There is an extreme demand for AI and Machine Learning experts who are adequately qualified to do cutting edge exploration and designing. Simultaneously, the inventory of specific Machine Learning ability is scant – however the circumstance is progressively further developing because of the new Master’s and Ph.D. programs in ML that have been introduced all around the world over the last couple of years.

All things considered, to hire Machine Learning experts is a difficult errand for talent recruiters. It is not only due to the shortage of ML ability yet additionally because of an absence of significant experience among recruiting professionals.

Why Hire Machine Learning Experts?

Machine Learning is the most common way of empowering computers to perform tasks that have until as of late, been completed performed by humans.

Before the introduction of machine learning, computer systems and software’s just knew the data that a programmer releases. The outcome is a software system that can’t innovate, and that must be provided orders to function.

To hire Machine Learning engineers, it permits organizations to change huge data sets into statistical information and actionable knowledge. This important information can be integrated into everyday business processes and operational activities to react to changing business demands or business conditions. Beside automation repeatable tasks, organizations around the world use Machine Learning to assist with business operations and scalability.

As machines have a wider scope of data processing ability than humans. It is feasible for them to organize and scan data more rapidly than any individual can. It not only creates more valuable software as well as more effective software programming.

This is really relevant to a recruiting manager without a strong technical background. It’s their job to choose if a candidate has the right Machine Learning abilities that are important to be successful. So, let’s dive somewhat more profound into machine learning experts and the most ideal approaches to hire machine learning experts.

Required Skills To Hire Machine Learning Engineers

1. Specialized Skills Required

The main expertise needed for such engineers is that they should have full-fledged specialized information on coding/programming to deal with such ventures. Here you need to check the specialized capacity of the applicant and do the coding test to ensure he/she is qualified to manage ML model development. To assess their specialized skills, additionally guarantee you have the experienced candidate to hire the right machine learning engineer.

2. Interactive Communication Skills

Another significant factor that should be thought of to hire Machine Learning experts is communication skills that are imperative to communicate with their colleagues. The ML engineer should agree on a communication framework with an organization prior to starting the project. In case you are more stressed over the communication boundaries you need to check this ability to enable the smooth communication process.

3. Creativity with Cognitive State of Mind

A ML expert’s mind ought to be adequately creative enough to comprehend your imagination towards a dream AI project. You need to make different inquiries about how much they are interested or enthusiastic about working on such projects. Their creative minds can just foster a unique model that can give an edge to your business. Whereas, simultaneously gives a chance to them to use their creative thinking in developing the more creative applications for your business.

4. Easily Adaptive to New Technologies

Technology is one the most advanced fields where new developments occur globally. Likewise, ML is also a continually developing field taken on by the new technologies everyday. Thus, while aiming to hire machine learning engineers should have the ability to adapt to such new technologies. He should always be updated with new evolving technologies and keep adjusting to the industry trends and execute it with the right solution in today’s environment.

5. Work with Detailed Deliverable Goals

To achieve the AI or ML projects, engineers need to divide the assignments into more modest deliverable objectives. A detailed plan outline for getting the specific steps that assists with building a significant model. Introducing detailed and deliverable objectives are particularly crucial to make the accurate decision and evaluate the project for best performance.

How can an IT Recruiter Hire Machine Learning Expert?

The execution of Machine Learning basically implies a system is presently not restricted by the software engineers’ humanly vision. Presently, a machine can learn its own strategies through new and inventive cycles that developers or analysts might not have even thought about.

However, it is exceptionally helpful in light of the fact that it permits developers to design software with a particular objective, without focusing on the whole process of how it does as such.

Discovering approaches of computer programming to interpret such immense amounts of data has become challenging for even the best engineers to execute. The ML takes into consideration the formation of methodologies beyond human foresight and planning.

Resources/Tools/Technologies for Machine Learning

There are a ton of tools available for Machine Learning experts and are accessible in the Python language, while R is less common. Some Deep Learning systems are accessible in C++ or Java, because it is quicker and more memory-effective than Python. In Python, the most famous libraries incorporate Pandas, Scikit-learn, TensorFlow, and PyTorch.

Skills and Techniques A Machine Learning Engineer Familiar With

An effective AI specialist ought to have extraordinary mathematical skills. Likewise, they should be specialists in both programming and statistics. Thus, they can utilize their problem solving abilities to have a depth of information on Machine Learning models. Without a doubt, the machine learning engineers prefer Python over any other language.

