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Amit Gandhi

serverless-cloud-computing

Serverless Cloud Computing: current trends, implementation and architectures

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It seems like everyone’s talking about serverless computing these days. But wait…when did we all stop using servers? In case you were wondering, servers are still necessary — serverless just means the average cloud user doesn’t have to worry about them anymore. You can now code freely without having to write an endless number of scripts to manage server provisioning.

 

serverless-trends

The interest over time for Serverless Computing

Also called FaaS (Functions as a Service), serverless computing can bring nearly limitless, provision-less computing power to your applications. Here’s how it can make a difference for you.

Introducing Serverless Cloud Computing

Serverless computing, of course, still uses computers—serverless doesn’t mean we’re running applications without using computing resources at all. It does mean that users of the serverless computing service don’t have to provision virtual machines or directly manage the computers. This frees up developers to focus on their application development instead. For development teams, this makes development a lot easier.

For companies offering SaaS (Software as a Service) or running their own computing tasks, it makes it a great deal easier to get the right computing resources and have applications run.

Users taking advantage of serverless computing for their applications find that it has a lot of practical value. In a business setting, serverless computing essentially extends what organizations are able to accomplish with their applications and enables them to provide greater value to their customers.

In fact, serverless computing is valuable for many reasons. For instance:

  • Scalability: Serverless computing makes it much easier to scale computing resources to meet the needs of your application.
  • Access on-demand computing: Computing resources are available immediately, whenever the application needs them or whenever users initiate the system to start. There’s no waiting around for computing time to become available, because it’s already waiting and can be quickly deployed or used on schedule.
  • Unlimited resources: Truthfully, serverless computing resources can seem almost unlimited. Your application can use whatever it needs to run, even if you suddenly have additional demand you didn’t plan for. While there’s no such thing yet as completely unlimited computing resources, serverless computing can get really close.
  • Time-to-market: If you’re a developer, being able to quickly have the right resources you need to get your software ready is a really big deal.
  • Security: Whenever human error is possible, it’s bound to happen eventually that someone will make a mistake. Serverless computing helps to protect against the inevitable. This makes it easier for you to focus on your work instead of preventing every possible security problem.

For these and other reasons, serverless computing is now more popular than ever before. It helps companies achieve their computing needs without having to spend so much time on computing resource management.

Switching from traditional servers to serverless computing can generate really mind-blowing savings, like cutting your monthly costs from $10k to just $370. Wow.

Before and After the Serverless Era

Doing anything without serverless computing can be fairly limiting once you’ve experienced the benefits. Getting here to the point where this technology really became available did take a while.

Yesterday’s Cloud, Today’s Cloud & Tomorrow’s Cloud

Just like A Christmas Carol’s three ghosts, the cloud has three personalities, too—let’s talk about yesterday’s cloud, today’s cloud, and the cloud of the future.

Originally, just the idea of outsourcing your computing to another network was a big deal. That other network’s servers could augment your existing computing resources and enable you to tackle much bigger projects than you could before. This was the very beginning of the cloud. With the Internet in its early days and basic server networks available to help you get a little extra computing help, it had a lot of promise for early software development and operations.

That’s yesterday’s cloud. It had severe limitations, like very limited overall resources. It was about the beginning of SaaS, and data analytics was on the horizon but not quite yet a big If you needed to scale, that might’ve required a discussion with your vendor and some changes onsite at their facilities.

At the end of the day, you were running virtual machines, but you still had to worry about the machines—not their hardware, because someone else was doing the maintenance—but you did have to manage your computing resources closely.

Today, there’s another cloud in town. It’s trying to free us from this close management. Cloud 2.0 has often been described in terms of data. Big data, analytics, and information. With fewer data constraints, companies are free to make the most of data in new ways.

And tomorrow, the cloud’s continued growth will bring us even more possibilities, making data use more practical for a variety of different industry applications.

Serverless Implementation Examples

In recent times, many organizations have successfully transitioned their applications over to serverless computing.

For instance:

  • Trello
  • Soundcloud
  • Spotify

For event-driven applications, the move or a partial move to serverless makes sense. These applications rely a lot on input from users that triggers the need for computing resources. Until specific events are triggered, the applications may need very little at all—but once a function triggers, the computing power needs increase almost asymptotically very rapidly. In many cases it’s tough to scale these applications without readily-accessible and affordable computing power.

Why Should You Move to Serverless?

Serverless is ideal for applications that have a lot of function-driven events, such as events driven by a mouse click. It’s great for systems that rely on user engagement—and require big bursts of computing power at key moments. It would be hard to have on premises infrastructure to meet these needs. It also doesn’t make sense to have to recreate resource management processes and micromanage machine use when you’re creating or operating software that works this way.

