Category Archives: Artificial Intelligence / Machine Learning

Artificial Intelligence problems-CloudMoyo

Top 10 Artificial Intelligence problems you must know

Artificial Intelligence (AI) is the toast of every technology driven company. Integration of AI gives a business a massive amount of transformation opportunities to leverage the value chain. Adopting and integrating AI technologies is a roller-coaster ride no matter how business-friendly it may sound. A Deloitte report says, around 94% of the enterprises face potential Artificial Intelligence problems while implementing it.

As an AI technology consumer and developer, we must know about both the merits and the challenges associated with the adoption of AI. Knowing these nitty-gritty of any technology, helps the user/developer to mitigate the risks linked to the technology as well as take the full advantage of it.

It is very important to know how a developer should address/tackle the AI problems in the real world. AI technologies must be accepted as a friend not as a foe.

Read this article to know what are the top 10 potential Artificial Intelligence problems that need to be addressed.

1. Lack of technical knowledge

To integrate, deploy and implement AI applications in the enterprise, the organization must have the knowledge of the current AI advancement and technologies as well as its shortcomings. The lack of technical know-how is hindering the adoption of this niche domain in most of the organization. Only 6% enterprises, currently, having a smooth ride adopting AI technologies. Enterprise requires a specialist to identify the roadblocks in the deployment process. Skilled human resources would also help the teamwork with Return on in tracking of adopting AI/ML solutions.

2. The price factor

Small and mid-sized organization struggles a lot when it comes to adopting AI technologies as it is a costly affair. Even big firms like Facebook, Apple, Microsoft, Google, Amazon (FAMGA) allocate a separate budget for adopting and implementing AI technologies.

3. Data acquisition and storage

One of the biggest Artificial Intelligence problems is data acquisition and storage. Business AI systems depend on sensor data as its input. For validation of AI, a mountain of sensor data is collected. Irrelevant and noisy datasets may cause obstruction as they are hard to store and analyze.

AI works best when it has a good amount of quality data available to it. The algorithm becomes strong and performs well as the relevant data grows. The AI system fails badly when enough quality data isn’t fed into it.

With small input variations in data quality having such profound results on outcomes and predictions, there’s a real need to ensure greater stability and accuracy in Artificial Intelligence. Furthermore, in some industries, such as industrial applications, sufficient data might not be available, limiting AI adoption.

Also Read: 5 Myths about the ‘Data Quality’ that could derail your analytics project

4. Rare and expensive workforce

As mentioned above, adoption and deployment of AI technologies require specialists like data scientists, data engineer and other SMEs (Subject Matter Experts). These experts are expensive and rare in the current marketplace. Small and medium-sized enterprises fall short of their tight budget to bring in the manpower according to the requirement of the project.

5. Issue of responsibility

The implementation of AI application comes with great responsibility. Any specific individual must bear the burden of any sort of hardware malfunctions. Earlier, it was relatively easy to determine whether an incident was the result of the actions of a user, developer or manufacturer.

6. Ethical challenges

One of the major AI problems that are yet be tackled are the ethics and morality. The way how the developers are technically grooming the AI bots to perfection where it can flawlessly imitate human conversations, making it increasingly tough to spot a difference between a machine and a real customer service rep.

Artificial intelligence algorithm predicts based on the training given to it. The algorithm will label things as per the assumption of data it is trained on. Hence, it will simply ignore the correctness of data, for example- if the algorithm is trained on data that reflects racism or sexism, the result of prediction will mirror back it instead of correcting it automatically. There are some current algorithms that have mislabeled black people as ‘gorillas’. Therefore, we need to make sure that the algorithms are fair, especially when it is used by private and corporate individuals.

7. Lack of computation speed

AI, Machine learning and deep learning solutions require a high degree of computation speeds offered only by high-end processors. Larger infrastructure requirements and pricing associated with these processors has become a hindrance in their general adoption of the AI technology. In this scenario, cloud computing environment and multiple processors running in parallel offer a potent alternative to cater to these computational requirements. As the volume of data available for processing grows exponentially, the computation speed requirements will grow with it. It is imperative to develop next-gen computational infrastructure solutions.

8. Legal Challenges

An AI application with an erroneous algorithm and data governance can cause legal challenges for the company. This is yet again one of the biggest Artificial Intelligence problems that a developer faces in a real world. Flawed algorithm made with an inappropriate set of data can leave a colossal dent in an organization’s profit. An erroneous algorithm will always make incorrect and unfavorable predictions. Problems like data breach can be a consequence of weak & poor data governance–how? To an algorithm, a user’s PII (personal identifiable information) acts as a feed stock which may slip into the hands of hackers. Consequently, the organization will fall into the traps of legal challenges.

