Tag: Data security

Understand the common issues faced by companies while adopting machine learning technology.

The global machine learning market is projected to grow from $15.50 billion in 2021 to $152.24 billion in 2028, according to a report by Fortune Business Insights. Enterprises all over the world are increasingly exploring machine learning solutions to overcome business challenges and provide insights and innovative solutions. And even though machine learning benefits are becoming more apparent, many companies are facing challenges in machine learning adoption.

As the name suggests, machine learning involves systems learning from existing data using algorithms that iteratively learn from the available data set. With this, systems are able to come up with hidden insights without being explicitly programmed where to look.

Importance Of Machine Learning

The interest in Machine Learning can be comprehended by simply understanding that there is a growth in volumes and varieties of raw data, the different processes, and hence, there is a need to find an affordable data storage.

The need of the hour is to implement a method by which organizations can quickly and automatically analyze bigger, more complex data. Not only this, by implementing and integrating Machine Learning in an organization, it becomes easier to optimize the process. How? Because Machine Learning helps deliver faster, and more accurate results.

What is simply required is to build a precise and customized model, in which Maruti Techlabs can serve as a fundamental assembling point, where your organization can find the best Machine Learning solutions.

Challenges Faced While Adopting Machine Learning

Machine learning is helping organizations make sense of their data, automate business processes, and increase productivity, and gradually profits too. And while companies are keen on adopting machine learning algorithms, they often find themselves struggling to begin the journey.

All the companies are different and their journeys are unique. But essentially, the frequently faced issues in machine learning by companies include common issues like business goals alignment, people’s mindset, and more. Let us discuss and understand the 6 most common issues which companies face during machine learning adoption. 

1. Inaccessible Data and Data Security

One of the most common machine learning challenges that businesses face is the availability of data. The availability of raw data is essential for companies to implement machine learning. Data is needed in huge chunks to train machine learning algorithms. Data of a few hundred items is not sufficient to train the models and implement machine learning correctly.

However, gathering data is not the only concern. You also need to model and process the data to suit the algorithms that you’ll be using. Data security is also one of the frequently faced issues in machine learning. Once a company has dugged up the data, security is a very prominent aspect that needs to be taken care of. Differentiating between sensitive and insensitive data is essential to implementing machine learning correctly and efficiently.

Companies need to store sensitive data by encrypting such data and storing it in other servers or a place where the data is fully secured. Less confidential data can be made accessible to trusted team members.

2. Infrastructure Requirements for Testing & Experimentation

Most companies that are facing machine learning challenges have something in common among themselves. They lack the proper infrastructure which is essential for data modeling and reusability. Proper infrastructure aids the testing of different tools. Frequent tests should also be allowed to develop the best possible and desired outcomes, which in turn, assist in creating better, stout, and manageable results.

Companies that lack the infrastructure requirements can consult with different firms to model their data groups aptly. Then, they can compare the results with a different perspective and the best one can be adopted accordingly by the company and subsequently, by the board.

The stratification method is usually used to test machine learning algorithms. In this method, we draw a random sample from the dataset which is a representation of the true population. The common practice is to divide the dataset in a stratified fashion. Stratification simply means that we randomly split the dataset so that each class is correctly represented in the resulting subsets — the training and the test set.

3. Rigid Business Models

Machine learning requires a business to be agile in their policies. Implementing machine learning efficiently requires one to be flexible with their infrastructure, their mindset, and also requires proper and relevant skill sets.

However, implementing machine learning doesn’t guarantee success. Experimentations need to be done if one idea is not working. For this, agile and flexible business processes are crucial. Flexibility and rapid experimentations are the solution to rigid monoliths.

If one of the machine learning strategies doesn’t work, it enables the company to learn what is required and consequently guides them in building a new and robust machine learning design. The willingness to adapt to failures and learn from them greatly increases the company’s chances of successful machine learning adoption.

4. Lack of Talent

This is the most worrying challenge faced by businesses in machine learning adoption. While the number of machine learning enthusiasts has increased in the market, it’ll still take a while for the same numbers to reflect on the number of machine learning experts.

With artificial intelligence and machine learning being relatively younger technologies in the IT industry, the talent pool required to fully understand and implement complex machine learning algorithms is limited. And if you don’t have the right people to implement it, then it is difficult to unlock the true potential of machine learning applications.

