiny change around us is captured & recorded as data. Big data is often used in describing large amounts of data. Not just a specific amount of data, but a dataset that cannot be stored or processed using traditional database software. Most of the data collected are basically unstructured and it requires storage and processing than that found in traditional relational databases, which are available. Computational power is sky-rocketing, meaning there are more opportunities to process big data, and the Internet has democratized data, steadily increasing the data available while also producing more and rawer data.

Change is considered as the biggest norm for the healthcare sector. Digitalization of health & patient data is facing a drastic and dramatic fundamental shift in the clinical, business & operating models for the future days. A pointed growth is being observed for this shift due to lifestyle changes, the aging population, daily emerge of mobile devices; software applications; innovative treatments, focused value and quality in treatments, evidence-based medicines etc. leading to bigger opportunities for improved healthcare delivery, disease surveillance,  optimized treatment for diseases that affect organ systems. Big Data analytics benefits the society as it carries big promises & has the potential to transform healthcare in reality. Since the privacy & data security is increasing day by day, big data is more concerned about protecting the privacy & data security.  In order to prevent the breakthrough of sensitive information, big data should be effectively utilized.

Stay tuned for a brand new slate of Sessions at Computer Science Meet 2018, August 30-31, 2018, Dubai, UAE

Name: Claire Deschamps | Email:

Thursday, 5 July 2018


Big Data Market will be worth US$46.34-billion by end of 2018. This clearly indicates that big data is in a constant phase of growth and evolution. IDC estimates that the global revenue from big data will reach US$203 billion by 2020 and there will be close to 440,000 big data related job roles in the US alone with only 300,000 skilled professionals to fill them. Bidding adieu to 2017 and just in the third month of 2018, we look at the marked differences in the big data space what exciting may be on the horizon for big data in 2018. Tracking big data trends is just similar to monitor the regular shifts in the wind- the moment you sense its direction, it changes. Yet the following big data trends are likely to shape up in 2018.
The expansion of the Internet of Things (IoT) has added innumerable new sources of Big Data into the Data Management landscape and will be one of the major Big Data Trends in 2018 and beyond. Laptops, smartphones, sensors on machines, all generate huge amounts of data for the IoT.

Organizations that are flexible enough to manage and transform the data into useful Business Intelligence, this represents a significant opportunity to gain a competitive advantage (or remain competitive). As Big Data grows, businesses attempt to keep up with it and struggle to turn the data into usable insights. Business Intelligence is key to stay competitive, and Data Analytics provides the up-to-date information needed.

In 2017, some companies expanded their services and software which translated Big Data into visualizations and graphs. This allowed researchers to gather and coordinate information about the general population more efficiently, and improve the customer experience. It also allows leaders to streamline the decision-making process.

The number of companies offering Cloud services will also continue to expand in 2018, resulting in competitive pricing, and allowing smaller businesses to access Big Data resources.

For More Info:

Contact Name: Claire Deschamps / Email:

