Wipro’s Survey — State of Intelligent Enterprises — received inputs from 300 decision-makers of the US and UK firms. The survey was focused on understanding organizations’ current state of ‘Intelligent Enterprise’ and the use of AI across various business functions.
Divided into four key areas, Wipro’s Survey provides insights into intelligent enterprise’s current state, challenges and benefits, adoption of AI, and future success.
One of the startling revelations was: only 17% of the respondents use AI across the entire organization. But, 80% of decision-makers understand the importance of AI and believe the technology is vital for being an intelligent enterprise. So, what is stopping them from embracing AI and other automation solutions?
Of the many challenges organizations witness, data security is the most pressing issue. Today, it is essential for tech companies to securely streamline data across various business functions to accomplish insights and make decisions in real time. However, companies struggle with security of data and other data-related barriers; 91% of business leaders feel data barriers limit organizations from becoming an intelligent enterprise.
Wipro’s Survey –State of Intelligent Enterprises
One possible solution for organizations that are struggling while mitigating such challenges is to collaborate with the right partners. Along with IT partners, other ways of accomplishing the intelligent enterprise is by investing in people by reskilling the workforce, investment in technology, research and development, and more. Besides, 95% of respondents said that the use of AI is also essential to become an intelligent enterprise.
69% of respondents say that investment in technology is one of the top three enabling factors for becoming an intelligent enterprise.
However, the companies are willing to transform their business, but the decision-makers believe roadblocks like the cost of new technology, security concerns over new technology, navigating organizational culture, and restriction due to dated systems are major challenges.
Wipro’s survey also notes that intelligent enterprise brings numerous advantages for organizations as it strengthens IT security, improves customer experience, enhances agility, among others.
Data science aspirants, be it freshers or other IT professionals, believe that obtaining online certification is more than enough to land a job in the competitive domain. This is mainly because they consider themself data scientists after acquiring the knowledge of tools and techniques from different sources. Aspirants think they are ready to join any organizations and analyze whatever data they are provided with. However, this is a myth. You are not always provided with the necessary data in organizations. You would not have everything readily available at your table to apply your learning from MOOCs or other courses. Consequently, devising a data science career and succeeding in it is not as straightforward as aspirants envision.
To help our readers make the right data science career decision and understand why data scientists fail to deliver outside their theoretical knowledge, we interacted with Chiranjiv Roy, Chief of Data Science & AI Products at Access2Justice Technologies. Chiranjiv provided several valuable insights into the industrial practices of data science while also suggesting the right approach for aspirants to succeed.
We also asked Chiranjiv about his journey and the practices he embraced to make a fruitful data science career. In his 20 years long career, Chiranjiv has become a learned data scientist while working for some prominent organizations like ResoluteAI.in, Nissan Motors, Mercedes-Benz Research and Development, Hewlett Packard Enterprise, WNS Global Services, and HSBC.
Today, Chiranjiv is also a visiting faculty at Engineering and Business Schools. He has 14+ patents filed on the usage of data science to solve real-world automotive and manufacturing problems by developing, enhancing products and gaining efficiency.
Chiranjiv’s Data Science Journey Has No Shortcuts
Chiranjiv has a Bachelor in Statistics and Dual Masters in Statistics and Computational Mathematics, and a PhD in Applied Data Science for Industrial Engineering. His foundation was laid with a keen interest in computational mathematics, physics, and applied statistics during his education. The love for data helped him carry out his master’s thesis and research work on failure models in a manufacturing factory, thereby giving him the confidence to keep learning.
Starting his career at HSBC in 2001 as a Data Analyst, Chiranjiv’s expertise with data and statistics helped him quickly become a manager at the company. After his five-year and eight-month spell at HSBC, he moved to WNS Global Services as a Senior Data Manager. Later he went on to become a Lead Data Scientist at Mercedes Benz and then Nissan Motors. “Data science was not a field of great importance or as popular as it is now when I started, but just got lucky that I never had to make a shift in my career and had my journey from data engineering to data science in the last two decades,” says Chiranjiv.