From a technical standpoint, it offers benefits such as:

  • Supports all major server side languages/frameworks like Node.js, Python, Java, Scala and Kotlin.
  • Software lifecycle management from a single platform i.e. you can build, deploy, update and delete.
  • Safety function for smooth deployment and resource manager
  • Minimal configuration required
  • Functions optimized for CI/CD workflows
  • Supports automation, optimization and best practices of enterprise computing
  • 100% extensible functions and frameworks

Key Steps in Migrating Existing Structure to Serverless

Making the transition to serverless computing doesn’t have to be too difficult. As long as you start with a viable plan and a willingness to adapt, you shouldn’t have too much trouble.

 

 

Here’s a few steps to get you started. You’ll be setting up an account with a provider and testing it with your own function. From there, you can quickly start tailoring the service to your needs.

Adapt this test process to your own applications and business needs, but choose something simple so you can play around with your new account:

  1. Begin with an application, or an idea.
  2. Create an account with a serverless computing provider, such as AWS Lambda, Google Cloud, or Microsoft Azure.
  3. Prepare to test your new account. To do so, you’ll want to create two buckets. For one of the buckets, you’ll upload photos or another file type you’ll be transforming. The other will receive these files once you’re done.
  4. In your management console, you’ll now create a new function using the two buckets you just set up. Specify how the buckets will be used.
  5. Name your function and set it aside for later.
  6. Create a directory and set up your workspace on your local machine.
  7. Write a Javascript file or other code to use files in your new account(Here’s an example using AWS)
  8. Upload.
  9. Test your function.

Once you’ve tested the process, you can start looking at how existing code (and new, from-scratch code, too) can leverage serverless computing capabilities.

 

Is Serverless the Future of Cloud Computing?

With so many uses and promises for the future, serverless is likely to continue playing a prominent role in the future of cloud computing. It’s not for every application and company, but for event-driven functions you need a little (or a lot) of on-demand computing power for, it makes sense.

Your business may benefit tremendously from making a move to the serverless cloud. Parkar can help your organization make sense of the cloud and how it can help you reach your business goals. Contact us for more information about how we can make a difference.

 

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

Machine-learning-for-enterprises

Introduction to Machine Learning for Enterprises

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Machine Learning, a sub-field of Artificial Intelligence, is playing a key role in a wide range of industry critical applications such as data mining, natural language processing, image recognition, and many other predictive systems.

The goal of Machine Learning is to understand the structure of different data sets and build models that can be understood and utilized by industry systems and people.

It provides automated study and extraction of insights from data by the means of different ML approaches, algorithms, and tools.

In this competitive age, how you use the data to better understand your systems and their behavior will determine the level of success in the market. Machine Learning goes beyond traditional Business Intelligence and accelerates data-driven insights and knowledge acquisition.

Although, it has been around for decades; due to the pervasiveness of data that is being generated and infinite scalability of computing power, it has taken the center stage now.

In this comprehensive guide, we will discuss methods, ML frameworks, predictive models and a wide range of machine learning applications for major industries.

Common approaches to Machine Learning

If you want to predict the traffic of a busy street for a smart city initiative, you can run it through ML algorithms and feed the past traffic data to accurately predict future traffic patterns. Industrial problems are complex in nature which means we must invent new but very specialized algorithms that can solve impractical problems.

Hence, there are different approaches to which ML models can be applied to train a software system.

Supervised Learning

It takes place when a developer provides the learning agent with a precise measure of its error which can be directly compared with specified outputs. In generic terms, supervised learning is ideal when you have what you want the machine to learn.

The algorithm is mostly trained on a pre-defined set of training examples which enable the program to reach an accurate conclusion when given new data.

supervised-learning

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classfication-vs-regression

Unsupervised Learning

In unsupervised learning, data is unlabelled, so the learning algorithm is left to find commonalities among its input data. As unlabelled data are more abundant than labeled data, machine learning methods that facilitate unsupervised learning are particularly valuable.

The goal of unsupervised learning may be as straightforward as discovering hidden patterns within a dataset, but it may also have a goal of feature learning, which allows the computational machine to automatically discover the representations that are needed to classify raw data.

Unsupervised learning is commonly used for transactional data. You may have a large dataset of customers and their purchases, but as a human, you will likely not be able to make sense of what similar attributes can be drawn from customer profiles and their types of purchases. With this data fed into an unsupervised learning algorithm, it may be determined that women of a certain age range who buy unscented soaps are likely to be pregnant, and therefore a marketing campaign related to pregnancy and baby products can be targeted to this audience to increase their number of purchases.

Without being told a “correct” answer, unsupervised learning methods can look at complex data that is more expansive and seemingly unrelated to organize it in potentially meaningful ways. Unsupervised learning is often used for anomaly detection including for fraudulent credit card purchases, and recommender systems that recommend what products to buy next. In unsupervised learning, untagged photos of dogs can be used as input data for the algorithm to find likenesses and classify dog photos together.