Friday, 29 June 2018

Business Intelligence
Business intelligence is a technology-driven process for analysing data and preparing actionable information to make data-driven decisions. Business intelligence comprises strategies and technology used by the enterprises for data analysis of business information. Business intelligence technologies can handle large amounts of structured and unstructured data to identify, develop and create new strategic business opportunities. They aim to allow for the easy interpretation of these big data to identify new opportunities and implementing an effective strategy based on insights that can provide businesses with a competitive market advantage and long-term stability.
Types of Business Intelligence:
Strategic Business Intelligence also known as auto-delivered intelligence is often associated with reporting from an analytical data source or data warehouse. Basically, strategic business intelligence improves a business process by analysing a predetermined set of data relevant to that process and provides historical context of data.  In addition, strategic intelligence provides the base for forecasting, goal-setting, planning and direction. Strategic business intelligence needs to be delivered in an interactive manner, enabling the manager to present his views on data in different ways. Also, strategic business intelligence emphasizes on its output on a graphical display such as charts and graphs to represent trends, opportunities and problem areas. Strategic business intelligence converges on four important parameters:
• Collection and storage of data
• Optimisation of data for analysis
• Identification of crucial business drivers through past data records
• Seeking answers to key business questions
Operational Business Intelligence
Operational business intelligence is associated with the transactional or operational data source and is consistent with reporting data during organizational processes. In general, operational business intelligence provides time-sensitive, relevant information to operations managers, business professionals, and front-line, customer-facing employees to support daily work processes. Also if the data retrieved from the analysis directly supports or helps complete operational tasks, then the intelligence is operational in nature. But operational business intelligence demands recipients time as possible which iron out the information presented in an interactive manner. Since operational business intelligence is task oriented there is less need of charts and graphs. Consider an example informing a staff member in an organization regarding information on client’s credit or on over dues. In such a scenario graphical representation won’t hold good but a brief message will solve the problem. Hence communication methods and devices play a vital role in operational business intelligence. Thus, operational business intelligence comprises multiple delivery methods like instant message, email, dashboard and Twitter. The output from operational business intelligence includes invoices, schedules, shipping documents, receipts and financial statements.
There is a difference in the kind of information processed in operational and strategic business intelligence according to the target customers. Also, there is quite a difference in the way the information is being delivered in accord with their requirement in both operational and strategic business intelligence. Albeit these differences strategic and operational business intelligence is more in demand in the IT world.
For More Info:

Contact Name: Claire Deschamps / Email:

Friday, 22 June 2018

A Closer Look at Three Popular Artificial Intelligence Technologies and How They’re Used
From robotic process automation to machine learning algorithms, many of today’s most influential companies are deploying artificial intelligence (AI) technologies to drive business results. While most decision-makers are aware of the business opportunities that emerging technologies present, many are unprepared simply because they fail to understand them.
AI includes a variety of technologies and tools, some that have been around for a long time and others that are relatively new. Nevertheless, one thing is clear: businesses are thinking harder about how to prioritize AI in 2018.
According to International Data Corporation (IDC), the widespread adoption of artificial intelligence will jump from $8.0 billion in 2016 to more than $47 billion in 2020. Here’s a closer look at three popular AI technologies and how innovative companies are using them.
When companies talk about using AI technologies, most are referring to machine learning (ML). The most popular branch of AI computing, ML involves training algorithms to perform tasks by learning from historical data rather than human commands. In other words, computers learn without explicit programming. Small start-ups and major brands use ML to access, organize, and make decisions off data in a more efficient and results-driven way.
At SAP, machine learning is an essential component of a content marketing strategy. The enterprise software company uses ML to analyse content to provide a more tailored experience for their customers. ML algorithms map published articles by themes, helping SAP personalize customer engagement through content.
The goal is to help the audience find more relevant articles based on their unique behaviours and search histories. For SAP, ML-powered technology allows them to go beyond standard recommendation engines with insights that inform targeting and content that engages the right customers with the right creative experience at the right time.
Computer vision is a branch of AI that deals with how computers imitate human sight and the human ability to view and interpret digital images. Through pattern recognition and image processing, computer vision understands the contents of pictures, and it’s having a profound impact on how we experience the world around us.
Amazon uses computer vision technology to improve brick-and-mortar shopping for customers through its Amazon Go experience. With no lines and no checkouts, customers simply use the Amazon Go app to enter the store, choose the items they want, and leave. How? Cameras snap pictures of customers as they shop. Using machine vision, deep learning, and sensor fusion, Amazon keeps track of items in a virtual cart, appropriately charging the right Amazon account.
Vision-guided retail is only the beginning, as computer vision will also likely open doors for smart cities where advanced vision technologies may be able to help reduce the number of collisions and injuries on the road.
AI-driven software, like robotic process automation (RPA), has become a competitive advantage for companies around the world. Digital technologies like RPA improve efficiencies, reduce mistakes, and even disrupt the way companies’ craft customer experiences.
South Africa’s largest bank, Standard Bank, digitized legacy processes through RPA, ML, and cognitive automation, increasing efficiencies in operational, back-office work. As a result, they’ve reduced customer onboarding time from 20 days to 5 minutes!
RPA software gave Standard Bank the flexibility and capability to deal with the challenges of financial services while staying current with other industries. RPA technology reduced mistakes and turned mundane work into something interesting, all while delivering a richer experience for their customers.