9. AI Myths & Expectation:

There’s a quite discrepancy between the actual potential of the AI system and the expectations of this generation. Media says, Artificial Intelligence, with its cognitive capabilities, will replace human’s jobs.

However, the IT industry has a challenge on their hands to address these lofty expectations by accurately conveying that AI is just a tool that can operate only with the indulgence of human brains. AI can definitely boost the outcome of something that will replace human roles like automation of routine or common work, optimizations of every industrial work, data-driven predictions, etc.

However, in most of the occasions (particularly in highly specialized roles), AI cannot substitute the caliber of the human brain and what it brings to the table.

Not everything you hear about AI is true. AI is often over-hyped. Read this article from Forbes to clear all your misconceptions about the AI technologies.

10. Difficulty of assessing vendors

In any emerging field, a tech procurement is quite challenging as AI is particularly vulnerable. Businesses face a lot of problems to know how exactly they can use AI effectively as many non-AI companies engage in AI washing, some organizations overstate.

It’s true that AI technology is a luxurious retreat because you cannot oversee the radical changes it brings in to the organization. However, to implement it an organization needs experts who are hard to find. For successful adoption, it needs a high-degree computation processing. Enterprises should concentrate on how they can responsibly mitigate these Artificial Intelligence problems rather than staying back and ignore this ground-breaking technology.

The key lies in minimizing the Artificial Intelligence problems and maximizing the benefits through the creation of an extensive technology adoption roadmap that understands the core capabilities of artificial intelligence.

CloudMoyo, a Microsoft Gold Partner, offers a comprehensive suite of advanced AI, machine learning, deep learning, neural networks, advanced analytics solutions to progressive enterprises who are looking to be ahead of the curve while adopting AI. CloudMoyo’s 10-day Artificial Intelligence workshop is catered to guide you through elaborated options of the existing cognitive APIs (face, speech, text etc.) and custom state-of-art AI solutions using the Microsoft Azure AI platform. Book a slot now to understand how AI and ML can help you to become more efficient, effective and customer oriented.

Artificial Intelligence-CloudMoyo

A complete guide on how to implement AI in your organization

Many C-level or IT decision-makers believe that the sheer volume of data sets the foundation of AI (Artificial Intelligence). Around 90% of the enterprises incorporate AI because it’s trendy. Many lack the required skillset and tools to use AI and mitigate complexities of the huge volume of data they have, unaware of the fact that AI can help them solve most of their business problems.

Why should you invest in AI?

Applications have evolved, and things have changed remarkably since the days of plain old reporting. Now-a-days, your applications can learn and understand where you could go, what you could do, who you could meet and even what you might like to eat. If you notice, all of this is predictive rather than reactive. This gives businesses a newer weapon to target their customers, improve processes and save costs. They can now understand customer behavior actively deliver personalized experiences rather than the traditional ‘one size fits all’ approach. In addition, applications can foresee relevant events ahead of time and aid decision makers to prepare for outcomes.

In short, AI strengthens customer experience, increases engagement, and builds strong targeted communication. It accelerates the decision-making process by helping in gaining competitive advantages. Instead of getting overwhelmed by the huge volume, variety and velocity of data, businesses can now use that data to realize the advantages of using artificial intelligence. Read on to know how to do it…

How to start with AI?

Ask these questions to yourself before gearing up for AI:

  • Are you done being overwhelmed by the mountains of business data and thinking of exploiting competitive advantages with it but don’t know how to do it?
  • Do you want to understand your customer better and increase the retention rate with innovative use of your business data?
  • Are you looking up for improving your customer behavior?
  • Want to explore more and identify many other/new sources of revenue?
  • So, step zero is to find and identify the key business problems and know your business priorities. Continue reading if any of the above-mentioned goals sound like you and that if you have enough business data to accomplish (any of) these goals.

Here is the complete guide to follow if you want to implement AI in your business:

  1. Collect and access appropriate data: Sounds basic? Well, it is one of the most important steps to implement advanced analytics. Simply begin with the place where your data lives.
  • Check the type of data that you’ve captured so far – structured or unstructured
  • Evaluate if there’s any governance in place
  • Identify how to find high quality data
  • Categorize each data (by adding metadata, tags etc.)
  • Start small. Don’t try to document each and everything. Just focus on collecting and accessing those data points that can make you solve your business priorities and issues.