Organizations are gradually realizing the avenues machine learning can open up for them. As a result, the demand for experienced data scientists has skyrocketed. And so have the salaries in this space. Job sites list data scientists as one of the highest paying jobs of 2020. With more and more organizations getting on board with big data, AI and ML, this demand is only going to increase in the coming years.

One path companies are taking to overcome this challenge is collaboration. Organizations are partnering up with companies that have the skillset and the experience to harness the power of machine learning and implement the offerings to suit your organization’s business goals.

5. Time-Consuming Implementation

Patience goes a long way in ensuring that your efforts bear fruits. And this cannot be truer for machine learning. One of the most common machine learning challenges is impatience. Businesses that implement machine learning usually expect it to magically solve all their problems and start bringing in profits from the get-go.

Implementing machine learning is a lot more complicated than traditional software development. A machine learning project is usually full of uncertainties. It involves gathering data, processing the data to train the algorithms, engineering the algorithms, and training them to learn from the data which suits your business goals.

It involves a lot of intricate planning and detailed execution. And yet, due to multiple layers and the usual uncertainties regarding the behavior of the algorithms, it is not guaranteed that the time estimated by your team for machine learning project completion will be accurate. Therefore, it is very important to have patience and an experimentative approach while working on machine learning projects. To achieve desirable results on adoption machine learning, you should give your project and your team plenty of time.

6. Affordability

If you’re looking to adopt machine learning, you will require Data Engineers, a Project Manager with a sound technical background. In essence, a full data science team isn’t something newer companies or start-ups can afford.

As a result, employing a machine learning method can be extremely tedious, but can also serve as a revenue charger for a company. However, this is only possible by implementing machine learning in newer and more innovative ways. Adopting machine learning is only beneficial if there are different plans, so regardless of one plan not performing up to the desired standards, the other can be put into action. Getting a glimpse into which machine learning algorithm would suit an organization is the only issue that one needs to get by. Once you get the best algorithm with which you’re achieving the required outcomes, you shouldn’t stop experimenting and trying to find better and more innovative algorithms.

Budgeting as per different milestones in the journey works out well to suit the affordability of the organization. If you are not confident on the talent required to implement a full-fledged machine learning algorithm, you can always go for a consultation with companies that have the expertise and experience in machine learning projects.

As a machine learning solutions provider, we at Maruti Techlabs, help you reap the benefits of machine learning in line with your business goals. Our machine learning experts have worked with organizations worldwide to provide machine learning solutions that enable rapid decision making, increased productivity, and business process automation.

The robot-depicted world of our not-so-distant future relies heavily on our ability to deploy artificial intelligence (AI) successfully. However, transforming machines into thinking devices is not as easy as it may seem. Strong AI can only be achieved with machine learning (ML) to help machines understand as humans do.

Machine learning can be confusing, so it is important that we begin by clearly defining the term:

Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves.

How Does Machine Learning Work?
Similar to how the human brain gains knowledge and understanding, machine learning relies on input, such as training data or knowledge graphs, to understand entities, domains and the connections between them. With entities defined, deep learning can begin.

The machine learning process begins with observations or data, such as examples, direct experience or instruction. It looks for patterns in data so it can later make inferences based on the examples provided. The primary aim of ML is to allow computers to learn autonomously without human intervention or assistance and adjust actions accordingly.

Why Is Machine Learning Important?
Machine learning as a concept has been around for quite some time. The term “machine learning” was coined by Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming. Samuel designed a computer program for playing checkers. The more the program played, the more it learned from experience, using algorithms to make predictions.

As a discipline, machine learning explores the analysis and construction of algorithms that can learn from and make predictions on data.

ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes.

Data Is Key: The algorithms that drive machine learning are critical to success. ML algorithms build a mathematical model based on sample data, known as “training data,” to make predictions or decisions without being explicitly programmed to do so. This can reveal trends within data that information businesses can use to improve decision making, optimize efficiency and capture actionable data at scale.
AI Is the Goal: ML provides the foundation for AI systems that automate processes and solve data-based business problems autonomously. It enables companies to replace or augment certain human capabilities. Common machine learning applications you may find in the real world include chatbots, self-driving cars and speech recognition.