For More Info:
Contact Name: Claire Deschamps / Email:

Friday, 8 June 2018

Machine Learning: What it is and Why it matters

Computer Science Meet 2018Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Another recent development was that MIT researchers were working on object recognition through flexible machine learning.
Machine learning is starting to reshape how we live, and it’s time we understood what it is, and why it matters.

What is Machine Learning?
Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves.
Machine learning is a method of data analysis that automates analytical model building.” In other words, it allows computers to find insightful information without being programmed where to look for a particular piece of information; instead, it does this by using algorithms that iterative learn from data.

Why Machine Learning?
To better understand the uses of machine learning, consider some instances where machine learning is applied: the self-driving Google car, cyber fraud detection, online recommendation engines-like friend suggestions on Facebook, Netflix showcasing the movies and shows you might like, and “more items to consider” and “get yourself a little something” on Amazon-are all examples of applied machine learning.
All these examples echo the vital role machine learning has begun to take in today’s data-rich world. Machines can aid in filtering useful pieces of information that help in major advancements, and we are already seeing how this technology is being implemented in a wide variety of industries.

Some Machine Learning Algorithms and Processes:
Computer Science Meet 2018
Other tools and processes that pair up with the best algorithms to aid in deriving the most value from big data include:
• Comprehensive data quality and management
GUI for building models and process flows
• Interactive data exploration and visualization of model results
• Comparisons of different machine learning models to quickly identify the best one
• Automated ensemble model evaluation to identify the best performers
• Easy model deployment so you can get repeatable, reliable results quickly
• Integrated end-to-end platform for the automation of the data-to-decision process

Whether you realize it or not, machine learning is one of the most important technology trends-it underlies so many things we use today without even thinking about them. Speech recognition, Amazon and Netflix recommendations, fraud detection, and financial trading are just a few examples of machine learning commonly in use in today’s data-driven world.

For More Information:
Contact: Claire Deschamps / Email:

Thursday, 24 May 2018

Artificial Intelligence in 2018

Artificial Intelligence research is currently focused on developing algorithms which allow humans and technology to communicate more naturally with each other, and ways to train those algorithms. The goal is to answer complicated questions in natural human language. AI and ML have made it possible to automate jobs normally requiring human discretion. These jobs include such skills as:
·         reading handwritten materials
·         identifying faces
·         learning
·         cognitive skills, such as planning, and reasoning using partial information
“AI techniques are evolving rapidly and organizations will need to invest significantly in skills, processes and tools to successfully exploit these techniques and build AI-enhanced systems. Investment areas can include data preparation, integration, algorithm and training methodology selection, and model creation. Multiple constituencies, including data scientists, developers, and business process owners will need to work together.
  • The Gluon Endeavour

Amazon uses Artificial Intelligence. Amazon’s recommendation engine uses AI to predict a customer’s interests, with roughly 5-10 percentage accuracy. In an effort to improve prediction accuracy, Amazon has joined forces with Microsoft in offering a novel endeavour to use Machine Learning in teaching AI entities. The new platform, called Gluon, grants access to AI developers of all skill levels. The Gluon platform is described as making it easier for AI developers to design and develop neural networks.

  • AI and Cyber Security
“Ironically, our best hope to defend against AI-enabled hacking is by using AI. AI can be used to defend and to attack cyber infrastructure, as well as to increase the attack surface that hackers can target, that is, the number of ways for hackers to get into a system. Business leaders are advised to familiarize themselves with the cutting edge of AI safety and security research.”
As businesses realize the importance of developing a cyber-security program, AI will become more popular. A well-constructed AI defence system can process years of attack history and learn various attack and defence strategies. It can create a baseline of normal user behaviour, and then search for anomalies, much faster than a human. This is significantly less expensive than maintaining a team of security professionals to deal with daily cyber-attacks. AI can also be used to develop defence strategies. Expect AI to become more heavily involved with Cyber Security in 2018 as well.

Contact: Claire Deschamps