Also read: 5 myths about your data quality that can derail an analytics project

  1. Formulate a hypothesis: You’ve successfully created a data inventory. Now, what’s next? –
  • Try to correlate your accumulated data with your business goals and challenges; Think how it will help to achieve your business objective
  • Organize the given data to manageable chunks
  • Map out your findings
  • Stick to your priorities and try to work with what you have got
  • Understand what data you’re allowed to stock up and use. Consider data ethics.
  1. Narrow things down: It’s time to focus on what matters to your business. Now, that you know what data is important and what will help you achieve your business goals, keep all your eyes on it—
  • Catalog it for future purpose
  • Don’t indulge yourself in analyzing everything at the initial stage itself; give it a time
  • Concentrate on the datasets that matter to you
  • Be 100% accurate to achieve success.
  1. Test your data: It’s high-time to create a prototype and test your accumulated datasets.
  • Ask as many questions you want to ask at this stage
  • Program the algorithms to find answer to the queries. Use relevant data
  • Look for the pattern and behavior
  • If you think you’re not capable enough, partner with someone who can bring fresh insights and experience
  • Demonstrate something tangible from your data-Its value and worth
  • Make the prototype speak
  • Document the usage and outcomes of the prototypes
  • Get more people involved like a data scientist, etc.

Also read: Unsure about prototyping a data project? Here are our tips to run a successful Proof of Concept

  1. Make it happen: It’s time to make your data speak in real-life business scenarios.
  • Integrate the prototype into their existing business process
  • Use your findings to enhance the existing process
  • Operationalize and standardize the data insights to share with the entire organization.
  1. Put your data to work: The final step is to make your data speak at real-time, real-life. Create value and readiness for AI in the long run. See if your data insights are now converting into valuable and actionable business insights.
  • Monitor the process and start from step One to sharpen your data
  • Identify other cases where you can apply data technology
  • Check if you’re all set to use various components of AI such as Bots, NLP, intelligent automation, predictive analytics
  • Know where to use your algorithms for better results
  • Take a human-centered approach to AI and add value to your organization.

Definitely, AI has limitless potential in transforming the way you do business. It will play a huge role in the growth and success of your business, but you may encounter some challenges while implementing AI. Check out some of those high-level pain points:

  • Lack of technical know-how
  • Noisy datasets
  • Expensive human resources
  • Weak computation speed

Nervous about applying artificial intelligence to your business as you think you’re not ready for this? Allow us to help you achieve this milestone. Take advantage of our 5 day data modernization assessment where we take you on a journey to explore how your data can yield marvelous results Contact us today.

Artificial Intelligence in Contract Management

Applying Artificial Intelligence to Contract Management

Contracts are difficult (or rather impossible) to sort. They are everywhere, distributed across many repositories, scattered across multiple locations. The inaccessibility of contracts makes the task of managing them cumbersome, leading to a risk of losing out important business opportunities that are buried in these resources.

The manual handling of these contracts become even more difficult when it comes to deal with the amendments, terminations and (the most important) renewals of the contracts. That’s how the need of digitizing contract management came into the picture with various contract lifecycle management (CLM) solutions coming up.


From contract management to contract intelligence

The next step was unlocking secrets from contracts. Combining artificial intelligence (AI) with the contract management system has revolutionized business opportunities by redefining the real potential of these contracts that were buried within millions of files and folders from years.

Artificial Intelligence can convert unstructured contract documents into structured enterprise data. Applying AI in contract management can help the enterprise in identifying business risks and opportunities; AI understands the contract language and the meaning of clauses, it turns the contract management from a simple document management repository to a live, strategy-making machine.


Applications of AI in contract management

Machine Learning and AI help identification & analysis of clauses and other data. It can let companies review contracts more quickly, organize large scale of contract data more easily, can help in contract negotiations, and increase the volume of contracts it is able to negotiate and execute.

Let’s see some applications of artificial intelligence in contract management.