Machine Learning frameworks

machine-learning-framework

In this section, we will talk about some of the best frameworks and libraries available for Machine Learning. Each of these Frameworks is different from each other and takes much time to learn. During the time of making this list, we took care of features other than the basic ones: user base and community & support was one of the most important parameters. Some frameworks are more mathematically oriented, and hence geared more towards statistical than neural networks. Some of them provide a rich set of linear algebra tools; some are mainly focused only on deep learning.

 

TensorFlow

Tensor Flow is developed by Google to write and run high-performance numerical computation algorithm. It is an open source ML library for data-based programming which uses data flow graphs. Tensor Flow offers an extensive amount of functions and classes that we can use to build various training models from scratch.

Earlier, we talked about different machine learning methods, Tensor Flow is capable to handle all kinds of regressions, classifications algorithms and neural networks on both CPUs & GPUs.  However, most of the functions are complex so it’s difficult to implement at the early stages.

What makes Tensor Flow the perfect library for enterprises:

  • Based on Python API
  • Truly portable as it can be deployed on one or more CPUs or GPUs and can be served simultaneously on mobile, computer with a single API.
  • It’s flexible enough to run it on Android, Windows, iOS, Linux and even Raspberry Pi.
  • Visualization
  • It has checkpoints to manage all your experiments
  • The community is large to help with any issues.
  • Acceptability across the Industries as tons of innovation projects are using TensorFlow.
  • It lets you handle the derivatives automatically.
  • Performance

Tensor Flow is being used by the top most companies in the world including:

  • Google
  • OpenAI
  • DeepMind
  • Snapchat
  • Uber
  • eBay
  • Dropbox
  • Home61
  • Airbus
  • And Tons of new-age startups

Spark

Spark is an analytics engine based on a cluster-computing framework built for large-scale data processing. The initial development was done at Berkeley’s lab but later was donated to Apache Software Foundation.

With some advanced features, it creates spark label vectors for you thus carrying away much complexity to feed to ML algorithms.

Advantages of Spark ML:

  • Simplicity: Simple APIs familiar to data scientists coming from tools like R and Python
  • Scalability: Ability to run same ML code on small as well as big machines
  • Streamlined end to end
  • Compatibility

CAFFE

Caffe is an open source framework under a BSD license. CAFFE (Convolutional Architecture for Fast Feature Embedding) is a deep learning tool which is mainly written in CPP.

It supports many different types of architectures for deep learning focusing mainly on image classification and segmentation. It also supports Graphic and CPU based acceleration for neural based engines

CAFFE is mainly used in the academic research projects and to design startups Prototypes. Even Yahoo has integrated caffe with Apache Spark to create CaffeOnSpark, another great deep learning framework.

Advantages of Caffe Framework:

  • Caffe is one of the fastest ways to apply deep neural networks to the problem
  • Supports out of box GPU training
  • Well organized Mat lab and python interface
  • Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
  • Speed makes Caffe perfect for research experiments and industry deployment.
  • Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convent implementations available.

 

TORCH

Torch is also a machine learning open source library, a proper scientific computing framework. Its complexity is relatively simple which comes from its scripting language interface from Lua programming language interface. There are just numbers (no int, short or double) in it which are not categorized further like in any other language. So, it eases many operations and functions.

Torch is used by Facebook AI Research Group, IBM, Yandex, and the Idiap Research Institute, it has recently extended its use for Android and iOS.

Advantages of torch framework include:

  • Flexible to use
  • High level of speed and efficiency
  • Availability of tons of pre-trained ML models

SCIKIT-LEARN

Scikit-Learn is a very powerful free to use Python library for ML that is widely used in Building models. It is founded and built on foundations of many other libraries namely SciPy, Numpy, and matplotlib, it is also one of the most efficient tools for statistical modeling techniques.

Advantages of Sci-Kit Learn:

  • Availability of many of the main algorithms
  • Quite efficient for data mining
  • Widely used for complex tasks

 

Business/Operational Challenges while implementing Machine Learning

To better understand how ML may benefit your organization — and to weigh this against the potential costs and downsides of using it — we need to understand the major strengths and challenges of ML when applied to the business domain.

High performance, efficient, and intelligence

ML can deliver valuable business insights more quickly and efficiently than traditional data analysis techniques because there’s no need to program every possible scenario or require a human to be part of the process — taking people out of the process. ML can process higher volumes of data, it also has the potential to perform much more powerful analytics. ML’s intelligence, provided by its ability to learn autonomously, can be used to uncover latent insights.

Pervasive Nature

Due to higher volumes of data collected by increasingly computing devices and software systems, ML can now be applied to a variety of data sources. It can also solve problems under a variety of contexts.

For Instance, it can be used to add unique functionalities to enterprise systems that may otherwise be too difficult to program. We’re already using to solve large-scale process improvement initiatives to support business objectives for many industry-leading organizations. Programs like Six Sigma is already being replaced by many corporations, and they’re leaning towards training ML algorithms to enhance their business process.