While working in countries like the US and India for companies like Hewlett Packard, Nissan Motor, and Mercedes-Benz Research and Development India, and more, Chiranjiv has extensively worked in the area of risk management, automobile, manufacturing, and optimization systems. He believes that working with top-line researchers has helped him learn and fall more in love with data science in developing Real-Time Applications of Data Engineering, Analytics and Sciences in Data Monetizations.
Chiranjiv says it takes years to become a data scientist since the roadmap is quite linear. Over the years, Chiranjiv has learned about the field in academics as well as while working for various organizations to gradually become the data scientist he is today. However, witnessing the unrest in beginners/practitioners about becoming a data scientist, he stresses the fact that today aspirants want to quickly dive into their data science career without understanding the intricacies of the landscape.
“Data engineering and analysis is the first step to be a good data scientist. But, aspirants believe learning some programming languages and algorithms will make them a successful data scientist. What they do not understand is that a data scientist is a problem solver not only a Python programmer,” says Chiranjiv.
To become a problem solver, one should know or understand the business challenges and convert them into data problems. Practitioners, in contrast, try to fit algorithms into problems. Given a business challenge, beginners immediately think of applying some machine learning models without even understanding the business domain or the real challenge.
This mostly happened due to numerous misinformation spread by different sources. Consequently, Chiranjiv explains the ideal approach, which one should follow in order to succeed in their data science career.
“From data science courses one can only obtain the fundamentals. But, that is not enough to solve problems. One needs to spend time in understanding the problem and knowing which domain the problem is related to. If you do not understand the business domain, talk to people who are well versed in the domain. The most critical aspect of becoming a data scientist is to understand the domain before forging towards data analysis. Unless you acquire domain knowledge, you should not jump into solving the problems by fitting data science algorithms,” says Chiranjiv.
“Once you have the business understanding, the next step is to assess the data and form business problems. Following this, you can start developing the models. That means, you need to evaluate which model is the right fit and then create a proof of concept (POC). However, this does not end here.”
“As a data scientist you also need to be a good storyteller. After the POC, you have to leverage your visualization skills and showcase how your models’ result can be effective in mitigating business challenges to the decision makers.”
“Storytelling is an essential skill for data scientists as top management do not know which machine learning or deep learning models are implemented to get the results. This is where visualization simplifies the job of decision makers in inferring outcomes and forecasting the value models can deliver. But value is only created if you are able to communicate effectively with the product developers. For this, you need to have knowledge of agile-based software development practices. In organizations, data scientists’ efforts can only bring business growth if their models are in the production,” he adds.
Remember, a Jupyter notebook model running on the cloud will not derive tangible benefits expected to “Solve the problem” hence application development is the key for a data scientist as everyone loves to see an application to play with.
Chiranjiv Roy
Is a PhD Essential To Succeed?
PhD teaches your process, time management, approach perfections, focus amidst enormous challenges, a self-starting attitude and helps you become a problem-solver more than a researcher.
When asked about whether PhD candidates have the edge over people who learn from online courses, Chiranjiv said that it depends on what aspirants want to become. There are two aspects of data science: Approach and Implementation. Academics focus on teaching the best approaches to solve problems. But, three or six month online courses can only teach the fundamentals of data science.
You should take at least a year-long course to ensure you go beyond just the fundamentals and learn the best data science approaches. This is where long-term courses, primarily PhD, assist; which is apparent through the number of research papers they publish.
Although long-term programs make you exceptional at approach, for the second aspect, implementation, you need to develop a product mindset. More often than not, the ideal approach for a solution might not be feasible to make a product. For instance, while solving a business problem, if you develop one of the superiorclasses of neural networks or deep learning techniques, the practical implementation might not be possible because of the absence of the required libraries or computational resources. Then you have to compromise on the best approach, which gives 99% accuracy versus an approach that can be implemented with the available libraries and computational resources but only delivers 90% accuracy. Such bargains have to be carried out by data scientists as product development strategy includes return on investment, timeline, and agile principles.
