However, the growth of AI within the data analytics realm has caused concerns in relation to data privacy, ethics, and governance.

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The convergence of data analytics and Artificial Intelligence

Data analytics focuses on statistical analysis and pattern recognition.

AI, particularly machine learning, focuses on algorithms that learn from data, adapt, and make predictions of decisions with minimal programming. Intersecting these two fields amplifies their strengths and allows them to build off each other.

Machine learning models have the ability to uncover intricate trends and patterns that basic data analysis can not. Businesses are able to accurately forecast outcomes, optimize and automate processes, or make data-driven decisions with speed and accuracy.

While most data companies keep is structured, analyzing unstructured data is equally as important. Natural Language Processing (NLP) and computer vision techniques can derive valuable insights from text, images, and videos, expanding data analytics capability.

Learn more about Natural Language Processing in business:

• How is Natural Language Processing (NLP) used in business?
• NLP techniques: key methods that will improve your analysis
• How do NLP and IDP address business challenges?
• The future of Natural Language Processing

Overall, the convergence of these two realms has unlocked untapped information for many organizations.

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Why is data analytics in Artificial Intelligence important?

Data analytics in artificial intelligence is important because it better informs decision-making, enhances efficiency and productivity, personalizes the customer experience, and manages risk.

Data-driven organizations can make decisions by uncovering correlations between large and complex datasets that require data mining techniques.

Companies are able to optimize strategies, reduce churn with AI deep learning algorithms, anticipate market shifts, and identify opportunities for growth.

Furthermore, AI-driven data analytics personalizes customer experiences by analyzing vast amounts of data to tailor products, services, and marketing strategies to the customer and target market.

Finally, vulnerability assessment and fraud detection are made accessible to more businesses due to AI’s ability to identify anomalies to detect potential risks and fraudulent activities.

Artificial Intelligence in data analytics: benefits and opportunities

Before getting into the challenges, it is important to expand on the many benefits AI in data analytics offers.

These benefits and opportunities include deep learning and big data, predictive and prescriptive analysis, customer insights, neural networks, and data visualization.

Deep learning and Big Data: unlocking new horizons in Analytics

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Deep learning is a subset of machine learning that trains algorithms called neural networks to recognize patterns, classify data, and make predictions.

The primary benefit of deep learning is that it can effectively analyze large quantities of data called big data.

In many organizations, millions of transactions happen each day. Each of these transactions has data attached to them, meaning the quantity of data the company collects is massive.

The term “deep” in deep learning means there are many layers within the trained neural networks. These multiple layers allow for a complex analysis and understanding of big data.

Deep learning has most notably seen success within autonomous vehicles and healthcare diagnostics due to its ability to automatically learn representations from raw data and make complex predictions.

Check out these articles:

• Importance of data science in IT industry
• Data-driven insurance: exploring the latest trends
• Business Intelligence: unlock the potential of your data and put it to use

AI’s role in predictive and prescriptive analysis

Predictive analysis takes historical data and statistical algorithms to forecast future outcomes. Prescriptive analysis takes predictive analysis a step further by suggesting possible actions and interventions to optimize or influence forecasted outcomes.

AI plays a transformative role in both predictive and prescriptive analysis, enhancing the capabilities and accuracy of these analytical approaches.

With AI, predictive analysis leverages machine learning algorithms to forecast future trends. These algorithms include regression, time series analysis, and neural networks.

The use of AI in predictive maintenance is often used in stock market forecasting, disease prognosis in healthcare, and customer behavior prediction in marketing.

Within prescriptive analysis, AI recommends the best course of action to achieve desired outcomes using computational models and algorithms.

Furthermore, AI aids in scenario planning, which aims to simulate different scenarios and prescribe actions. Scenario planning is widely used in supply chain management, resource allocation, and even healthcare for treatment recommendations.

AI-driven data analytics in customer insights

As previously touched on, complete and accurate customer insights can be generated with AI-driven data analytics.

These insights include personalized customer profiles, segmentation and targeting, and sentiment analysis. AI enables hyper-personalization by creating detailed consumer profiles from vast datasets.

These profiles encompass preferences, purchase history, and interactions across multiple touchpoints. Businesses use this data to tailor offerings and improve customer satisfaction.

AI and Machine Learning algorithms also excel in segmenting customers into finer segments based on nuances, similarities, and behaviors. Businesses can target highly specific groups, and they could use it as a competitive advantage.