Uncover hidden insights

It can handle nonspecific and unexpected situations. When organizations are uncertain about the value or insights inherent in their data — or are confronted with new information they don’t know how to interpret — ML can help discover business value where they may not have been able to before.

With all the benefits and capabilities, there are some challenges that become a roadblock for organizations in adopting ML in their Industry such as:

It requires considerable data and computing power as it applies analytics to such large amounts of data and runs such sophisticated algorithms, it typically requires high levels of computing performance and advanced data management capabilities. Organizations will need to invest in infrastructure to handle it or gain access to it through the on-demand services of external providers, such as big data analytics cloud providers.

It adds complexity to the organization’s data integration strategy. ML feeds off of large amounts of raw data, which often come from various sources. This brings a demand for advanced data integration tools and infrastructure, which must be addressed in a thorough data integration strategy.

 

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

BI-for-Enterprise

A comprehensive guide on smart Business Intelligence for Enterprises

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The best way to understand Business Intelligence, is probably an overview of how it works in action: taking an organization from data-rich but UN-optimized in data usage, to truly data-driven and able to fully reap the benefits of their information and technology.

Place yourself in the shoes of an established organization – hypothetical, of course – from a data-rich industry like healthcare, IT, or retail. By nature of the tools necessary just to conduct your business, you already have massive amounts of raw data on hand, and you probably made a not-insignificant investment to acquire it. This vast store of data might include, as examples – depending on your industry:

For a medical corporation:

  • Patient records
  • Diagnosis reports
  • Patient surveys

For a retail organization:

  • PoS data
  • In-store sensors
  • Footage from security cameras
  • Demand data based on customer footprint

This is where business intelligence at the enterprise level comes in. Previously, ETL design and implementation for the data warehouse was ignored by various organizations but slowly they started adopting a modern approach to handle the data mining, analytics, optimization and reporting.

Let’s have a look at the difference between is a look at traditional BI approach vs Modern approach:

Modern BI Approach

By taking a holistic view of your entire organization and the data gathered in every function, enterprise BI knocks down silos and provides advanced solutions, with a deep understanding of how disparate data from across the enterprise can, when used the right way, provide maximum benefit to the entire enterprise.

While the examples above are broad and hypothetical, many major companies have already benefited from enterprise BI, ranging from giants like Amazon and Netflix, to relatively smaller, niche organizations like music analytics platform Next Big Sound, and digital game developer Miniclip. Read on to discover more about how enterprise BI works.

 

Major Components of Business Intelligence

Although Business intelligence and analytics implementation differs in the details for every organization, there are a several underlying principles that are relatively constant. These include:

Data Optimization

A primary component of BI is making data more efficient to access and analyze. This is achieved in several ways:

  • Data aggregation (a centralized data warehouse enabling faster querying);
  • Data cleaning (standardizing data so that it’s better able to “communicate” with other data);
  • OLAP (online analytical processing, a way of quickly analyzing data via multiple dimensions)
  • Denormalization (optimizing data query times in several ways, including allowing redundant data storage)

Real time analytics

True BI is an “always-on” process, enabling agile, nimble responses to inefficiencies or problems as soon as they’re detected. In practice, this means access to real-time metrics and dashboards, as well as an alert system that ensures that no time is wasted in producing a response.

The data from sources like sensors, markets, logs, social interactions, purchase/spends can be processed for Real time analytics.

real-time analytics

Predictive elements

Your raw data is a record of history: how a process has been performing, how customers have been making purchase decisions.

With BI, the vast amount of historical data on hand is put to use in vast simulations, drawing on statistical inference and making use of ML and AI tools to provide probabilities for future events and behaviors, which can then be put to use to make more informed decisions in a broad range of areas, including development, marketing, sales, budgeting, and even hiring and promotion.

predictive analytics

Credit:Dataschool

KPI insights

KPIs (key performance indicators) are often seen as an intractable, “conventional wisdom”-driven metric. Enterprise BI can detect surprising data patterns that may change the way you look at, choose, and assess your KPIs – ultimately resulting in improved performance and results.

KPI Insights

 

Unstructured and unconventional data sources

The typical image of big data, and databases in general, is row after row of numbers, along with simple text data like names. A key differentiator in BI is the ability to draw on unstructured data, which is typically in an unconventional, “un-quantifiable” format like long-form text, such as customer reviews or comments.

The benefits of the advanced technology behind BI mean that this data can be analyzed on its own and in relation to more traditional data, providing even more easily accessible, digestible, actionable information.

Structured-unstructured

Comparison of Top BI tools

Choosing the right BI tool means asking a few questions of your organization and your stakeholders: Which tool is right for analyzing the data you have on hand, and the market data needed to make decisions? Which tool makes sense for the people who will be using it? And which one can produce the output and results that you’ll need?