Academics can give you knowledge of fundamentals and techniques. But, your intelligence will come into the picture when you talk to the software development team and come out with the top five models out of which they might be able to implement the number three, four, and five to achieve the same result as the non-feasible but the best models — one and two. Optimizing the number three, four, or five models to achieve the outcome of what the best models would deliver is your intelligence. This is what organizations expect from data scientists.
You cannot make products just by doing MOOCs or other academic courses. It is not always about the best approach, you can only have a successful data science career in organizations if your skills can help deliver products. There is a high probability and propensity that your top three models will never get into the production — this is a practical reality.
Most of the business challenges in the world are solved by regression and support vector machines. Then who wins the game? Is it the data scientist who deploys basic approaches and makes products or the data scientist who has learned every approach in the world but keeps struggling to get his/her model into the production? The former, since he/she can bring business value to organizations with data-driven products. Which means a PhD is not necessary. You can learn from any resources but make sure that the fundamentals are clear.
However, if you do not want to work in organizations and be a professor, PhD is a must.
Working with a wide range of technology organizations and entrepreneurs has helped Chiranjiv gain knowledge in highly regulated industries such as financial services, manufacturing, IT systems, and automotive.
Currently, as a data science leader at Access2Justice Technologies, Chiranjiv manages a team of data scientists, communicates with stakeholders, and creates a culture for the team to thrive. According to Chiranjiv, culture can be the difference between a successful and a bummer data science initiative, which can define organizational growth.
To accomplish the right culture, Chiranjiv pinpoints the importance of a leader’s role in companies. A leader has to set aside his time to guide the new joinee by educating them about the problems and ensure the delivery of desired results in the future. But, this seldom happens. Often, professionals are left to fit in the team, as a result, organizations fail to harness the full potential of practitioners.
As a part of his job, Chiranjiv also hires and promotes data scientists and ML engineers for potential roles. He assesses their intent to learn, knowledge of data structures and algorithms, and a self-challenging attitude. “I also help leaders and HRs who ask me to recommend proficient data scientists. As a result, I always helped aspirants/professionals in obtaining the first role or a good transition.
Connect, Content, Create and Convince is the mantra for success
Chiranjiv Roy
Final Thoughts For Aspirants
For aspirants, Chiranjiv explains that data science is value-driven, it is not cost reduction or resource calibration for any business. He suggests that problems are everywhere, which means one can obtain data from a wide range of things around them; Kaggle is not the only place where you can get datasets.
To further clarify, Chiranjiv cited an instance where a student from IIM asked him how he will get an internship or a job or newer datasets on Kaggle to solve real-world problems, given the hiring and the activity on data science platforms have slowed down due to COVID-19. Chiranjiv explained to him how he could use an Arduinoin his hostelbetween the power source and televisions of different brands to collect the data of power fluctuation during power outages. Then he can use the gathered information to create a time series plot and develop a model to analyze the surge in power of several television brands. Eventually, he can come to a conclusion and showcase which manufacturer is more reliable, thereby helping the college to buy superior televisions in the future.
Going back to coding, working and mentoring startups from understanding actual business problems, developing system designs, data architecture, data models, DevOps, DataOps, and MLOps, the world demands from a data scientist is a leader who can take a concept to real-life product.
“Aspirants should come up with different use cases on their own, this will showcase their interest in solving business problems. However, aspirants are dependent on Kaggle for datasets because it prepares and provides the data. The reality, in contrast, is that you will not get data while working for organizations. You only get problem statements. One has to think through to find different sources to collect data, and trust me DATA IS ALL AROUND US,” concludes Chiranjiv.
Stay tuned to our website for our upcoming column: How To Become A Successful Data Scientist...
OpenAI rolls out the pricing of its GPT-3. A researcher named Gwern Branwen revealed the subscription plans through a Reddit post. It was mentioned that the users who got access to the beta API of GPT-3 had received the subscription plan of the largest natural language model. However, not many would be able to get their hand on the model extensively as GPT-3 costs around $100 to $400 per month.