Regarding sentiment analysis, Natural Language Processing algorithms analyze unstructured data like reviews and social media comments to help understand customer satisfaction levels and proactively enhance the customer journey.

Unveiling patterns: how neural networks transform complex data structures

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Neural networks, a fundamental component of deep learning, excel at discovering patterns within complex data structures to extract valuable insights. Neural networks consist of interconnected layers of artificial neurons, each performing specific computations.

Each layer extracts increasingly abstract and complex features from the raw input, allowing the network to automatically sort data and unveil intricate patterns. Neural networks need to be trained in order for them to unveil these patterns.

They automatically learn relevant features from the data during the training process. Eventually, the networks will be able to learn by themselves to discover complex patterns and insights.

Within neural networks, there are two subsets that are used to handle high-dimensional data like images and text. Convolutional neural networks (CNNs) analyze images by learning hierarchical features. Recurrent neural networks (RNNs) process sequential data like time series or natural language.

These networks are adaptable and flexible depending on how they are trained. They can be trained on diverse datasets and applied to a wide range of problems.

Even though neural networks unveil intricate patterns within large sets of data, the depth of their functionality is a byproduct of how they are trained.

How AI pushes the envelope in data visualization

Once AI data analysis features insights within business analytics and gains valuable insights, AI can also be used to effectively visualise these insights. After all, customer data mean nothing unless they are conveyed in a way that management can understand.

Besides automating the insights it discovers, AI tools can also provide interactive visualisations that allow users to explore data dynamically. For example, users can drill down into visualizations at a finer level than usual.

Finally, AI can be used to generate real-time data visualisations that are updated as new data is retrieved. This capability is useful within applications pertaining to financial markets and other monitoring systems.

Data analytics and Artificial Intelligence: challenges

The benefits of AI within business intelligence are vast and make the future of data analysis shine brighter as the technology develops.

However, there are also many challenges associated with AI that have expanded into the realm of data analytics, creating the need for caution when using this great tool.

Ethical considerations: responsible use of AI in data analysis

Like many practices, there are ethical considerations that must be considered when using AI and deep learning algorithms.

First, AI algorithms like neural networks are often trained on biased or incomplete datasets, which perpetuate and exacerbate social biases, leading to unfair outcomes. Therefore, companies should be intentional with the databases they use.

In the realm of data privacy and protection, striking a balance between data utilization and safeguarding individual privacy rights is a must to avoid lawsuits that result from poor data protection.

Since AI-powered solutions must be trained on loads of existing data, companies must ensure that this data is attained legally and managed carefully.

Challenges in merging legacy systems with advanced AI solutions

Integrating legacy systems with advanced AI solutions creates several challenges due to differences in technology architectures, data formats, and operational paradigms.

These challenges have the ability to impede the seamless incorporation of AI into existing systems.

Regarding compatibility, legacy systems operate on outdated technology stacks or proprietary formats that are incompatible with modern AI frameworks. Bridging this gap requires adaptations and integrations that might not align quickly and seamlessly.

Furthermore, legacy systems may contain fragmented or inconsistent data stored in disparate formats. In order to integrate AI, a company must aggregate and clean these silos to ensure accuracy and uniformity, which can be complex and time-consuming.

Other challenges relating to integrating legacy systems with advanced AI solutions include security and compliance, skill gaps, costs, and resistance to change.

Merging new and old technology requires time, money, skills pertaining to both systems and buy-in from the people working within the organization.

Transparency and trust: building confidence in AI-generated data insights

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To the naked eye, it may seem skeptical to make decisions based on AI-generated insights. However, this skepticism is totally normal and stems from the lack of transparency and trust around AI-driven data analytics solutions.

For example, an organization can encourage employee buy-in to a new AI solution by making AI models explainable and interpretable. Techniques like providing feature importance, model explanations, and decision rationales enhance transparency and build trust by demystifying AI-driven predictions.

Adhering to ethical guidelines and regulatory compliance also instills confidence in AI-driven insights. AI models that prioritize fairness, privacy, and compliance with data protection regulations build trust among users and stakeholders.

Finally, making AI interfaces user-friendly and continuously monitoring the functionality of AI solutions will encourage people to use the system due to the sense of familiarity and support they feel.

Weighing the opportunities and challenges for informed decision-making

Wrapping up the conversation on Artificial Intelligence (AI) in data analytics reveals a landscape teeming with both boundless opportunities and formidable challenges.