Hire Right Machine Learning Expert for a Successful Project

You can’t recruit substandard developers and anticipate top quality results. You likewise can’t recruit the ideal individuals without redesigning the workflow. It is crucial to create harmony between Machine Learning experts and the workflow of machine learning programs they are working with.

A great deal of energy is spent to hire machine learning engineers for the project. While they are significant, they can’t work without the right analysts, engineers, or strategists. The target of these job roles are not clearly visible with respect to the data scientists.

Thus, before you enter any ML project, assembling the right talent is fundamental for guaranteeing the success of your venture. Machine learning experts administrations are majorly in demand as enterprises look to adapt it for improving their growth.

EngineerBabu provides high-end dedicated Machine Learning solution providers to assist your organization or projects with digital alteration. For any query or guidance, contact us and hire machine learning expert. We are here to help you in scaling your business venture in this competitive market with a guided direction.

The post Tips To Hire Machine Learning Expert appeared first on EngineerBabu Blog.

With integration of Machine Learning into mobile applications, the organizations can likewise further improve their business sales by making futuristic and intelligent decisions. Since the innovation renders countless advantages to businesses, the demand for Machine Learning integration into existing applications and making ML-supported applications is increasing at an extraordinary rate.

The ML engineers build applications that support machines to learn from their past information or data, examples, and patterns without any precise programming structure. Thus, it isn’t simply learning. Machine Learning is about listening, understanding, and learning by using human efforts to the machine. What’s more, to develop these systems, know the top attributes of a Machine Learning Engineer.

So, let’s learn in detail about what is Artificial Intelligence, Machine Learning, and how to hire a Machine Learning Engineer?

What is Artificial Intelligence (AI)?

Artificial Intelligence is a collection of various technologies working together to empower machines to sense, understand, act, and learn with a human-like level of intelligence. Possibly that is the reason it appears to be like everyone’s meaning of AI is different. However, Artificial Intelligence isn’t only a certain thing.

Technologies, such as ML and NLP (Natural Language Processing) are all parts of AI technology. Everyone is advancing along its own way and, when applied with data, automation, and analytics, can assist businesses to accomplish their objectives, whether it is developing customer care or enhancing the supply chain.

The AI algorithms are intended to make decisions, regularly using real-time data. They are like inactive machines that are only appropriate for mechanical or pre-determined reactions. Using sensors, computerized data, or remote inputs, the information is combined from a wide range of sources, analyzes the material in a split second, and acts over the derived output from those data or information. It is deliberately designed by humans and arrives at a conclusion depending on their instant analysis.

What is Machine Learning (ML)?

Machine Learning is a subsidiary or we say, an application of AI that gives systems the ability to learn automatically and improve with experience without being specifically programmed. ML focuses on computer program development that can access information and use it to learn on their own. 

The most common way of learning starts with data observation, like direct insight, or guidance, to look for some patterns in data and make better and futuristic decisions. The major perspective is to allow the computer system to learn automatically i.e., without human assistance or guidance and define activities accordingly.

It is important to hire a Machine Learning engineer because it gives businesses a perspective or pattern on customer behavior and business operations, in addition to that supports new products development. A lot of leading organizations, like Uber, Facebook, and Google make ML a focal point of their operations. Thus, Machine Learning has turned into a huge competitive differentiator for many organizations.

How to Hire A Machine Learning Engineer?

If the company is spending on Artificial Intelligence and Machine Learning development, then it is recommended to be very careful when you hire a Machine Learning Engineer or AI expert, who can be a great asset for your company. Hence, there are few important points to consider when you aim to hire a Machine Learning engineer or Machine Learning experts. Below we have mentioned in detail what you need to look at when you hire a Machine Learning engineer.

1. Technical Skills

The main expertise needed for such engineers is that s/he should have full-fledged technical expertise coding/programming to chip away at such tasks. Here you need to ensure the technical specialization of the applicant and do the coding test to verify s/he is qualified to manage AI and ML model development. To assess their technical abilities, likewise ensure you have the accomplished experts to recruit the right candidate.