There are different pricing plans:
Explore: 3-month free trial with a limit of 100K tokens
Create: $100/month with a limit of 2M tokens. 8 cents for every additional 1K tokens
Build: $400/month with a limit of 10M tokens. 6 cents for every additional 1K tokens
Scale: You will have to contact OpenAI
As per Branwen, 3,000 pages of text can be written by GPT-3 by utilizing 2M tokens.
OpenAI started private beta on 11 July, where one can request for access to the API for free. But, from 1 October, users will have to pay to leverage the arguably superior artificial intelligence language model.
This does not mean that the API will be available for the general public from 1 October; it will still be private. Nevertheless, you can assimilate the pricing, although the plans may change in the future.
Since the release of GPT-3 API, users have created never-before-seen use cases such as code generation, semantic search, chat, synthetic writing, among others. A wide range of GPT-3 examples can be accessed on the official site of OpenAI.
Although many see GPT-3 as a game-changer, several practitioners, along with the CEO of OpenAI, believe that the model is overhyped.
Undoubtedly, uses cases of artificial intelligence that look good on social media rarely comes to life due to several constraints during productization.
Consequently, we will have to wait and see whether the model can assist researchers and data scientists in building groundbreaking real-world solutions.
With the uncertainty of GPT-3’s effectiveness in the real-world, the pricing of the GPT-3’s API might be costly for developers to leverage the model once it is released for everyone.
The Odisha Government has made all the courses on Coursera free for its residents. One can not only access data science courses but also all other existing Coursera courses. The initiative was undertaken under the banner “Skilled in Odisha” to ensure people can upskill during the COVID-19 pandemic. This will allow people of Odisha to come out stronger with new skills post the lockdown.
To get access to the Coursera platform for free, you will have to register for the special skill development program here. In addition to Coursera courses, the initiative also offers access to SAP ERP. You can choose either Coursera or SAP ERP based on your interest. However, Analytics Drift recommends opting for Coursera as you can learn from a wide range of courses right from data science to web development and android developments.
It has been a few weeks since the start of this initiative, but only a few aspirants have shown interest that is evident from the number of page visits (19337 at the time of writing this news). However, The Odisha Government will stop accepting requests for free access to these platforms on 29 September 2020.
Nevertheless, you can register for access by filling the necessary details like identity card number, name, phone no, email address, and current address. After the registration, you will get an email immediately with a registration id, which can be used for further communication. However, you will have to wait for a few days — around 1 to 5 days — before you get a final confirmation from the Odisha Skill Development Authority (OSDA) about your application after verifying the provided details/documents.
On receiving the final confirmation email, you can create your Coursera account with the email you provided during registration. But, if you already have a Coursera account with your email id, Coursera will automatically identify that you have been provided free access to all the courses by the Odisha Skill Development Authority.
You can enroll in multiple courses for free and get certificates post-completion. The access, however, will be revoked on 30 December 2020. Consequently, you will have to complete the courses before the free access ends to get the certificates.
Hurry up! Learn as much as you can in the next four months.
Kneron, an edge AI chip maker for edge devices, has released its latest chip that is at least 2x more power-efficient than Intel and Google’s existing processors. KL720 AI SoC focuses on driving the processing needs of real-world use cases such as Smart TVs, AI glasses and headsets, and high-end IP Cams.
According to the company, KL720 AI SoC comes in at 0.9 TOPS per Watt to provide better performance for AI workloads, of course, with an added 2 to 4 times the power-efficiency of the rest. Kneron leverages the Arm Cortex M4 CPU for navigating different workloads across the resources.
The processor can handle heavy workloads at the edge by processing 4K images, Full HD videos, and 3D sensing for fool-proof facial recognition and gesture control for gaming, among others.