From the granular patterns uncovered by neural networks to the personalized customer experiences crafted by AI-driven analytics, the potential for innovation seems limitless.

However, amidst these opportunities lie challenges that demand diligent navigation and ethical foresight.

The ethical considerations surrounding AI’s role in data analytics underscore the importance of responsible use. Issues of data privacy, bias mitigation, and compliance with regulations must be addressed to ensure the ethical and equitable deployment of AI-driven insights.

Moreover, integrating advanced AI solutions with legacy systems poses hurdles related to technological disparities, data compatibility, and cultural shifts, necessitating a delicate balance between innovation and practical implementation.

Analyze data using AI/ML tools in partnership with Future Processing

Explainable AI, stringent ethical frameworks, user-friendly interfaces, and continuous monitoring stand as pillars fortifying confidence in AI-generated insights.

By embracing these strategies and advocating for ethical practices, organizations can harness the full potential of AI in data analytics while fostering a climate of trust, reliability, and responsible innovation.

As the journey through the terrain of AI and data analytics continues, navigating these challenges with foresight and adaptability will undoubtedly pave the way for a future where insights are not just powerful but also transparent and trustworthy.

If you are looking for an experienced and proven business partner for AI and data analytics activities, then get in touch.

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Churn Prediction Using AI

One of the ways in which firms can use artificial intelligence to retain more customers is to conduct churn prediction. By using various AI services to gauge customer behavior, firms can reliably predict customer churn and track the various factors that may account for it.

In the modern era, data is the currency of digital storefronts, and optimizing data usage is the holy grail of all business processes. One way that firms can attain this goal is by integrating machine learning algorithms. These algorithms can use customer demographics to reliably return the consumers who are at the highest risk of churning.

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Various inputs are needed to help ensure accurate predictions, so ensuring that there is proper research conducted on the market is a must for any firm looking to implement AI services. Some of the best inputs to use for machine learning algorithms include customer demographics, transactions, pricing, economic factors, competitor activity, consumer behavior, customer journeys, and more. Essentially, the more variables that are fed into the service, the better it will be able to predict consumer behavior.

Determine Intervention Timing with AI Churn Applications

Churn prediction and intervention require the correct timing on when to get involved as a business. A churn prediction model may be able to help determine levels of churn and ways to reduce customer churn. However, to successfully reduce customer churn, the time to intervene must be determined carefully. Intervention timing can be tricky, but AI churn applications can be used as an effective tool in determining this and reducing customer churn.

Identifying Trigger Events

Trigger events are events that cause a customer to want to stop doing business with a company. These are usually events that are within the control of businesses and include instances in which customers felt undervalued or disrespected even once, giving them reasons to leave. To identify these important trigger events, AI applications can be leveraged and utilized.

Generally, customers are not usually forthcoming with their motivations for leaving, but to determine any reasons as to why customers wish to leave, historical data can be aggregated to identify potential triggers. Potential triggers for customers include price hikes, service outages, and customer service interactions. These can all be prevented or mitigated, and AI systems can categorize customer behaviors occurring due to these changes and test whether such events are leading to customer churn.

Use AI to Intervene at the Right Time

Following the usage of applications that can adequately determine when a customer is most likely to leave a company, that business can then intervene in an immediate manner. Numerous software solutions available send alerts when individual customers or customer accounts are likely to churn, providing companies with sufficient notice to take remedial actions to retain these customers.

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These remedial actions can be manual, or they can be automated, using AI to enhance the promptness of such actions. For manual actions, customers can be reached out through phone, email, or social media, offering them discounts or sales that would entice them to stay with the company further rather than leave.

While this is effective, an AI-powered solution could perform this action faster and without the need for human action. For example, customers could be enticed through AI-prompted customer interactions such as reaching out, providing support, asking if they need help, or providing a promotional offer. All of these demonstrate how AI can be used to intervene and prevent customer churn in a timely and efficient way.

Two More Action Steps to Leverage AI

Following the first step, intervention, there are two more action steps that need to be taken to successfully leverage the power of AI in reducing churn:

Acquisition

The possibility of more customers churning is not just predicted by their profile elements or interactions with the business. It is also predicated on the acquisition channel (Google Adwords, social media, content marketing, partner referrals, etc.).

Based on the predictive analysis performed per channel, companies can target the most lucrative users who would provide the best retention and customer lifetime value. They can tailor their products to these customers, allowing them to maximize their long-term profits and success without sacrificing real value.