There are various programming languages and frameworks that you need to test the expertise of to hire a machine learning engineer. These are as follows;

a. Python

The programming language expertise is fundamental to perform ML algorithms. You need to ensure that the candidate is aware of all the accessible features in the language. 

b. Pytorch/TensorFlow

There are two deep learning frameworks that overwhelm the ML market. TensorFlow is simpler to use for deployment while Pytorch is appropriate for experimentation. Ensure that the candidate must possess hands-on experience of both.

c. Scikit-learn

This is the most exemplary ML algorithm. Mastery in Scikit will help in solving many small-data problems.

d. NumPy and Pandas

While working with data, select precisely in what data you are interested. Hire a Machine Learning engineer who uses NumPy for basic functionality and Pandas for complex.

e. Apache Spark

Apache Spark enormously speeds-up the development efforts. Machine Language developers should be familiar with Apache Spark.

2. Interactive Communication Skills

Another significant factor should be considered while aiming to hire a Machine Learning Engineer is their communication abilities, which is essential to coordinate with their colleagues. AI and Machine Learning experts should agree on following a communication framework with the organization before starting the project. Hence, in case you are worried about the communication skills, you need to check the ability in the candidate to ensure a smooth communication process.

3. Curious, Creative, and Adaptable with New Technologies

The venture for which you are looking to hire a Machine Learning Engineer and an AI expert should spark their creative mind and ignite their imagination. They ought to fire a number of questions and you should feel motivated by their passion for the project and quality deliverables. AI and Machine Learning expert have to make their path and dedicated effort to understand why they are developing or improving something. Thus, they can put forward recommendations that are significantly valuable for the project.

After being curious and creative, a machine learning expert should have to be adaptable to new technologies. As evidently, change is the only constant in life, and that has never been more genuine than today in innovation. Artificial Intelligence and Machine Learning are continuously growing fields with new technologies and sectors getting introduced each day. Your AI and Machine Learning experts should regularly update with shifts in technology and adapt to industries evolving patterns to execute apt solutions that work in the current situation.

4. Set Detailed and Deliverable Goals

To complete the AI or ML projects, engineers need to divide the tasks into more modest deliverable goals. A detail plan is presented for getting the specific steps which support in building a significant model. Laying out detailed and deliverable goals are crucial to make the right choice. Yet, evaluate the project working for its best performance.

Cost to Hire a Machine Learning Engineer

To calculate how much does it costs to hire a machine learning engineer, here is a useful guide to note:

a. Plan and Analysis

At this stage, you need to unmistakably state what software you need to make, what issues it will resolve, and what benefits it will offer to clients. During this phase, you can plan all the required development stage. Furthermore, compute the estimated cost for software development and deployment.

b. Prototype Development & Evaluation

Regularly, these costs about $25,000 for complex project ventures. In any case, it essentially relies upon the complexity of the software to build. Therefore, these charges may vary i.e., less or more. 

c. Minimum Viable Product (MVP)

MVP is an original product that contains the least number of features and functions for the planned functionality. Then, the MVP is handed to a group of users who test it and give their feedback to build the improved version of the final outcome. Thus, you can get accurate feedback during the development phase and can remove/improve/modify something to address the genuine needs of clients. MVP development cost begins from $25,000 to $100,000 and even more sometimes.

d. Product Launch

Generally, we calculate the last stage expense in the start. Thus, it almost present the required definite expenses for planning and deploying the final product. However, to prepare that the prototype and MVP development can bring about greater numbers.

Overall, the minimum development cost of AI-based software developed by top artificial intelligence companies ranges from $5,000 to $100,000. The expense relies upon the project complexity, industry, development time, applicability, and numerous other factors. Discussing what amount does it cost to develop an AI or machine learning software for each driving industry. Below is the following result;

Computer Software: 50,000+; Financial Services: 30,000+; Information Technology & Services: 25,000+; Healthcare: 25,000+; Education: 15,000+; Research & Development: 10,000+; Web & Mobile Applications: 10,000+.

Wrapping Up

Hiring a right AI or Machine Learning expert would be a troublesome and time taking process as well. It includes basic stages of vacancy detail with an exact set of responsibilities, to avoid the confusion between data analyst, data scientist, and machine learning engineer. Thus, it requires thorough understanding of technology and expertise to hire a machine learning engineer who is expert in his task. So, to get a reliable and expert engineer, you can hire a Machine Learning engineer from EngineerBabu and for any other query or information you can contact us. We are an expert team of developers and engineers that are excellent in their deliverables and client satisfaction.