Below, we take an in-depth look at several of the solutions available:

Cognos

Developed by IBM, Cognos is widely used and delivers one of the most user-focused, intuitive end user interfaces available. It’s ideal for users who frequently make use of data in presentations, such as business cases to upper management.

Key features include real-time analysis, ready-to-present data visualization tools, and the ability to quickly share information with colleagues.

Domo

Another broadly-used tool, Domo is designed for relevance to the entire organization for businesses in almost any industry. Domo’s BI reporting tool is especially multifaceted and powerful.

DomoKey features include real-time alerting and a robust mobile app for data access and management from anywhere.

Qlik

We’re now looking at tools with more specialized benefits. Qlik is ideal for organizations where data might be more limited, difficult to clean, or just considered “incomplete.” It’s ideal for organizing and analyzing even these types of “difficult” data, providing insights which otherwise may not have been possible.

Key features include an associative engine which connects all available data in such a way that makes it possible to infer the conclusions described above, even in sub-optimal data sets.

Pentaho

Yet another tool which is ideal for pulling in data from all areas of the company and enabling it to “talk” to each other to generate useful analytics and reports. Pentaho is ideal for companies involved in production or manufacturing, as it specializes in integrating data from connected, IoT (Internet of Things) devices.

Pentaho

Key features include the above-mentioned IoT focus and advanced visual report- and analytics-generation tools.

Spotfire

Taking a more predictive, probability-driven approach, Spotfire draws heavily on artificial intelligence for organizations in competitive industries where which trend forecasting is a critical need.

Spotfire

Key features include real-time analytics, identification of potential data inconsistencies, and location-based analytics.

How Is Business intelligence being used?

We’ve discussed several business intelligence use cases already, both in the introduction (with our hypothetical companies), as well as some of the optimal uses for the BI tools above. Now, let’s take a closer look at some of the practical benefits of BI across various industries.

BI in Banking & Finance

  • Via the data warehouse and BI tool, centralize access to disparate internal KPIs like lead time, cycle time, sales, and more, to analyze and make decisions regarding overall employee performance
  • Analyze customer satisfaction in key areas like service and performance, identifying areas for improvement to increase value to customers
  • Zero in on process improvements and service offerings that can help court targeted, high-value clients
  • Track and process data on internal processes and company culture, using the results to identify process efficiencies and optimize the environment for growth
  • Harness the potential of the data warehouse and BI tool to generate reports and other personalized content to improve customer relations

BI in Retail

  • Use PoS & beacon data to offers discounts to customers when they enter the store
  • Optimize inventory and stock management by drawing on RFID, PoS, and/or beacon data to order, fulfil, and stock merchandise
  • Draw on continuously processed data to drive real-time merchandising updates, optimizing the customer path at a granular level and increasing spend
  • Tailor inventory orders with demand and fulfillment forecasting informed by real-time supply chain analytics like seasonality, shipping distance, economic and market factors, and more.
  • Turn personnel scheduling into an efficient, fast process by utilizing data based on promotions, season, historical sales, and competitor

 

BI in Healthcare

  • Predict specific efficacy scenarios for different medications and treatments on a patient-by-patient basis using data drawn from testing, wearable fitness devices, and the wealth of historic data.
  • Centralize and provide easy access, via BI tools, to the entire contents of a patient’s medical history, collected over years from numerous sources.
  • Create easily understandable charts and graphs with intricate, visualized details on patient health, treatment plans, test results, medication regimens, and more.
  • Identify disease risks with increased accuracy based on both personal medical data and a broad range of environmental factors and data
  • Improve and streamline communication between medical care providers when a patient is visiting different facilities and specialists.

BI in Manufacturing

  • Store and optimize data pulled from existing connected, IoT devices which previously had separate, isolated data management and storage systems, increasing the ROI for these installations.
  • Analyze and classify production alarms and errors in real-time, allowing for faster diagnostics and remedies.
  • Install and optimize predictive maintenance protocol based on machine and process data, creating a more effective practice than simply following set schedules.
  • More efficient automation through continuous limit monitoring, reducing or eliminatingmissed alarms and allowing for closer limit tolerances
  • Data warehouse and real-time analytics are key to the innovative cyber physical systems that will define Industry 4.0 – improvements in efficiency, quality, safety, and more.

BI in Supply Chain Management

BI-in-supply-chain

  • Fully integrate, normalize, and analyze data from all steps of the supply chain, from raw material through to the facility and/or retail location itself, to identify efficiencies and improve forecasting.
  • Evaluate current suppliers and identify potential new partners through historical data: on-time delivery, damage rate, customer satisfaction, and more.
  • Anticipate, respond to, and neutralize cost fluctuations with historical and real-time global data
  • Optimize ordering and delivery schedules more quickly and efficiently
  • In real-time, identify and respond to abnormal fluctuations in commodity pricing, adjusting ordering and inventory accordingly and immediately.