Kneron earlier chip–KL520–was focused toward edge AI for smart locks, security cameras, drones, and home appliances. However, with its latest processor, it has doubled down on high-performance tasks at the edge. What differentiates Kneron is that its products deliver high power efficiency. Its first-generation chip (KL 520) runs on 8 AA batteries for fifteen months, thereby delivering value without hassles.
“KL720 combines power with unmatched energy-efficiency and Kneron’s industry-leading AI algorithms to enable a new era for smart devices. Its low cost enables even more devices to take advantage of the benefits of edge AI, protecting user privacy, to an extent competitors can’t match. Combined with our existing KL520, we are proud to offer the most comprehensive suite of AI chips and software for devices on the market,” said Kneron founder and CEO Albert Liu.
The robustness of their solutions has been recognized by Gartner’s Cool Vendor in AI Semiconductor 2020 and some funding of $40M in January 2020. The company is committed to enhancing end-to-end integrated hardware and software solutions for edge AI.
LinkedIn Fairness Toolkit (LiFT) was released by the largest professional networking giant to enhance explainable AI initiatives by practitioners and organizations. This comes at the time when we are witnessing heated debate about the fairness of computer vision technology. Today, artificial intelligence is being used in a wide range of solutions right from identity authentication to determining defects in products and finding people with facial recognition. However, the lack of explainability in machine learning models has become a major roadblock that is hampering the proliferation of the latest technologies.
Although a wide range of libraries, services, and methodologies have been released in the last few years such as LIME, IBM Fairness 360 Toolkit, FAT-Forensics, DeepLIFT, Skater, SHAP, Rulex, Google Explainable AI, among others, the solutions have failed to democratize among users as they fail to deliver in large-scale problems or are very specific to cloud or use cases. Consequently, there was a need for a toolkit that can be leveraged across different platforms and problems.
This is where LinkedIn Fairness Toolkit bridges such gaps in the artificial intelligence landscape by enabling practitioners to deliver machine learning models to users without bias.
LinkedIn Fairness Toolkit (LiFT)
LinkedIn Fairness Toolkit is a Scala/Spark library that can evaluate biases in the entire lifecycle of model development workflows, even in large-scale machine learning projects. As per the release note of LinkedIn, the library has broad utility for organizations that wish to conduct regular analyses of the fairness of their own models and data.
Since machine learning models are now actively used in healthcare and criminal justice, it is necessary for an explainable toolkit to find correlation among different categories effectively. Consequently, LinkedIn Fairness Toolkit is a near-perfect fit for such use cases as it can be deployed to measure biases in training data, detect statistically significant differences in models’ performance across different subgroups, and evaluate fairness in ad hoc analysis.
In addition, LinkedIn Fairness Toolkit comes with a unique metric-agnostic permutation testing framework that identifies statistically significant differences in model performance (as measured according to any given assessment metric) across different subgroups. However, the Evaluating Fairness Using Permutation Tests methodology will appear at KDD 2020, the authors noted.
What’s Behind LiFT?
LinkedIn Fairness Toolkit
Built to work effortlessly on large scale machine learning workflows, the library can be used at any stage of the models’ development. As a result, you can carry out ad hoc analysis and still interact with the library for explainability. While the library provides flexibility as it can be used in production and even on Jupyter Notebook, for scalability while measuring bias, it leverages Apache Spark to enable you to process a colossal amount of data distributed over numerous nodes.
High-level and low-level APIs
The design of the LinkedIn Fairness Toolkit also offers multiple interfaces based on the use cases with its high-level and low-level APIs for assessing fairness in models. While the high-level APIs can be used to compute metrics available, with parameterization handled by appropriate configuration classes, the low-level APIs enable users to integrate just a few metrics into their applications, or extend the provided classes to computer custom metrics.
LinkedIn has been using LinkedIn Fairness Toolkit for various machine learning models for explainability. For one, its job recommendation, when checked with LiFT, did not discriminate between gender. The model in production showed no significant difference in the probability of ranking a positive/relevant result above an irrelevant result between men and women.