The post How to Hire A Machine Learning Engineer? appeared first on EngineerBabu Blog.

One can expect to see more, not less of AI in the coming year. More jobs would be created rather than snatched away by AI; at least this is what tech giants are believing.
Amazon’s CEO, Jeff Bezos in a statement on the future of AI said, “I predict that, because of artificial intelligence and its ability to automate certain tasks that in the past were impossible to automate, not only will we have a much wealthier civilization, but the quality of work will go up very significantly and a higher fraction of people will have callings and careers relative to today.”

How will AI develop in 2019?

Tech biggies like Alibaba, Google, and Microsoft, have come up with ambitious plans to ‘democratize‘ AI.
Google seems to be spearheading this revolution through its offering, Tensorflow. Tensorflow is helping small businesses and developers innovate through its open-source machine learning framework.
Another AI project by Google, Jigsaw leverages artificial intelligence to detect toxic comments and hate speech which has proven to be quite successful. This tool assigns a toxicity score to a piece of text based on numerous parameters.
In a bid to expand their horizons, Google is targeting emerging economies like India to expand its offerings. Indian policymakers and government officials are being trained in AI tools to streamline governance. NITI Aayog, a policy think-tank of the government of India, is partnering with Google to work on an array of initiatives to help build an Artificial Intelligence (AI) ecosystem across the country.
Amitabh Kant, Chief Executive Officer of NITI Aayog, said in a statement: “NITI’s partnership with Google will unlock massive training initiatives, support start-ups and encourage AI research through PhD scholarships, all of which contribute to the larger idea of a technologically-empowered New India,”
Through their collaboration, the organizations are planning to conduct hands-on training programmes to sensitize and educate lawmakers and technical experts in government about relevant AI tools and how they can be used to streamline governance.

On the same lines, Chinese tech behemoth Alibaba aims to bring sweeping and disruptive changes to China’s vibrant business landscape.
The machine intelligence technology division leads Alibaba’s efforts into AI. The division is working in areas including computer vision, speech recognition, optimization, and natural language processing.
One of the projects underway comes in the form of AliMe, an AI-enabled chatbot Alibaba developed that recognizes what people say both in terms of the text and the speech.
Not limiting the use cases of AI to just e-commerce and retail, Alibaba has ventured AI in farming and agriculture. On this front, the company is coming up with initiatives that can carry out a variety of things including, tracking animal IDs, detecting nutrition management, etc.
“Alibaba has changed the everyday life of the Chinese in China. Looking forward, our visionary leader, Jack Ma, wants us to be able to reach two billion consumers and to help 10 million businesses around the world. That’s a huge call, but we already have half of the platforms in place.” said Alibaba’s chief scientist Xiaofeng Ren, in a statement to CeBIT.
Meanwhile, Microsoft too is getting on the AI bandwagon. It’s CEO Satya Nadella in a bid to demonstrate that Microsoft can develop state-of-art services and still be a trusted provider for preserving user’s data has come up with a plan to expand the offerings of its digital assistant Cortana. Microsoft is targeting the office space to introduce a workplace-specific service to integrate AI into more of its enterprise services.

What will become increasingly important?

At the helm of all these AI developments that planned is, ‘Data.’ According to an industry insider, the benefits of AI can only be reaped if the data being used is fit for purpose.
Major AI companies are leading in this domain solely because of the fact that they have a lot of data for the algorithm to be trained on. For these techniques, to prove fruitful, it is extremely critical to have smart and relevant data. The performance of algorithms is more dependent on quantity rather than the quality of the data. Unless you can feed all the knowledge manually, it is vital to have an abundance of data for the algorithm to be trained on.
Artificial Intelligence rests upon the idea of mimicking how humans learn. All of our life experiences act as a set of data for us to learn. Thus a successful AI needs to mirror human behaviour to get successful.
Let us change the frame of reference and drill down to some of the significant developments that AI is definitely going to witness in 2019.