The Business Intelligence Process

With an understanding of what business intelligence tools look like, and some examples of how they can be put into use – and what the benefits are – let’s close with a look at the details of the process itself. These steps are the underpinning of what ultimately leads to better processes and results for your organization.

Pulling up the data

In this initial step, all aspects of the available raw data are assessed: scope, type, source, state/suitability, and more. This initial audit defines the methodology and, potentially, the tool or tools that will be used to clean, standardize, aggregate, and optimize data for centralized use.

Tool deployment and installation

Once the most suitable tool or tools have been identified, the service provider will install and deploy the tool, either as a managed solution, a SaaS solution, an on-site installation, or some combination of the above. Client and customer concerns and requirements, such as security and auditing, must be considered here. At this point, adoption and training efforts within the organization may begin.

Big data integration

With the BI tool installed, all existing data can be analyzed to provide the desired analytics and insights. More importantly, new data is continuously and efficiently being integrated alongside existing data, providing the basis of the real-time analysis and alerts that are a key component of BI.

Cloud integration

A cloud solution is most often the right choice for the scale of data included and generated in a BI implementation. The right service provider will combine the storage benefits and efficiencies of the cloud with performance-maximizing practices like de-formalizing data for optimal results.

Visualizations

BI dashboards take all the data being constantly analyzed and provide a broad range of visualization and summary choices to make it presentable, usable, and actionable. Visualizations can range from the easily digestible, like charts and graphs (which may appear simple but are driven by advanced BI analytics), to more intricate formats like heat maps, candlestick charts, and beyond.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

3 Emerging Technologies Transforming the Health Care Landscape

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The rate at which technology is changing our everyday lives is truly remarkable, and one especially transformative area is health care. Emerging technologies are primed to disrupt many aspects of patient care in increasingly advanced ways, and they’re making their way into health care on a minor scale in mobile and wearable technologies.

These devices started as popular non-medical fitness monitors but are beginning to expand into medical-grade wearables, home health monitoring tools, and mobile care apps. Where else will technology take health care — and what do these advances mean for IT professionals?

1. Wearables, mobile apps, and big data

Researchers predict the wearable medical devices market will reach $14.41 billion by 2022, up from $6.22 billion in 2017. Until now, these devices had been limited to individual fitness trackers that connected to smartphone apps, but they are poised to offer real-time access to medical records as well as diagnostic and treatment functionalities. This could help empower patients to take control of their health, improving patient outcomes and saving health care providers and patients time.

To make this transition, medical device manufacturers, health care records system providers, IT developers, and health information regulators will need to learn how to integrate patient-generated data into their workflows and products. Privacy and security concerns, data relevancy to clinical situations, and big data handling are the biggest challenges facing the widespread adoption of clinically relevant personal medical-grade devices. Health IT managers and developers may need to look at Internet of Things (IoT) application programming interfaces (APIs) and standardization techniques to help handle this unstandardized user-generated data.

2. Machine learning and artificial intelligence (AI)

AI and cognitive computing technologies are able to integrate patient-generated and IoT big data. These technologies use algorithms to mine large datasets, recognize patterns, and make connections between disparate items in ways that mimic the human mind — but much faster and more comprehensively than any medical professional can. Savvy developers can tie these cognitive computing platforms to electronic health records to spot trends not only within a single patient’s records but also across patients to assist doctors in recognizing anomalies as well as diagnosing and treating patients with similar conditions.

AI is also likely to play an important role in researching and developing treatments for many health conditions. Using large centralized data repositories, these AI systems can store vast amounts of data generated through health care systems, the IoT, wearable medical devices, and more to gain deeper insights into some of the most impactful health issues such as heart disease, diabetes, Alzheimer’s, and autism. Health care providers, developers, and IT decision-makers alike will need to work together to develop big data gathering methods and analytical tools to best take advantage of the tremendous benefits machine learning and AI can offer health care industry insiders and their patients.

3. Blockchain

The third technology trend transforming health care today is blockchain. Blockchain is the technology behind Bitcoin, the cryptocurrency that’s shaking up the financial world. Blockchain is a massive distributed network of replicated databases containing records stored on an encrypted ledger. No central administrator exists, users can change only the blocks of records they have access to, and software time-stamps any entries or updates and syncs them across the other networked databases. Because of the massive amounts of data surrounding the health care industry as well as the need for security and adherence to privacy regulations, blockchain offers tremendous potential for many areas of the industry, including secure patient medical record storage, clinical trial data privacy, drug development, supply chain integrity, as well as medical billing and insurance claims. Although still in its infancy, blockchain will likely have a significant impact on the health care industry going forward.