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Experience

Additionally, a better customer experience also helps to reduce customer churn. For example, in each acquisition channel, color, font, user flow, and other parts of the experience are all things that ultimately impact churn. With AI and behavioral analytics, companies can ascertain where to focus their efforts on tweaking the user experience to reduce customer churn.

Automating Customer Retention and Modeling Customer Sentiment with AI

As automation becomes more commonplace in today’s business world, there is no reason why it should not be applied to customer retention, customer churn, and customer sentiment modeling strategies. With the power of artificial intelligence (AI) and machine learning (ML), customer retention rates and customer loyalty can be amplified while churn can be reduced.

As seen in the graph below, marketing automation, which includes automating customer retention, is one of the top performers in engaging both new and old customers.

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Automating the Process of Offering Incentives to Keep At-Risk Customers

The most useful process to automate is the process of offering incentives to keep at-risk customers – customers that are about to or at risk of churning. The automation of the process can begin with the prediction of at-risk customers with Machine Learning (ML). ML can identify trends within consumer habits to identify which customers are at risk of churning.

When it is predictable to determine when a customer is most likely to churn, incentives can be offered at the right time. These incentives can be automated with the power of AI and machine learning models. An AI platform, Tellius, automates customer retention by ‘offering the right incentive at the right stage of the customer journey before a customer cancels or defects.’

With ML, platforms like Tellius can determine which offers will best persuade customers to maintain their loyalty. They can then personalize that specific offer to play a major role in increasing customer retention rates.

4 More Ways to Automate Customer Retention

There are a plethora of ways to automate customer churn. However, automating these aspects will lead to the greatest benefits and keep bringing in new customers without sacrificing the customer experience. As seen in the graph below, a small change in retention leads to significant changes in revenue. Therefore, automating customer retention in as many ways as possible is vital to an organization’s ongoing success.

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Stay Updated About Customers

The best way to predict churn is to stay updated about customers to act on the most up-to-date information. It is vital to know where leads are in order to ensure a smooth customer journey and eliminate any churn tendencies.

Reach Out at the Right Time

Reaching out at the right time is similar to offering incentives at the right time. Automating the process of following up on leads instantly can scale outreach efforts without sacrificing the quality of communications. This form of automation is simple but effective. An example can be sending automated check-ins through email.

Ask Customers for Feedback

Asking for feedback and constructive criticism is crucial for a business to recognize what is working and what is not working. Even though feedback can shine a negative light on certain aspects of a business, it can also improve product and buyer experience. Furthermore, positive reviews often outshine negative reviews and can lead to more clients and even eliminate instances of presumed churn in existing clients.

Thank Customers and Clients

Finally, saying “thank you” can go a long way, especially when it’s automated. Showing gratitude fosters long-term relationships, which can minimize churn. After all, the best customer is a returning customer that will consistently and undoubtedly stay loyal and purchase an organization’s product or service.

Analyzing Customer Sentiment

In short, analyzing customer sentiment means analyzing customer interactions to predict how they are going to act and prevent churn. Analyzing customer sentiment can be automated by natural language processing (NLP), which analyzes the data from emails, reviews, text messages, and phone calls to identify which services need to be altered or improved. This data is often used to update technologies, retrain customer service, or modify products.

Conclusion

Churn reduction can be handled in different ways by companies, but various methods, including AI, have been found to utilize customer data and predictive analytics to help reduce the churn rates that companies face to the greatest extent.

AI has grown increasingly important in today’s digital marketplace. Companies will soon find themselves lagging behind their competitors and dealing with massive customer churn rates if they fail to implement AI applications to reduce churn successfully in an immediate and timely manner.

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Critical Steps for Approaching Analytics in Your Business

Developing practices required to reduce customer churn and increase customer satisfaction may take an organizational overhaul from the very top. For example, an organization’s structure utilizing analytics must be built from the ground up, ensuring that a company has the business processes, infrastructure, and support to analyze customer behavior and increase customer satisfaction through different methods of analyzing customer churn and calculating customer churn.

Customer churn metrics can be difficult to manage at first, but there are critical steps that must be taken to apply customer churn analytics and allow for the building of loyal customers compared to losing customers.

Initially, a business should define the role of analytics in their company and establish its necessity, create an analytics center of excellence to measure customer churn and move from functional silos to cross-functional teams.

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An analytics-led approach for a company to solve business problems, such as analyzing customer churn, requires a top team that consistently finds data to support analytics and continuously runs tests to make sure that strategies are working successfully. For example, at one leading high-tech player, the senior team demands that data analytics support every decision.