1. More jobs will be created by AI than lost to it

A utopian workless future will still be a distant fantasy in the coming year. It is still far from reality that the rise of machines will take over our jobs and cause a social strife all over the world.According to a Gartner report, as many as 1.8 million jobs would be lost to automation, especially in manufacturing, but also, 2.3 million would be up for grabs!
Let us consider a scenario to understand the situation better – Consider a garment factory, imagine a machine operator in a factory. Although a portion of his/her job could get automated, they will definitely have other roles, for instance, managing inventory and overseeing junior workers, which computers cannot supervise. Also consider the difference between a worker in a US garment factory and its counterpart in Vietnam: the American unit is more likely to be technologically forward, and a typical worker’s day will likely include a higher number of non-routine tasks that can’t be automated.
Undoubtedly, it is fairly accurate to say that non-manual jobs will be lost to AI, but more and more will be created as well! Repetitive tasks wouldn’t need to be performed by humans, and we will get more time to utilize our creative thinking and exemplary abilities to perform tangible tasks.
Industries such as education, IT services, public sector, especially healthcare will see a drastic infusion of new job opportunities pertaining to AI.
Although when it comes to doctors and lawyer, AI service providers have made a concerted effort to present their technology as something which can work alongside human professionals, assisting them with repetitive tasks while leaving the “final call” to them.

2. AI assistants will become a fixture in enterprises

AI is so intertwined in our everyday lives that we don’t even realize it, from a simple Google search to shopping at Amazon, or watching Netflix – AI is at work to provide us with the best-personalized experience.
With significant product releases from the likes of Apple, Samsung, Google, Amazon, and several other vendors – It is evident that people are embracing the comfortable and conversational modes of interaction.
2018 was the year of consumer voice assistant, 2019 will be the year where enterprises will see an influx of voice assistants in their day-to-day operations. Already, enterprises have begun realizing the importance of conversational technologies and are leveraging them as an extension of their businesses to support a wide range of tasks.
NLP (Natural Language Processing) and Machine Learning AI assistants will become increasingly efficient, thanks to their exposure to more and more information about how we communicate. By the end of 2019, these assistants will become so sophisticated that they would be able to anticipate our behavior, read our facial expressions, and even understand our habits.

3. AI will venture beyond tech companies

Many AI luminaries believe that AI has a much broader scope outside the tech domain. Citing use cases from a recent McKinsey report which found that AI will generate revenue of more than $ 10 trillion in GDP by 2030.
According to Andrew Ng, co-founder of Google Brain, “I think a lot of the stories to be told next year (2019) would be in AI applications outside of the software industry. As an industry, we’ve done a quite decent job of helping companies like Google and Baidu, but also Facebook and Microsoft — which I have nothing to do with — but even companies like Square and Airbnb, Pinterest, are starting to use some AI capabilities. I think the next massive wave of value creation will be when you can get a manufacturing company or agriculture devices company or a health care company to develop dozens of AI solutions to help their businesses.”
Many companies are recognizing the importance of catching up to AI technology, lest they be left behind. Here are 11 industries that are experiencing disruption.
     • Agriculture
     • Retail
     • Manufacturing
     • Law
     • Automotive
Although the domain that would reap the maximum benefits will definitely be healthcare. The following could be significant developments in healthcare:
     • Managing Medical Records and Other Data
     • Doing Repetitive Jobs
     • Virtual Nurses
     • Precision Medicine
     • Health Monitoring
     • Healthcare System Analysis
     • Drug Creation

4. SMEs will see a significant transformation in 2019

The small and medium-sized businesses are bound to see a major overhaul in 2019. This is precisely due to the recent commitments by tech giants – Organizations like Google, Amazon, Facebook, etc. are offering open-source frameworks that could easily be integrated with the current business processes.
In 2019, SMEs would no longer need to break the bank to incorporate AI into their operations. By leveraging existing platforms like Tensorflow (By Google), Keras, SparkMLlib, Caffe, etc., SMEs could save considerable cost and time which would have otherwise been required in developing and designing the products in-house.
Apart from this 2019 would also witness an influx of new AI-powered analytics tools. These tools are beneficial for businesses who don’t want to invest heavily in Artificial Intelligence and are still looking to reap the benefits of it. Analytics tools offer a gateway for small businesses to leverage the potential of AI even if they do not possess a vast amount of data.

Must Read: How Can AI Benefit Small Businesses

5. AI skills will become increasingly in demand

Organizations have been facing an impertinent issue of lack of technical skills required for developing to-the-mark AI products.

AI-related skills, as well as in related areas such as machine learning are in short supply today. Due to the relative newness of this technology, most educational institutions haven’t yet introduced a dedicated curriculum in their syllabus for AI and deep learning. This has created a crevice of sorts in the demand and supply of skilled AI professionals who are good with these skills.