  • Contract classification: Sort each contract by type based on the content of the clause. E.g. MSA, SOW, lease, Independent contractor agreement etc.
  • Clause classification: Scan through the document and understand the significance of each paragraph. Based on the content, it classifies the clauses
  • Mark out important part of the clause: Highlight the important information covered in the clause
  • Learn about the new clauses: If the program uses enough documents with any new clauses, AI will learn all about it and secure it its clause library enhance clause classification with time
  • Supervised learning and retraining: Reviewer can change the wrongly classified clauses and the code learns to recognize in future, based on what the reviewer programmed it for
  • Similar Documents classification: Identify and classify similar types of documents

Also Read: 6 Things to Check Before Contract Management Software Implementation 

How does it work?

To understand the intricacies of artificial intelligence in contracts, let’s discuss a couple of scenarios in depth –


Automatically organize, classify, and extract important piece of information

Automated contract abstraction and migration is another technology-enabled service that automates the entire process of abstracting information from all your contracts. AI algorithms powered by natural language processing (NLP) based machine learning perform abstraction and migration with unprecedented speed and accuracy and creates an index of all the key terms, provisions, and obligations. Thus, you can automatically extract important information from a contract such as names, organization and vendor information, the contract signature date, renewal dates and more — the hundreds or thousands of contracts you have can be auto-tagged with the right companies, right data, right deadlines and automatic renewal alerts can be set. This will help you transform your business completely.


With pattern recognition algorithms, AI can identify areas for improvement

With hundreds and thousands of legal documents being uploaded to a contract management system, it is very tedious to tag each clause in the documents manually for further processing. However, machine learning algorithms can help to identify name of the clause on the basis of its contents. Now, with this trained model, we can upload document in a contract management software and it auto tags documents. It goes wrong at some points. Therefore, we do consider that. If that algorithm predicts clause name wrongly, all you can do is give it a correct label for given text and it should adjust itself after a few iterations of the clause and it does not repeat the same mistake again.

Also Read: Top 3 Things to See in Your Contract Management Implementation Partner


Conclusion:

Artificial intelligence is becoming very big deal in business. Impact of such artificially intelligent systems on enterprises is huge when it comes to staying a step ahead of peers and enriching the way they serve their customers. And the underlying platform for AI is data and the cloud!

As a trusted Microsoft Partner, CloudMoyo understands AI, data and the cloud, and how to build integrated intelligence into applications using the most advanced cloud technologies. Wherever you are in your AI journey, we can help you modernize the way you do business.

Get started with Artificial Intelligence with a custom workshop for your team today!

AI, ML and Deep Learning-CloudMoyo

Difference between Artificial Intelligence, Machine Learning and Deep Learning

The tech world today is talking about three important terminologies: Artificial Intelligence, Machine Learning  and Deep Learning. These names often create confusions. Many think the three terms are one and the same when there are significant differences between them. They are often used interchangeably but that isn’t the case.

So, what exactly is the distinction between the three – Artificial Intelligence, Machine Learning and Deep Learning? To visualize the difference between them first try to picture the relationship between the three terms.

AI,ML and DP-CloudMoyo

 

 

 

 

 

 

 

 

 

Visualize them as 3 concentric circles where Deep Learning is a sub set of Machine Learning which in turn is a subset of Artificial Intelligence. Artificial Intelligence as the ‘idea’ popped up first, then comes Machine Learning that flourished later and finally Deep Learning- that came with extra spaces and as a breakthrough that can drive the AI boom.

Let’s dive in:

Artificial Intelligence (AI): AI is “Machine exhibiting Human Intelligence.” Artificial Intelligence or AI is the broad and advanced term for computer intelligence. The Merriam-Webster dictionary defines it as “a branch of computer science dealing with the simulation of intelligent behavior in computers “or “the capability of a machine to imitate intelligent human behavior

Artificial intelligence can be referred to anything pertaining to a computer program. For example- a computer program playing rummy or a game of chess, or Facebook recognizing picture of a friend before you manually tag them or voice recognition inventions like Google Home or Amazon Echo –  powerful speakers and home assistants which answer to human questions or commands.

If you go deeper, AI can be categorized into 3 broader terms- Narrow AI, Artificial General Intelligence (AGI) and Superintelligence AI. The Narrow AI is the technology that performs a task better than that of the humans themselves can. Image classification on Pinterest is one of the examples of Narrow AI technologies in practice. Don’t you think that these technologies, interestingly, exhibit some dimensions of human intelligence? If yes, then how?

The ‘how’ part takes us to the next concentric circle and the space of ‘Machine Learning’.

Machine Learning (ML): ML is “The construction of Algorithm that helps achieve Artificial Intelligence.”  Machine Learning is a subset of Artificial Intelligence. It is one of the most promising AI techniques that takes all the data, learns (makes algorithm) and predicts results. The whole premise of ML is that the system simply gets trained by itself using algorithms with large amount of data to perform tasks.