By making the library open-source, the social media giant is committed to further enhancing its functionality. The AI team at LinkedIn is also working toward bringing fairness while eliminating bias for recommendation systems and ranking problems.
To learn more about how the library was optimized for different types of tests and analysis, click here.
Qlik® today announced the acquisition of the assets and IP of Knarr Analytics, an innovative start-up that provides real-time collaboration, sophisticated data exploration, and insight capture capabilities, to complement Qlik’s cloud data and analytics platform. Acquiring Knarr Analytics advances Qlik’s vision of Active Intelligence, where technology and processes trigger immediate action from real-time, up-to-date data to accelerate business value across the entire data and analytics supply chain.
“Every process and decision can be informed and enhanced by real-time data to trigger action and augment decision making when it matters most – what we call Active Intelligence,” said James Fisher, Chief Product Officer at Qlik. “Acquiring Knarr Analytics will help us further advance customers’ Active Intelligence, enabling tighter collaboration between data stewards and business users that will increase data use and value throughout the organization.”
The need for real-time data-driven decision making, which requires merging and using new data sources on-demand, has exposed a gap in data and analytics supply chains. This gap requires a new way of thinking that centers on collaboration between all data and analytics personas, from data integrators and data stewards, to BI developers and analytics consumers.
Knarr can help create unique data and insight fabric by engaging more users throughout the analytical process, surfacing greater business context for both underlying data and resulting insights. This level of collaboration and sharing is essential to the creation of continuous intelligence at the core of Active Intelligence that drives action and value from data.
Knarr IP will enhance the Qlik Sense® analytics cloud platform Insight Advisor experience, as well as the data exploration experience in the catalog. Qlik’s customers will realize increased value and benefits through:
1. Sophisticated visual exploration of underlying data models before building analytics.
2. A glossary in the catalog for added business context, helping data consumers understand what specific data will best help answer their questions
3. Real-time, multi-player collaboration to generate insights interactively with a team, helping organizations remove barriers between data and analytics users
4. Ability to capture and share these insights with notes and snapshots, while automatically capturing the exploration state and context, enriching understanding and driving action
5. Increased effectiveness through machine learning of Qlik’s unique approach to Augmented Intelligence, helping drive more complex analysis and better outcomes for users of all levels
The terms of the deal are not being disclosed. Knarr Analytics co-founder and CTO Speros Kokenes, a Qlik Luminary, will be joining Qlik as a member of the Applied Research and Emerging Technology Team. As of today, Knarr Analytics products will no longer be for sale, and Qlik will support existing prospects and partners while bringing Knarr IP into Qlik’s cloud platform throughout 2021.
fast.ai — an independent research center that makes superior solutions for easy accessibility of deep learning — has released a free course, along with the book. The course — Practical Deep Learning for Coders — is aimed at the introduction to machine learning and deep learning, and production and deployment of models.
Practical Deep Learning Course by fast.ai covers the material from the book PyTorch: AI Applications Without a PhD. Every video lesson covers one chapter of the book that is also freely available if you do not want to purchase it from Amazon. PyTorch: AI Applications Without a PhD. is hosted on GitHub, where you can access the book in a freely available interactive Jupyter Notebooks.
However, the entire book is not covered in this Practical Deep Learning for Coders course. In the future, fast.ai will release the second part of the course that will complete the book’s remaining lessons.
Unlike most of the free courses and short-term courses, this deep learning course by fast.ai covers an end-to-end data science workflow as it also provides lessons on the deployment of models and data ethics. This makes it a must for aspirants who want to learn advanced techniques and make themselves relevant to the industry.
What you will learn:
Optimize models to get exceptional results in computer vision, NLP, recommenders, and more
How to turn your models into web applications, and deploy them
How to enhance your models’ accuracy, speed, and reliability
Ethical implementation while making models
Other techniques such as random forests and gradient boosting, parameters and activations, random initialization and transfer learning, among others
Over the years, fast.ai has been releasing some of the best deep learning courses online for aspirants to learn for free; Last week, it released a course on ethics — Practical Data Ethics.