In doing so, the company actively seeks to test new use cases for data and ensure that strategies and findings discovered through data are supported through multiple tests over time. By having a top team that sets the standard for analytics, agility, and quick responses, companies can set the guidelines for their company to master customer churn analysis basics and ensure that new customers find value in the product or service.

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Additionally, organizations that place importance on customer loyalty, analyzing churn, and retaining existing customers should establish a center of excellence for analytics that is maintained by qualified data scientists and data engineers. This center can help in bringing forth cross-functional teams and ensures a hub of interconnectivity that allows for unilateral decision-making made due to data analytics to affect all aspects of the company.

For companies focused on churn analysis, preventing poor customer service, average customer lifetime, etc., data centers should be focused on those key aspects, and data for such key metrics must continually be collected. For most companies, this can be accomplished by working with an external data analytics provider that utilizes an important churn analytics tool to measure churn metrics and allows for revenue growth and company success.

Finally, increasing the rate of experimentation in data analytics within an organization helps guide the significant changes to existing business operations. Instituting rapid test-and-learn processes like those required for customer churn analytics and analyzing churn metrics and churn data requires truly cross-functional teams that include members of multiple departments of a company, such as marketing, finance, operations, IT, and legal. Each team should have the results for different segments of data and operations and be empowered to identify and implement new strategies that focus on demonstrating product or service value to more customers and getting customer feedback on their tactics.

All of these steps are critical in order for businesses to successfully transition into analytics-driven organization that use data to analyze and determine why customers churn in order to help reduce this rate.

Strategies to Reduce Churn Using Data

Customer churn can be difficult to predict or control organically, but by directly leveraging data as part of reduction strategies, organizations can make improvements that can really pay off when it comes to customer retention.

Revise Internal Processes

The first set of factors that businesses need to take into account when forming strategies to reduce churn is internal processes. These processes can manifest in a variety of effective methods aimed at reducing the customer churn rate.

One step is to develop a coherent data roadmap and stick to it. It should include automated and scalable KPIs that you can use to measure progress and create targeted solutions, as well as prioritize tasks that will help you address churn efficiently.

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Ensure that any changes you make are based on the data available, rather than simply relying on guesswork or intuition. It’s also important to differentiate between data-related issues and systemic problems – either may be causing your churn, but your chosen solutions need to be tailored specifically to each one if they’re going to have any real effect.

After a data roadmap has been constructed, the next step is to follow through on the roadmap by using data analytics and machine learning to create predictive models.

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Analyzing customers to determine which users drop, where voluntary churn occurs, and other customer churn analysis metrics can help to create an idea of impending customer churn. Based on this, companies can utilize machine learning to generate predictive models that will improve the customer journey, customer experience, and other business processes that directly tie to the customer churn rate.

Find Your Target Customers

While businesses can orient themselves internally to prevent customer attrition as much as possible throughout the entire customer journey, these churn reduction strategies can fail if the right customer segments and markets are not targeted.

An important factor to include in churn strategies is to focus on high-quality leads. Using data to determine crucial information about a business’s customer base is key. Having an idea of how many customers engage with the business, customer lifetime value, and other historical customer data and churn data is essential to being able to predict churn and improve customer retention. Consolidating, analyzing, and applying this data in the form of algorithms can help identify which customers leave and generate a churn prediction for prospective customers.

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Using this algorithm to shape the business model and limiting which customers are being targeted can reduce the churn rate and decrease initial customer acquisition costs.

A similar but distinct customer-facing strategy is to segment the market to focus on retaining the right customers. Distinguishing between groups based on how customers engage with a business, such as via RFM metrics, is another vital step in creating customer churn strategies.

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Specialized churn analyses based around these groups can enhance all your metrics and make them more tailored and applicable to specific customers. By using data and product analytics tool sets in this more specialized manner, businesses can tailor each customer’s life cycle and improve the customer experience and reduce churn rates.

Specific Considerations and Best Practices for Customer Retention

Now that it is understood that the churn rate can be heavily reduced with the use of data analytics, the specific considerations and best practices of using analytics to minimize churn must be evaluated. The graph below shows that companies most commonly use analytics to modify business processes like churn reduction. Like any step in reducing customer churn, incorporating data analytics into a business’s churn reduction strategy must be done the right way.