What is Not a Machine Learning? – A hand-coded software that works with specific instructions to perform a specific task.

A large set of data helps ML to outclass AI technologies of facial, object, image and speech recognition, etc… A Machine Learning system works or makes predictions based on patterns. Computer vision is till date, one of Machine Learning’s finest application areas. However, it requires hand-coded classifiers like edge detection to get the task done. It produces results which are good but not something that could beat human intelligence.

Want to understand Machine Learning better? Read our beginner’s guide to ML

Deep Learning: DL is “A subset of Machine Learning”. Deep Learning is the technique for implementing Machine Learning. It works with new and next level of accuracy for many important issues like recommender systems, sound recognitions, etc… It uses a set of algorithms inspired by the structure and function of the brain called “neural networks”.

Some Machine Learning techniques that Deep Learning uses combining it with neural networks help in influencing human decisions. It requires a huge set of data and number of parameters which make it expensive.

A deep learning algorithm could practice learning how a crocodile looks like. It may use a huge number of resources (datasets) of crocodile images to understand how it differs from an alligator.

A device with Deep Learning capabilities can scan humongous amounts of data (a fruit’s shape, its color, size, season, origin, etc.) to define the difference between an Orange and an Apple.

Two major differences between ML and DL:

  • Deep learning automatically finds out the features which are important for classification, where in Machine Learning, these features should be given manually;
  • As against Machine Learning, Deep Learning requires significantly large volume of data to work well and thereby require heavy high-end machines.

Of course, the differences between Artificial Intelligence, Machine Learning and Deep Learning are subtle and not as obvious as that of determining a difference between two fruits! This is because Deep Learning is the next evolution of Machine Learning!  And Machine Learning is one of the ways to achieve artificial intelligence!

Why don’t you give us a shout here so that we can demonstrate how your enterprise can use Machine Learning & AI to create models that reveal insights for predictive risk mitigation and faster response to challenge varied business situations. To explore our AI/ML solutions, click here.

 

Difference Between Data Warehouse & Data Lake|CloudMoyo

Difference between a Data Warehouse and a Data Lake

Is a data lake going to replace the data warehousing system in near future? Whether to use a data warehouse or a data lake or both? These are some of the common queries raised by the business users. Businesses should understand the concept of both data lake and data warehouse, most importantly when and how to implement them.

A data Lake is a repository that stores mountains of raw data. It remains in its native format and transformed only when needed. It stores all types of data irrespective of the fact that whether they are structured, semi-structured or unstructured.

On the other hand, a data warehouse is a storage repository that stores data that are extracted, transformed and loaded into the files and folders. A data warehouse only stores structured data from one or more disparate sources that are processed later for the business users. Data extracted from a data warehouse helps the users to make business decisions.

Read and know-Towards which direction is the Data Warehouse is moving?

What is Right for Your Company- A Data Lake Or A Data Warehouse Or Both?

Organizations, nowadays, generate a huge amount of data and access the huge number of disparate datasets. It makes the gathering, storing and analyzing of data more complicated. Therefore, these are the factors to choose data management solutions- for data gathering and storing and later analyzing them for competitive advantages. Here’s where data lakes and data warehouses help the business users in their own way. Data Lakes can be used to store a massive amount of structured and unstructured data that comes with high agility -can be configured and reconfigured when needed. The data warehouse system as a central repository helps the business users to generate one source of truth. It needs IT help whenever you use the data warehouse to set up new queries or data reports. Some data, which is incapable of providing answers to any particular query/request, is removed in the development phase of a data warehouse for optimization.
Take a deep dive into the Microsoft Azure Data Lake and Data Analytics
Classifications give Clarifications

Let’s explore and classify a few points to present some key differences between the Data Lake and Data warehouse:

  1. Data: Data Lakes embrace and retain all types of data, regardless of whether they are texts, images, sensor data, relevant or irrelevant, structured or unstructured, etc… Unlike a data lake, data warehouses are quite picky and only store structured, processed data. When the data warehouse is in its development stage, decisions are made on the grounds of which business processes are important and which data sources are to be used. A data Lake allows business users to experiment with different types of data transformations and data model before a data warehouse gets equipped with the new schema.
  2. User: Data lakes are useful for those users who are looking for data to access the report and quickly analyzing it for developing actionable insights. It allows users like data scientists who do an in-depth analysis of data by mashing up different types of data, extracted from different sources- to generate new answers to the queries. A data warehouse, on the contrary, supports only a few business professionals who can use it as a source and then access the source system for data analysis. A Data warehouse is appropriate for predefined business needs.
  3. Storage: Cost is another key consideration when it comes to storage of data. Storing data in a data lake is comparatively cheaper than in a data warehouse. A data warehouse deals with data of high volume and variety, thus, is designed for a high cost storage.
  4. Agility: A data warehouse is highly structured, therefore, comes with low agility. The data lakes, on the other hand, requires to technically change the data structure from time to time as it lack a defined structure that help developers and data scientists to easily configure queries and data model when need arises.

Below is a handy table that summarizes the difference between a Data Warehouse & a Data Lake –

Basis of Differences Data Warehouse Data Lake
Types of data Stores data in the files & folders Stores raw data (Structured/Unstructured/Semi-Structured) in its native format.
Data Retention Do not retain data Retains all the data
Data Absorption Stores transaction system or quantitative metrics Stores data irrespective of volume and variety
User Non-cosmopolitan like the business professionals Cosmopolitan-the Data scientists
Processing Schema-on-write, meaning- cleansed data, structured Schema-on-Read, raw data which only transforms when needed
Agility Needs fixed configuration-less agile Configuration and reconfiguration are done when required-Highly agile
Reporting and Analysis Slow and expensive Low storage, economical

In the concluding lines, it is quite tempting to say, “go with your current requirements” but let me advocate you here that if you have an operative data warehouse just go for implementing a data lake for your enterprise. Alongside, your data warehouse, the data lake will operate using new data sources you may want to fill it up with. You can also use the data lake as an archive storage and like never before, let your business users access the stored data. Finally, when your data warehouse starts to age you can either continue it by using the hybrid approach or probably move it to your data lake.

Learn more about Azure Data Lake, Azure Data Warehouse, Machine Learning, Advanced Analytics, and other BI tools.

Machine Learning apps using Microsoft Azure-CloudMoyo (2)

Building Enterprise-Class Machine Learning Apps Using Microsoft Azure

In our earlier post, we introduced the concept of Machine learning (ML) and also some types as well as applications in real world. In the second part of this series, lets peek into how to build Machine learning apps using Microsoft Azure.

What is Azure Machine Learning Studio?

Microsoft breaks down the use of Machine Learning (ML) in simple terms. As they put it, “ML examines large amounts of data looking for patterns, and then generates code that lets you recognize those patterns in new data. Your applications can use this generated code to make better predictions. In other words, Machine Learning can help you create smarter applications.”

Naturally, ML will seem daunting at first, and you may possibly feel like it’s a technology that has no use for your organization but there are a number of applications that make ML easy to use.

The Machine Learning Studio, powered by Microsoft Azure, is a powerful simple browser-based, visual drag-and-drop authoring environment where no coding is necessary. At its core, it’s a cloud service that helps people and organizations execute the machine learning process.

The Microsoft ML solution integrates neatly with open-source technology, and really delivers on the inherent value that is created from all the data that our modern sophisticated tools can generate. It stands to reason that the more data you have available, the more accurate your relevant results are going to be.

Azure enabled Machine Learning and Analytics

Azure Data Platform also known as Cortana Intelligence provides everything you need to transform your organization’s data into intelligent action. Below, we take a look at some key advanced analytics components that are a part of this suite and can help you build enterprise-grade machine learning applications-

  1. Azure Machine Learning Studio: Azure ML Studio is a fully managed cloud service that allows easy to build, deploy and shares predictive analytics solutions. It enables to deploy your model into production as a web service that can be called from any device, from anywhere and that can use any data sources.
  2. Data Lake Analytics: Azure Data Lake Analytics is a new distributed service in the Azure Data Lake. Built for cloud scale and performance, Data Lake Analytics makes the complex task of managing distributed infrastructure and complex code easy. It dynamically provisions resources and lets you do analytics on exabytes of data.
  3. HDInsights: Azure HDInsights can handle any amount of data, scaling from terabytes to petabytes on demand. It is a 100% Apache Hadoop distribution and because of that, HDInsights can process unstructured or semi-structured data from various sources. This helps business to get and analyze new types of data and discover some actionable business insights for competitive advantages.
  4. Stream Analytics: Azure stream analytics helps businesses to develop and deploy cost effective solutions with faster approach to acquire new business possibilities from streaming data in real-time. Stream Analytics can query data as it’s collected using an SQL-like language or feed it into machine learning models for analysis.