Note: Please do not make the Jupyter Notebook version of the book into PDF and distribute it to respect the provider’s generosity. Use it only for your education or learning if you cannot buy it on Amazon.
Dataiku announced that it raised $100 million Series D funding for enhancing the platform to empower different data-driven firms to leverage data effectively. Lead by Stripes with Tiger Global Management and joined by existing investors Battery Ventures, CapitalG, Dawn Capital, FirstMark Capital, and ICONIQ. In December 2019, Dataiku was valued at $1.4 billion when Alphabet’s investment firm CapitalG poured money into the company.
In an attempt to democratize Enterprise AI, Dataiku is committed to explore new opportunities and include functionalities in its end-to-end data science platform. There is a sudden change in people behaviour, which has forced organizations to transform the delivery of services and products.
Dataiku wants to capitalize on the opportunity by allowing companies to bring resilience in the difficult times caused by COVID-19 with its robust platform. The firm already enables users to collaborate for data science projects, code to click, model development, model prediction, and model deployment.
Founded in 2013, today, the company has more than 450 employees, 300 customers, and partners around the globe. Due to its feature-rich Dataiku platform, the company has quickly gained traction in the data science domain.
I suspect in ten years we won’t be using spreadsheets
Florian Douetteau, CEO of Dataiku
Currently, Dataiku’s significant competitors are Alteryx, Databricks, and more, which have made reasonable grounds in the self-service analytics domain. All these platforms, including Dataiku, are working towards democratizing data by allowing users to get insight into information without programming skills.
According to Dataiku, with this Series D funding, the company will continue to simplify its platform for organizations to have hundreds, thousands, or hundreds of thousands of machine learning models in production.
Friends App, a social app, is an Indian platform where you can post short videos. Not only can you share and post videos in India but also share these across the world. One of the best features of the app is that it gives you access to the outer world, allowing you to stay connected to your friends and family. Enabling access globally, one can interact and know more about other cultures. Another great feature is that it introduces a new platform that can be trusted and is reliable for not only India but also across the world.
It is currently available on Google Play store, listed under the Social category, and has secured an amazing rating of 4.8 stars. The app is for indigenous users, for entertainment and movies. You can post your thoughts, feed, blog on your account and classify the language that the video is in.
Besides that, you can even select the language of the videos that you prefer. Follow the pages and people that you like in order to stay updated with their activities. Not only does it provide a platform for short videos and posts but also can keep yourself up to date with the world affairs. There is a feature named CineTV, which you may be able to see on the home screen. This feature displays short videos from across the world.
It has been downloaded by hundreds of people now and has gained positive feedback from those who have used it. Founded by Manju Patil and Mrityunjay Patil, Friends App is based in Bangalore. Currently, the app size is 15M, which is quite light and is compatible with Android devices of 5.1 and above.
We live in a world where many people struggle for what they are trying to achieve. The creative approach of the app might just be the best chance for people trying to do what they like to. The app tries to bring out the creativity that might be recognized somewhere around the world. You can get recognized by some known creators around the world and have an opportunity at something great that you can achieve with this platform.
As mentioned above, the potential of the app is unknown, but there’s almost no way it can affect society negatively. Since it is a platform open to anyone and everyone the content you’ll get is more likely to be authentic and not copied from somewhere else. It is a platform designed for everyone that can enjoy as well as present their talent.
With the Chinese apps on the downfall, people are looking for alternatives all over the world. The best part is, it is not an alternative to any other apps but rather one of its kind apps as it provides a unique platform to all the video creators that are focused on movies and entertainment and much more. This is a unique opportunity for Indians to come together, show their creativity, and grow along, staying updated with the world by introducing this platform on a global level.
Summarizing, it is an overall platform for all the users over the world to present their ideas and thoughts as well as their creativity in a very unique way. It is a trustworthy and reliable app that opens opportunities. Being a form of entertainment and for better interactions for everyone around the world. It’ll be a great choice as it will be a very distinctive approach to everybody.