Machine Learning Is Widely Adopted
Machine learning is not science fiction. It is already widely used by businesses across all sectors to advance innovation and increase process efficiency. In 2021, 41% of companies accelerated their rollout of AI as a result of the pandemic. These newcomers are joining the 31% of companies that already have AI in production or are actively piloting AI technologies.

Data security: Machine learning models can identify data security vulnerabilities before they can turn into breaches. By looking at past experiences, machine learning models can predict future high-risk activities so risk can be proactively mitigated.
Finance: Banks, trading brokerages and fintech firms use machine learning algorithms to automate trading and to provide financial advisory services to investors. Bank of America is using a chatbot, Erica, to automate customer support.
Healthcare: ML is used to analyze massive healthcare data sets to accelerate discovery of treatments and cures, improve patient outcomes, and automate routine processes to prevent human error. For example, IBM’s Watson uses data mining to provide physicians data they can use to personalize patient treatment.
Fraud detection: AI is being used in the financial and banking sector to autonomously analyze large numbers of transactions to uncover fraudulent activity in real time. Technology services firm Capgemini claims that fraud detection systems using machine learning and analytics minimize fraud investigation time by 70% and improve detection accuracy by 90%.
Retail: AI researchers and developers are using ML algorithms to develop AI recommendation engines that offer relevant product suggestions based on buyers’ past choices, as well as historical, geographic and demographic data.

Training Methods for Machine Learning Differ
Machine learning offers clear benefits for AI technologies. But which machine learning approach is right for your organization? There are many to ML training methods to choose from including:

• supervised learning
• unsupervised learning
• semi-supervised learning

Let’s see what each has to offer.

Supervised Learning: More Control, Less Bias
Supervised machine learning algorithms apply what has been learned in the past to new data using labeled examples to predict future events. By analyzing a known training dataset, the learning algorithm produces an inferred function to predict output values. The system can provide targets for any new input after sufficient training. It can also compare its output with the correct, intended output to find errors and modify the model accordingly.

Unsupervised Learning: Speed and Scale
Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. At no point does the system know the correct output with certainty. Instead, it draws inferences from datasets as to what the output should be.

Reinforcement Learning: Rewards Outcomes
Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards. The most relevant characteristics of reinforcement learning are trial and error search and delayed reward. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback — known as the reinforcement signal — is required for the agent to learn which action is best.

Machine Learning Is Not Perfect
It is important to understand what machine learning can and cannot do. As useful as it is in automating the transfer of human intelligence to machines, it is far from a perfect solution to your data-related issues. Consider the following shortcomings before you dive too deep into the ML pool:

Machine learning is not based in knowledge. Contrary to popular belief, machine learning cannot attain human-level intelligence. Machines are driven by data, not human knowledge. As a result, “intelligence” is dictated by the volume of data you have to train it with.
Machine learning models are difficult to train. Eighty-one percent of data scientists admit that training AI with data is more difficult than expected. It takes time and resources to train machines. Massive data sets are needed to create data models, and the process involves manually pre-tagging and categorizing data sets. This resource drain can create latency and bottlenecks in advancing ML initiatives.
Machine learning is prone to data issues. Ninety-six percent of companies have experienced training-related problems with data quality, data labeling and building model confidence. Those training-related problems are a key reason why seventy-eight percent of ML projects stall prior to deployment. This has created an extraordinarily high threshold for ML success.
Machine learning is often biased. Machine learning systems are known for operating in a black box, meaning you have no visibility into how the machine learns and makes decisions. Thus, if you identify an instance of bias, there is no way to identify what caused it. Your only recourse is to retrain the algorithm with additional data, but that is no guarantee to resolve the issue.

The Future of Machine Learning: Hybrid AI
For all of its shortcomings, machine learning is still critical to the success of AI. This success, however, will be contingent upon another approach to AI that counters its weaknesses, like the “black box” issue that occurs when machines learn unsupervised. That approach is symbolic AI, or a rule-based methodology toward processing data. A symbolic approach uses a knowledge graph, which is an open box, to define concepts and semantic relationships.

Together, ML and symbolic AI form hybrid AI, an approach that helps AI understand language, not just data. With more insight into what was learned and why, this powerful approach is transforming how data is used across the enterprise.