As technology continues to disrupt the health care field, both patients and providers will likely benefit from improved diagnostic techniques, treatments, record keeping, research, security, and so much more. Only by staying abreast of these technological advancements will software developers and IT decision-makers find opportunities to optimize their health care software projects to integrate with and take advantage of blockchain, AI, and wearables, allowing them to offer the medical advantages this new technology enables to their patients and stay ahead of the competition.

Our experts at Parkar Consulting & Labs have the knowledge and expertise to help you make the most of emerging technologies so you can pass them and the value they bring to your customers. Contact us today to learn how we can help.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

3 Big Ways Artificial Intelligence Is Changing Software Development

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Agile technologies such as DevOps and continuous integration, continuous delivery (CI/CD) practices have brought about positive changes for software developers over the past decade. From faster time to market and greater collaboration between development and operations teams to fewer end-user issues and improved testing, DevOps has changed the way developers work. In a similar vein, artificial intelligence (AI) is poised to change how organizations and their engineering teams approach software development.

AI’s promising potential

Software engineers are tasked with creating solutions for the myriad problems, challenges, and everyday organizational tasks in most every industry imaginable. Ironically, they even develop the tools that make their development processes easier. AI is well-suited to helping software engineers develop these intelligent software tools because it can learn from and replicate human behaviors. Because of this, AI and machine learning algorithms can impact nearly all areas of software development.

Best uses for AI in software development

AI and machine learning have already made big impacts in software development. Here are three of the most important ways it is changing the development landscape and the evolving role of software engineers.

  1. Estimating delivery schedules — When development teams work together for long periods of time, they become fairly adept at estimating delivery times, although they may still encounter challenges due to a variety of influencing factors, including flawed code and changing user demands. AI can help development teams make more accurate estimates, even with the numerous and diverse factors that come into play. And as the AI programs gather more data and learn from other development projects, the accuracy of those estimates is likely to continue to improve.
  2. Project management — AI systems can take over daily project management tasks without the need for human input, according to The Next Web article. Over time, they can understand project performance and use that knowledge to form insights, complete complex tasks, and help human project managers make improved decisions.
  3. Testing and quality assurance — Developers are creating tools that use AI to detect flaws in code and automatically fix them, according to the Forbes article. This is the logical next step after testing automation and will likely lead to higher-quality software and improved time to market. Software engineers could have less involvement in testing mechanics but would shift their roles to approving and acting on test findings. In other words, AI could streamline software testing by providing the right data to the human engineers who can then make better decisions.

evolution of the programmer

Best practices and AI’s future

Based on these changes alone, it seems AI and machine-based learning are primed to disrupt the software development field. What does that mean for development company leaders, software engineers, and software development in general?

Overall, AI will likely help software development become better, faster, and less expensive. However, for this to happen, engineers would have to learn a different skill set so they could build AI into their development toolboxes. They’d need more data science skills and a better understanding of deep learning principles to reap the full benefits of machine-based learning. Also, instead of turning to logging and debugging tools to find and fix bugs, engineers would need tools that allow them to question the AI to find out how and why it reached a particular conclusion. In addition, AI could allow more tasks to run autonomously and require fewer daily management tasks. Finally, developers could use AI for routine tasks so humans can focus on what makes them human: thinking creatively according to the problem’s context, something that AI has not yet mastered.

Will AI eventually replace the human element in software engineering? Not likely, but it certainly has the potential to make development faster, more efficient, more effective, and less costly all while letting engineers and other development personnel focus on honing their skills to make better use of AI in their processes.

For the best in thought leadership on emerging technologies, look to the software development experts at Parkar Consulting & Labs. Contact the Parkar professionals today.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

Rapid Test Automation

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In today’s competitive landscape, enterprises and organizations need to assess the best way to execute test automation for their different projects. This has developed an awareness regarding the value that automated software testing can bring.

A well-established test automation methodology brings in predictability, repeatability, and agility, and thereby drives software engineering to a higher degree of quality. Test Automation Assessment helps understand whether or not an application requires to be automated. Based on certain criteria, recommendations are made that help decide whether an application really needs to be automated, and the advantages that may thus be achieved. Test automation assessment is usually performed either for customers with an existing test automation framework, or for clients with a want for a new test automation framework.

However, to continuously evolve best quality applications, organizations need to consistently test the automation process. Rapid Test Automation Assessment (RTAA) is a commonly used approach that enables organizations test the process.

What is a Rapid Test Automation Assessment?

If the test automation assessment is to be achieved within shorter timelines than the normal time frames, RTAA becomes a necessity. RTAA refers to a fast analyses and execution of a TAF that fits in a small environment, specifically created based on the criticality of the test cases.