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Best Practices for Customer Retention Analytics

Gather Multiple Data Points to Make Relevant Recommendations

Decisions should not be made based on one data point or trend. In order to fully understand the metric a business is measuring, the business must analyze multiple data points and trends and not make assumptions around only one piece of data. An example can be someone in New York, USA buying a surfboard. Even though this purchase is a data point, it does not mean the customer should be shown advertisements for surfboards. They could be purchasing the surfboard for a friend or family member in California, USA. Therefore, it is vital to analyze consumer trends over time to generate actionable insights.

Leverage Social Proof Where You Can

Oftentimes, customers that aren’t responding well to certain product recommendations need to be reminded that others who share similar interests to them are benefiting from using those specific products. A business must proactively identify customers that are content with a certain product and then acquire positive testimonials from social media comments and surveys to use as a key selling point.

High Quality Data Is a Must

High-quality data turns into high-quality results. Before engaging in churn analysis using data analytics, a company must first test methods to strengthen the data it collects. Many a time, all a company is lacking is the improvement of its internal data collection measures. In other cases, a business should look into the methods they use to collect external data and experiment with new products.

Improving Internal Data Collection

For many organizations, the way to noticing key performance indicators and generating effective data analytics is improving internal data collection. Therefore, this section of the article will highlight the most important pieces of advice that a company can use to improve this area. Below you can find a list of the methods that are the most effective:

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Be Particular When Asking Questions

Being particular when asking questions means not asking the wrong questions, not asking for the consumer ratings of a specific product or service, and not being too vague. Basically, an organization should strike a healthy balance between specific and vague questions.

Don’t Be Impersonal

It is easy to make a survey or other data collection method sound impersonal or like an organization does not care about their customer. Consumer surveys should be treated like any other aspect of customer service. Questions should be worded carefully and encourage the consumer to give their honest opinion.

Don’t Send Long or Complicated Surveys

Long and complicated surveys discourage customers from giving feedback, even if they really want to. Surveys should be easy to access and not take more than five minutes. In fact, there is no reason a survey should be longer than five minutes because the purpose of the survey is to simply create actionable insights.

Mention How Much Customer’s Feedback is Valued

This point is similar to the point about not being impersonal. The consumer is doing the business a favor by filling out a survey. After all, the opinion of existing customers and new customers is vital to deciphering key performance indicators.

Don’t Just Focus on Detractors

It is easy to try to get the customer’s view on every single problem within a company. However, resolving minor problems that the customers have will only benefit a business in the short term. With that being said, it is important to get customers’ views on minor problems every so often; however, the survey should primarily attempt to get a strategic view of what is going well and what is not going well within the organization.

Conclusion

Reducing churn is becoming an increasingly difficult task in a digital era where competitors can be found anywhere and at any time within a digital environment, and companies must quickly shift their tactics if they wish to succeed.

While there are multiple methods that exist to reduce customer churn, enhancing business practices to include analytics and data will allow businesses to quickly combat a rising churn rate, even if implementation may take some time.

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ML in PL conference is held annually since 2017. At the beginning it was organised at the Faculty of Mathematics, Informatics and Mechanics of the University of Warsaw, but during the pandemic it was moved to a virtual platform. This year it came back to its original location, after two years of existing just in virtual space.

The main aims of the conference (and of the ML in PL Association in general) are to:

• Build a strong local community of ML researchers, practitioners, and enthusiasts at various levels of their careers,
• Support new generations of students with interests in ML and promote early research activity,
• Foster the exchange of knowledge in ML,
• Promote business engagement in science,
• Support international collaboration in ML,
• Increase public understanding of ML.

This year’s conference lasted for three days and was packed with knowledge, networking, and entertainment.

ML in PL conference – agenda

It all started with a students’ day, where one could listen to eight presentations done by students or take part in the NVIDIA’s workshops on mechanics of deep learning.

The core part included:

• 9 key-note lectures,
• 3 discussion panels,
• 9 contributed talks,
• 4 sponsors’ talks,
• a poster session with 34 posters.

Among many topics covered were learning with positive and unlabelled data, computer vision, probabilistic & auto ML, deep learning, reinforcement learning, NLP, science-related ML, probabilistic neural networks and consolidated learning. Besides lectures, there were also multiple sponsors’ booths and a conference party, giving immense networking possibilities.

The conference was so rich in topics, lectures, and meetings that it is impossible to cover all of them. That is why I selected four which in my opinion were the most inspiring and interesting ones. Here they are!