As the democratization of AI is on the lines – It has to become viable for both, the tech giants and small and medium-sized businesses to recruit AI professionals easily.
Moreover, AI professionals require extensive on-the-job training, and as of now, there aren’t enough experienced AI professionals to take over leadership roles required by organizations who are just beginning to introduce AI in their services.
2019 would see tech giants such as Amazon and Google invest globally in expanding their talent pool. The Google Brain facility in Toronto is exclusively dedicated to research in AI; also Amazon has established an AI-focused lab in England and plans to build a similar facility in Spain.

Wrapping Up

The application and use case of Artificial Intelligence is beginning to encompass more and more businesses and is becoming a critical aspect of our everyday lives. As this article suggests, the use of machine learning is not just limited to the more conventional industries. Its use goes far beyond what one can imagine – Even bloggers nowadays are utilizing machine learning algorithms to engage users and improve their conversion rates.
Many companies already see the competitive edge AI-powered solutions can give and we can easily anticipate now more than ever the unusual uses of artificial intelligence in the future.
But the elephant in the room certainly needs to be addressed – The advancement and the evolution of AI comes with abundant risks. Due to its relative infancy, no proper regulations have been put in place to ensure that this gold mine doesn’t get exploited by notorious elements.

We urgently need to account for ethics and issues of bias during the development of these AI-based products, whereas today organizations easily overlook these things.

Considering how gruesome it is in finding the perfect tech partners for developing an efficient AI product. EngineerBabu houses a talented bunch of AI professionals who have proven their mettle by creating hundreds of successful AI products based on Machine Learning and Deep Learning technologies.

Contact Us for a free consultation call, we would love to guide you. In the meanwhile, check out some of our amazing AI products, right here.

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The post Artificial Intelligence in 2020 | AI Development appeared first on EngineerBabu Blog.

Hey Siri! Can you help my business?

Quite recently I got into a discussion with my marketing manager, a tall and burly mid-age man;
Let’s call him Steve. 
Steve and I got into a discussion on the subject of how AI in general, is transforming the way business is conducted.
This man, a well-educated gentleman, only partially agreed with my perspective,
He added,
“I acknowledge technology has made quite an impact, but this impact is austerely restricted. My domain of work, or for that matter most small businesses haven’t observed any major transformation even after the recent developments in AI,”He continued,
“It is predominantly large organizations that have profited from new-age technologies like AI and Machine learning.”
This got me wondering…

• Is the AI use-case still confined to big & fancy corporations?  
• Has it not facilitated small and medium-sized enterprises at all?

My inquisitiveness made me go scouting around, inquiring small and medium-sized business owners, asking them this question.
Surprisingly, almost everyone had the same outlook! After a lot of scavenging, this is what I learned.

How Can Small Businesses Leverage AI Now?

AI and ML applications are almost everywhere, from ride-hailing services to voice search on smartphones, we use AI services every single day – It feels so accessible to us consumers that it must be within reach of small business too!
It is expected that AI will be a game-changer for all industries by providing smart applications and efficiency in the business processes through optimal utilization of organizations data.
Huge corporations have inculcated AI into their systems and are quite satisfied with the improvements, but SMEs are still not sure about potential use cases of AI for them, because they question if it can really bring a change in their business processes.

Artificial Intelligence isn’t just for companies with billion-dollar budgets.

In fact, small business communities are competing and how! Many innovative SMEs are significantly cutting costs and delivering excellent customer experiences with AI-powered applications.

If applied optimally, AI tools can transform the MSME space enabling small business owners, and entrepreneurs create hugely successful operations across a spectrum of business functions including, product development, sales, customer service, and marketing.
Based on my extensive research, it became rather evident that most SMEs are struggling because of their belief that AI requires considerable investment.
Fortunately, for businesses on a budget, one doesn’t need to break the bank to start incorporating machine learning and AI into their operations. By launching on a smaller scale and leveraging ready-made solutions, one can efficiently harness the capabilities of AI and improve performance across different departments considerably.