4 Steps for a Rapid Test Automation Assessment

  • Understand the Existing System: This involves analysing the current state of quality assurance and testing methodologies being followed. An inceptive understanding of the system, their technology, processes and testing information will be taken up as part of the assessment. An understanding of the system is known through knowledge of the objectives, a know-how of the technology stack is taken up, user flows is identified, and analysis of the manual test cases if any.
  • Assessment: Utilization of the tools and the extent of their automation readiness approach will be identified in this step. A requirement traceability matrix is prepared that details the extent of test cases, business and details of the functional requirements and areas of quality enhancement. Tool feasibility and confirmation in addition with automation ROI analysis is also taken into account as part of the assessment approach. But foremost, the top few of the most business-critical test cases are recognized.
  • Proof of Concept (POC) to Demonstrate Feasibility: This phase comprises of implementing a TAF for the environment and executing only the identified critical test cases for conducting a POC. The POC will help identify financial and operational benefits and provide suggestions regarding the actual need for complete automation.
  • Recommendations & Implementation: Specific test automation tools, automation feasibility, and automation approach will be clearly defined in this phase.

Primary assessment focus areas are automation framework, automation integration and its fitment in the SDLC. In automation framework areas, reusable function libraries, test object maps, exception, error management etc. will be detailed.

In the automation integration focus area, test management, source code repository, and defect management, continuous build management etc. will be defined. In the fitment in SDLC focus area, details like existing /target automation coverage, metrics, test prioritization etc. will be listed.

Outcome of the Rapid Test Automation Assessment

The output of this rapid test automation recommends appropriate automation strategies and executes them to improve testing quality, minimize testing effort, schedule and ensure return on investments. A comprehensive report of the process, tools and people will be provided. Predictions for effective project management, simple details on the response and demand for continuous communication with business teams and the need to absorb changes recommended by business will be defined. Execution of tools to effectively track defects and a well-defined test strategy document covering all aspects of testing requirements will be provided.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

Mobile Test Automation

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Introduction

Mobile phones are designed for the customers who want to do everything with a single device through quick access to data, alerts, business processes, transactions and reports. Mobile applications have nearly replaced web applications for consumers.

Mobility Testing

Testing of mobile applications is different and more complex than testing traditional desktop and web applications. Mobile applications need to be tested on a variety of software platforms, versions and under different network connectivity conditions.

Unlike the desktop world, where PCs are established as standardized reference hardware, the wide variety of device form factors (e.g. phones and tablets of various screen size and software platform) adds another layer of complexity in testing mobile apps. Device diversity is an especially acute problem for Android devices – even the official 1 Android device gallery includes over 60 devices of various screen sizes, resolutions and form factors.

Device and platform diversity, short release cycles, lack of mature testing tools and the variety of network connectivity options result in frequent cost overruns and missed deadlines in today’s mobile application testing environment. Moreover, the rapid pace of mobile OS updates, frequent introduction of new devices, new features of mobile apps and customer expectation of quick upgrades require #Continuous Integration Methodology to be deployed & hence #DevOps and Continuous Testing.

Mobile Test Strategy

A comprehensive mobile test strategy which  includes device and network infrastructure, optimized selection of target devices, and an effective combination of manual and automation tools to cover both #Functional and #Non-Functional testing has become essential for getting mobile applications to market on time and within budget.

Main Elements of Mobile Test Strategy

  • Target Device Selection – Create an optimal mix of simulator and physical device testing on different models to maximize test coverage.
  • Test Automation – Select an effective test automation tool and maximize the use of automation to reduce the cost of functional and regression testing. 
  • Network Environment – Consider testing primarily on WiFi networks and network simulation tools to simulate cellular connectivity and various network conditions.
  • Types of Testing – Consider different types of testing required (functional, regression, cross platform, usability, performance, security, and compliance).

Mobile Test Automation

Automated testing is a highly effective approach to mobile application Quality Assurance that can provide significant business returns, provided it is implemented by using the right tools and architecture, factoring in cross-platform challenges. However, automation requires a significant initial investment (in a test tool as well as scripting) and ROI is realized when the same automated test is executed multiple times on different devices/OS with negligible incremental cost.

The key points to be considered during the selection of Mobile Test Automation tool:

  • Provide low cost solutions, avoid high license fees or operational expenses
  • Support test case execution on multiple mobile device platforms
  • Modular & easily up-gradable to keep up the pace with new trends in mobile market & support new iOS/Android/other OS versions.
  • Integrate/inter-operate with other test management/bug tracking/build management systems
  • Accessible by non-technical Test SMEs to maintain good blend of technical & Subject matter knowledge within test team
  • Test data management should be separated from automation scripts so that test data sets can be changed with little overhead
  • Ability to test a specific chunk of suite as well as complete suite.
  • Support batch execution
  • Customized test reporting

Conclusion

Mobile Automation is still in its nascent stage with major scope to improvise. The principles and success factors of test automation for mobile applications are different from those traditionally applied to conventional applications. Understanding of the business dimension, selection and implementation of right automation test tools and ability to manoeuvre technology challenges are the success factors for building and executing a robust Mobile Test Automation Strategy.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

© 2018 Parkar Consulting Group LLC.