Can you transfer the best software engineering practices to machine learning code?

The short answer is you can, and you should. And you can even get a really nice assistance with that! This assistance is called Kedro. Kedro is an open-sourced Python framework for creating maintainable and modular machine learning code. It was presented by Dominika Kampa and her colleagues from QuantumBlack AI by McKinsey.

One of its most powerful features is the pipeline visualisation. The ML code can be very complex and maintaining it as well as explaining it to business is often too much. If one is able to represent the code as a flow with clear input, output, parameters, dependencies and layers, then it’s a lot easier to grasp the entire solution as well as its bits. I recommend going through a demo, where you can check out how the visualisation works in practice.

One of the technical underlying aspects of the visualisation is the project template. This is how you start the project – by defining directory structure. Afterwards you add the data, create a pipeline with the use of functions and finally package the project by building documentation and preparing it for distribution.

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Another interesting feature is the experiment tracking. The results together with environment description of all your experiments are stored in one place with a possibility to easily go through them. The only thing you need to do is add a few lines of code.

Does human-AI synergy exist?

One of the most inspiring and enthusiastic talks was given by Petar Veličković, Staff Research Scientist at DeepMind, Affiliated Lecturer at University of Cambridge and an Associate of Clare Hall, Cambridge. His main research interest is geometric deep learning, particularly graph representation learning. This topic is recently becoming popular, both in applications and research. Graphs enable modelling complex relationships and interdependencies between the objects. They find many applications from social science, through logistics to chemistry and many more. Combined with machine learning, they demonstrate ground-breaking achievements, mainly due to their great expressive power.

Among the most renowned success-stories of applying Graph Neural Networks (GNN), Petar mentioned Halicin antibiotic discovery by MIT and Google Maps expected time of arrival optimisation by DeepMind, delivered with Petar’s contribution.

An interesting question is whether we can also utilise GNNs in abstract domains such as pure mathematics? Together with a group of mathematicians, Petar checked it for a long-standing open conjecture (40 years without a significant progress!) from Representation Theory. The scientists wanted to understand a relationship between two objects, where one of them could be represented as directed graph – ideal to utilise GNNs. The method chosen allowed them to analyse and interpret the outputs with the use of attribution techniques. Such techniques help to understand what features or structures are relevant to the prediction. The group managed to discover two important structures, which finally led to a mathematical proof.

Their work proved that AI can inspire and assist humans, even in a very abstract domain, because it augments and guides the domain search. Empowering human intuition, rather than providing an explicit answer, can have a very powerful impact in the end.

Do machines see like humans?

Artificial neural network is an example of an algorithm highly inspired by nature, i.e., biological neural networks. It loosely models the work of neurons in a biological brain. This method turned out to be a very powerful tool which can solve various problems, from understanding text to interpreting speech and recognising images. But does the human-like representation strictly imply that machines cognition and human cognition pay attention to the same characteristics of an object?

Matthias Bethge, Professor of Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, decided to verify some inductive priors in computer vision. He focused on misalignment between human and machine decision boundaries, which basically means he examined images which were easy to recognise by humans but difficult for convolutional neural networks (CNNs) and vice versa.

One of the inductive priors that was researched was texture-based classification. The scientist checked the prediction performance of texturized images (generated from original texture synthesis) and benchmarked it with original image predictions. It turned out that this transformation didn’t deteriorate the results. As long as texture is the same as original, the algorithm performed well. Hence, he decided to go a step further and constructed a dataset with elements that combined texture of one class with a shape of a different class (for example representing shape of a cat on an elephant’s texture).

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He then compared a fraction of objects correctly classified by shape and a fraction of objects correctly classified by texture. It turned out that humans rely almost exclusively on shape, while CNNs were more biased towards using texture information. If CNNs rely strongly on texture, this implies they are also more vulnerable to texture changes. Hence, we can improve the performance of machines by feeding a model with a training set augmented by randomised textures (also generated with the use of NNs).

What Matthias Bethge has shown is that we can move closer to the intended solution by comparing machine cognition with human cognition. In his work, he researched many other approaches which make machine decision-making more human-like. He constantly proves that crossover between neuroscience and machine learning can significantly empower the latter one.

What can you infer about society by analysing ML models bias?