Read Also: An Ultimate Beginner’s Introduction to Machine Learning

Choose the Right Partners for Implementing Machine Learning for Your Organization

Working with third-party providers such as CloudMoyo gives organizations the ability to access the incredible power of Machine Learning, without needing to spend vast amounts of money and resources in setting them up.  Choosing the right partners to setup your infrastructure might be the most important decision that you ever make with regard to ML. The CTO of Sift Science, Fred Sadaghiani is quoted in Forbes magazine as saying that “a good machine learning person is a curious person, is somebody who can be creative, is somebody who can take an extremely abstract unclear problem and bring to light clarity around the possibilities.”

Machine Learning can help companies to:

  • Analyze historical or current data
  • Identify patterns and trends
  • Forecast future events
  • Embed Predictive Analytics into applications
  • Recommend decisions

Leveraging the power of data driven insights should be the goal of all analytics. It needs to produce results. When you add to the insights the predictive ability of the software itself to recommend decisions, then you begin to see the immense potential of machine learning over a period of time.

Conclusion

Machine Learning is a new and complex field. Successes will be hard won, and frustration is likely to be the order of the day. Companies need to look for partners who are determined and who have a relentless drive to seek out new answers and try new methodologies. Passion for this growing field is also a necessity, as well as passion for the industries in which the machine learning solutions are being applied.

Every passing day sees new stories coming to light about the applications around machine learning. Using ML to save on water bill, to boost the rewards for frequent flyer programs, to transform radiology, the list goes on and on. Over the next decade, organizations that have put systems in place and asked the tough questions about what ML can do for them now, stand to be the greatest beneficiaries of this brave new frontier of computer science.

CloudMoyo is a Microsoft Gold Partner that has invested heavily in developing a strong machine learning competency leveraging the Microsoft Azure Data Platform. Using Data Science, Natural Language processing (NLP) Internet scale data management, API and data cleansing/parsing/analysis, we can help your business to identify patterns or trends by analyzing current or historical data with the purpose to forecast future events. While integrating Machine Learning / Artificial Intelligence into business, we will embed predictive analytics into your application that will help in taking future decisions. Contact us today to set up a free consultation and start to reap the advantages from the data that you create.

 

Introduction to Machine Learning

An Ultimate Beginner’s Introduction to Machine Learning

For many people, their first experience and introduction to Machine Learning was the ‘Recommendations’ feature on Amazon. The system was able to predict future choices to a consumer based on what previous choices they had made. Many people conflate the concepts of Artificial Intelligence and Machine Learning, whereas, AI is the ability of a machine to perform intelligent tasks, while Machine Learning refers to the ability of a machine to find meaningful patterns and information from the data that it is given.

As long ago as 1959, the MIT engineer Arthur Samuels described machine learning as a “Field of study that gives computers an ability to learn without being explicitly programmed. The key change was the sudden availability of big data to inform machine-learning algorithms. “These algorithms provide a way to forecast future behavior and anticipate forthcoming problems,” according to Business Insider.

Machine Learning heralds a profound change in the technology around us, and it is already having present in many aspects of our lives.

Application of Machine Learning:

Let’s take a look at 3 areas where ML is having a profound impact:

  1. Traffic Management: The problem of traffic jams and commuter rush hours is something that most of us have to live with in our daily lives. It seems impossibly complex to solve. There are multi-mode transportation models to take into account, shifting weather dynamics, seasons, construction, accidents and so many other variables to consider on an ongoing basis. Despite the complexity, applications such as Google Maps have found ways to use location data from smartphones to analyze traffic movements in real time. Maps use the fundamentals of ML to ingest vast amounts of data and recommend traffic routes to commuters that will minimize travel time.
  2. Self-Driving Cars: Once the idea of driverless cars was just the stuff of science fiction. But no more. The future is rushing towards us at full-speed, and most car manufacturers expect to be in production of driverless cars in the next few years. Autonomous vehicles use an array of sensors, cameras, algorithms and masses of real-time data to create transport options that are predicted to dramatically reduce accidents, congestion, and pollution in the near future. Machine Learning is central to the way that self-driving cars will be operating in the near future.
  3. Spam Filters: It’s not only in massively complex applications that ML is coming in handy. Consider the daily work and the hundreds of decisions that must be made by spam filters on your email inbox. Simple rules are not enough to combat effective spammers who are constantly changing the way they work.“Machine learning allows the software to adapt to each user based on his or her own requirements. When the system flags some emails as spam, the user’s response to these emails (either reading or deleting them) will help train the AI agent to better deal with this kind of email in the future,” explains DigitalTrends.com

Introduction to Machine Learning (ML) Process 

Determining which data to use is the first step in effective machine learning. Once that data is selected, then it usually has to be formatted to correctly fit the process. Experts in the field are best suited to help companies select and prepare their data. You should bear in mind that getting to the point where the data is ready to process is often the most time-consuming part of any project.