Here are 4 best approaches to employ AI in SMEs right away:

1. Leveraging existing platforms

Developing your own AI is undoubtedly a complex and time-consuming process and requires a considerable amount of investment. Fortunately, several tech companies have open-sourced their AI efforts in a bid to make AI reach more and more consumers. Leveraging this platform for your business saves you considerable cost and time, required in developing and designing. Here’s a compiled list of some of the most popular AI platforms that your business could leverage:

• Tensorflow (by Google)

Released in 2015, TensorFlow is one of the most well-maintained and extensively used frameworks for machine learning.
Developed to fuel its research and production objectives, TF is now widely used by numerous companies, including Dropbox, eBay, Uber, Twitter, and Intel.
Tensorflow’s extensive use case is attributed to its availability in almost all the major programming languages, like C++, Go, Haskell, Java, Python, Rust and most recently, Javascript. One can also find third-party packages for other programming languages.

• Keras

Keras is notorious for its user-friendliness, modularity, and ease of extensibility. The standout feature that makes Keras unique is that it could be applied as a bolt-on and stand-alone software as well. Keras was designed to simplify the creation of deep learning models.
Being written in Python, it can be deployed on top of other AI technologies such as Tensorflow, CNTK, and Theano.
The platform provides a comprehensive machine learning library that allows for,

• Easy and fast prototyping,
• Runs optimally on CPUs and GPUs
• Supports both recurrent and convolutional networks.

• Spark MLlibDeveloped by Apache, Spark MLlib is a machine-learning library that supports Java, Python, Scala and even R. Spark MLlib is specially designed for processing large amounts of data and could quickly be deployed across various SMEs like manufacturing, finance, healthcare and many more. The tool also provides interoperability with NumPy in Python and R libraries.

• Caffe

Released in 2017, Convolutional Architecture for Fast Feature Embedding often abbreviated as Caffe, is a machine learning framework that primarily focuses on expressiveness, speed, and modularity. This open source platform is written in C++, and also comes embedded with a Python interface.
Features:

• • Expressive Architecture
  • Extensive Code
  • Fast Performance
  • Active and Vibrant Community
    Caffe could serve as an ideal platform for your business as it is easily deployable and its active community provides on-the-go solutions.

• fastText (by Facebook)Created by Facebook’s AI Research (FAIR) lab provide word embeddings and text classifications. It is available across all major platforms like Linux, MacOS and even Microsoft Windows.
Written in C++ and Python, fastText allows users to create unsupervised or supervised learning algorithms for obtaining vector representations for words.

2. Start Small

AI enablement can’t be achieved overnight. Trying and rushing into the process is definitely a detrimental approach. Going all-in and investing massively is not recommended for small and medium scale businesses. SMEs need to start slow and increase their AI efforts gradually over time.
Below is a structured approach as to how to proceed:

• Start by integrating first-party apps that facilitates productivity of the already employed workforce.
• Once ready, steer your efforts towards open-sourced AI, cloud systems and flexible workflow models.
• Start with a small problem with a high chance of demonstrating a positive return on investment (ROI).
• There are three steps
  • Define: Define crystal clear expectations of what AI can and cannot do for your specific business profile.
  • Measure: Measuring should be done against meaningful baselines.
  • Decide: Decide whether the experiment worked or not, and fail fast.

Thus, it is critical to tackling a small problem which has a high return on investment, especially if you are a small business. Adhering to this approach proves fruitful when you are struggling with the amount of money you are willing to invest in AI.

If you are on the lookout for creating an AI product and are looking for a free consultation, click here. 

3. Implement AI for analysis

The most crucial and essential use case of AI for small or even humongous business is in analytics. AI has opened new horizons in the domain of analytics; by leveraging predictive analysis, one can scale up their enterprise in a tremendous way.
If you are trying to implement AI for your business without wanting to invest heavily in machine learning, then analytics is the way to go.
AI-powered analytics software will get you business intelligence at a throwaway price.
Here is a compiled list of some of the best analytics softwares that your business can leverage:

• Amazon Machine Learning

AML is a machine learning service that provides tools and wizards for creating machine learning models. With the help of easy-to-use analytics, Amazon aims to make machine learning more accessible than ever.
AML can be connected to the data stored in RDS, Redshift or Amazon S3.

• H20

H20 has carved out a niche for itself AI and machine learning arena because its primary tools are free and open source.
You can get the main H20 platform simply by downloading them.
H20 operates under the Apache 2.0 license, one of the most flexible open source licenses available. Besides, you can also run them on clusters powered by AWS for just a few hundred bucks.