Once the data is ready, then the team will choose algorithms that are suitable to the project. Microsoft explains how “these algorithms typically apply some statistical analysis to the data. This includes relatively common things, such as a regression, along with more complex approaches, including algorithms with names such as two-class boosted decision tree and multiclass decision jungle.”

Data experts combine the machine learning algorithms with the prepared data in various ways until they achieve the result they want to get. Once that pattern is established, then ML allows you to generate code which can be used in any new sets of data to achieve the same results quickly and effectively.

Read also: Building Enterprise-Class Machine Learning Apps Using Microsoft Azure

Integrating Artificial Intelligence with Machine learning 

There are many common misconceptions around the use of artificial intelligence. People often imagine it to be a reference to robotics, but in fact there are many more subtle applications of AI which are already gaining traction. Others see AI as being something that will replace workers, but in fact it can be used as a tool to augment and improve the work that people do.

“In the age of the connected customer, the most effective method of closing the customer experience gap is for companies to invest in advanced predictive analytics and artificial intelligence (AI) powered customer relationship management (CRM) platforms,” says Vala Afshar, Chief Digital Evangelist at Salesforce.

CEOs and CTOs need to understand the capabilities of AI in their chosen field, and understand how to build a business case for incorporating advanced technologies. Commitment to integrating AI into your business should come from the top-down, and be driven by partnerships with specialists who understand how it can work for you.

Machine Learning is often the first step towards a more comprehensive AI strategy.

Popular Machine Learning Methods

2 out of 4 methods are widely accepted and implemented, they are: Supervised learning and Unsupervised learning. Let’s take an overview of all the four types.

  1. Supervised Learning: Trained algorithms with labeled examples are known as supervised learning method. In this, any piece of tool or machine could have either of the two data points: Failed (“F”) or Runs (“R”). A set of inputs with the corresponding correct outputs is received by the learning algorithm and to find errors the algorithm learns by comparing its actual output with correct outputs. Then comes the process of modifications. Supervising Learning follow the method of classification, regression, prediction and gradient boosting. It uses certain patterns to foresee the values of the label on add-on data that are unlabeled. It helps the applications to predict future events on the grounds of historical data. For instance, insurance claims, credit card fraudulent, etc…
  2. Unsupervised Learning: In this, algorithm needs to figure out what data point is to be displayed, the system will not display the correct answer. This method of Machine Learning is used against non-historical labelled data. The aim here is to find a proper structure within the data by exploring it. For instance, this works best on transactional data. In other words, Unsupervised learning helps in identifying a particular section of buyers who can be managed/treated similarly in a marketing campaigns as they possess same attributes.
  3. Semi supervised Learning: This method uses both unlabeled and labeled data for training. This learning helps in classification, regression and prediction. Identifications of a person’s face on a web cam can be one of the examples of this method.
  4. Reinforcement Learning: A method in which the learning discovers the algorithm through trails and errors which action yield with great results. Reinforcement learning has 3 primary elements: (a) the agent- the decision maker, (b) the environment (everything the agent interacts with, (c) the actions (everything agent does or can do). So, the goal here for the agent is to make strong decisions that yield best results with faster approach.

Machine learning has recently gained a lot of popularity, since its inception in the early 90s. It enables business transform technology. However, businesses that want to get into the Digital Transformation scene sometimes find Machine Learning beyond their reach because it is inherently complex in nature.

Today, lot of tech players offer Machine Learning platforms. Microsoft Azure’s suite of machine-learning offerings is fairly comprehensive, targeting everything from companies seeking simple, on-demand services through to those looking to train their own models using in-house data scientists.

Therefore, turn your challenges into competitive advantages by finding CloudMoyo as your perfect Machine Learning consulting partner that will help you in making strategies to reduce redundant processes, optimize operations and enhance the business efficiency. Talk to our ML Expert today!