• Microsoft Onboard

In the verbatim of its CEO, Satya Nadella, “Our goal is to democratize AI to empower every person and every organization to achieve more.”
Keeping that in mind, Microsoft has developed an open-source software framework that competes with the likes of Caffe and Tensorflow. The solution enables enterprises to scale efficiently across multiple machines on large datasets.

4. Deploy Chatbots

Chatbots are an excellent place to receive an instant return on your investments from AI. Their capability to interact and engage with customers, answer basic & even complex questions, and route inquiries aptly allow businesses with limited staff to appear much more prominent.
AI can leverage its lightning-fast data processing capabilities to analyze all available information of any customer and automate responses based on that, thus reducing the burden on the staff in charge.
It also improves customer service and retention – providing 24/7 call center services, which otherwise requires a considerable investment.

Benefits of AI in Small Businesses

AI can drive significant breakthroughs for SMEs across many departments. Though AI is still evolving, we are starting to believe that it will change the way businesses are managed through the level of technology it provides. Many organizations have begun implementing AI into their business workflow and are seeing a considerable improvement in ROI and business turnover.
AI can be useful for small companies as well, by making correct use of the data they generate. Business owners should start thinking about the steps which can allow them to implement AI no matter the size of their business successfully.

Below are some of the points which can help small business owners to adopt AI:

1. Highly efficient customer service

Customer nurturing is an essential process for any business. Support is vital to retain a customer, and sometimes support agents fail to deliver unique and appropriate solutions to their customers. Through AI enabled support services, this anomaly could be readily addressed. Besides, the risk of human error is wholly eradicated.

2. Important insights into the competitor’s business process

Studying the competition thoroughly is crucial for understanding market trends and staying competitive. AI-based analytics softwares provides much more significant and relevant insights into the business process of your competitors.
For instance, AI-powered competitive analysis tool, Crayon can help track your competitor’s activities across different channels like websites, social media, and web applications. This functionality enables small businesses to get a better understanding of ongoing changes in the competitor’s strategy.

3. Off the rack solutions

Small enterprises can deploy off the shelf tools into literally every element of their business workflow involving data. These solutions provide not only better insights into the process, but also suggest better solutions for the problem. For instance, AI enabled tools like Monkey Learn uses technologies like entity extraction and sentiment analysis to derive a better understanding of the business. Also, it doesn’t require the user to code and is extremely easy to integrate. Another interesting tool is the Enterprise Immune System by Darktrace. This AI leveraging security application company builds their own understanding of what normal human behavior looks like within an enterprise and detects emerging threats in real time.
This tool requires no special skills to install and is also self-learning.

4. Transform your marketing process

AI is fundamentally changing marketing from the roots. AI-based advertising platforms have already been deployed by huge companies like Facebook and Google to target specific customer-set who are receptive to their message. This is just as true for small businesses; by deploying programmatic advertising, SMEs can easily reach a considerably large audience online, which was not possible earlier. It is strongly recommended for small businesses to start leveraging and integrating AI now if they want to reach potential consumers in the future.

5. Cost and Time benefits

The utilization of deep learning by SMEs can end up saving a lot of money spent on employing extra employees or outsourcing for specified projects. When repetitive or time-consuming work is done quickly and efficiently at the push of a button then employees are freed up to do creative work that will help the company grow.

Wrapping Up

“Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.”

—Ginni Rometty, CEO IBM

AI and Machine Learning use-case is not just restricted to large enterprises now. They are an excellent way to achieve tremendous growth for SMEs as well. They allow small businesses to effectively delegate conventional tasks to be done by innovative yet fast approach, making employees free to focus on other resourceful and creative opportunities that can help companies expand.
However, to achieve success with AI, small businesses need to strategize and align these applications with their respective business goals. The AI journey can be prolific for SMEs if they properly understand the significance of this emerging phenomenon and accordingly prioritize it to achieve long-term and scalable success.
To a relatively greater extent, the scenario concerning AI and businesses is very similar. Just like the 80s, when there were exceptions and some businesses considered investment in computers for the future of their industry, SMEs who visualize value through such investment and are at the same time are able to afford it, should move ahead and evaluate their investment decision based on their business objective.
With an extensive portfolio of developing hundreds of successful AI-based platforms for our cherished customers. We truly imbibe all the skills and expertise that is required for designing a great product.
Check out our portfolio 
And feel free to reach out to us for any consultation whatsoever, we will be extremely delighted to assist you. 

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