During the poster session, there was a poster which particularly attracted my attention. It was a poster co-authored by Adam Zadrożny from National Centre for Nuclear Research and University of Warsaw, and Marianna Zadrożna from Academy of Fine Arts. The researchers examined text-to-image models, trained on the datasets of images and captions crawled from the Internet. They analysed results of DALL-E mini model, which in contrast to DALL-E and DALL-E 2 is more prone to pick up bias from the original datasets.

Bias can be seen as a drawback of the model, but it can turn into a research tool for a much broader topic, which are misconceptions consolidated in society. The researchers generated images based on prompts linked to health. What they discovered was that for example, the words ‘autistic child’ returned only pictures of boys, as if girls didn’t suffer from autism. They also checked prompt ‘person with depression’, which returned pictures of young adults. This made them think whether in our collective imagination we take into account that depression can also occur among old people?

These are just two examples, but you can find more of them by checking results of DALL-E mini on your own.

Is ML in PL conference worth attending?

Definitely yes! I’d recommend this event to everyone interested in machine learning. It provides a lot of inspiring talks, allows getting to know state-of-the-art techniques, and is a great occasion to exchange thoughts with other community members. The thing I like most about this event is that it strongly expands our horizons.

See you next year!

Data Science and Engineering

Process data, base business decisions on knowledge and improve your day-to-day operations.

Let’s work together

Before we dive deeper into the actual topic, it would be good to take a step back and describe what an MLOps actually is. Following Google Cloud Docs:

The aim of MLOps is to take care of the entire ML solution lifecycle in production, by monitoring and automating all steps of the solution. And by saying all steps, we don’t mean just the ML model. In fact, an ML solution is composed of a vast and complex range of surrounding elements, such as data collection, data verification, model analysis, testing and debugging, serving infrastructure, monitoring, and many more.

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MLOps – going beyond PoCs

As MLOps is still quite a new concept, there aren’t many people specialising in it. Currently it’s a niche area, but likely to grow very quickly to accommodate huge demand for commercial applications; in fact, much quicker than might be expected. In the times of widespread digital transformation, especially in the post-covid reality, and the emerging need for more sophisticated data processing tools, such as ML methods, it’s just a matter of time when companies that are already building ML solutions, will have to go beyond the Proof-of-Concept stage and move on to productionise their ML solutions. But before all that hype comes to pass, MLOps can simply be composed of IT-world hipsters filling an awesome niche. Sounds fancy, right?

The art of anticipation

Mathematicians are well-known for their rigorous attitude to solving problems. Physicists for example are most often satisfied with MVPs like ‘rule works for n=3 dimensional space’, while mathematicians in order to acknowledge such a rule, would start checking if the rule still applies if they take any n∈ℕ. They will try to verify as many assumptions as possible to prove that the solution is working properly. At least that’s what they have been taught for so long.

You might start wondering now, ‘Okay, the above makes some sense, but how dare we require MLOps to be pessimists?’. A well-productionised ML system should be prone to bad scenarios and deal with them efficiently. And who’s better at foreseeing worst-case scenarios than pessimists? This should be a special type of pessimist, though. I would call that type an ‘active pessimists’. An active pessimist, as opposed to a ‘passive pessimist’, not only anticipates a possible problem but also prepares an effective solution to it. This natural characteristic of predicting what can go wrong in the process may be of much help in creating robust ML systems.

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MLOps & DevOps

Last but not least, many graduates of mathematics end up working with data: either as data engineers, data analysts, data scientists, or ML engineers. That’s not a surprise since they are believed to be most exposed to numbers and calculations, as compared to other people. The more knowledge they have about different stages of data solution, the better.

However, practice shows that in most cases, hardly anyone touches the deployment stage. ML solutions, when productionised, are quite often at the MLOps Level 0, which is basically a manual process.

But here’s the catch: DevOps is a broad field that needs to be explored first if one wants to become an MLOps. Consequently, I believe that the experience in working with data at different stages with all the above-mentioned aspects make such DevOps adept an even better MLOps engineer.

Did you get fed up with hyperparameters tuning of your models? Maybe it’s time to dive deeper in DevOps. Your top skills and inborn traits might be just the perfect fit for this feat.

Sources:

• https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
• https://blogs.nvidia.com/blog/2020/09/03/what-is-mlops/

Achieve business goals with the right experts

Engage a team of talented software development engineers or specialists in chosen technologies.

Accelerate your success

That’s an IBM definition. Machine Learning is a complex concept, so before deciding to leverage any ML solutions, it’s wise to take a step back and ask yourself a few questions first.

The basics

• Do you have any data to work with? If so, is your data structured or unstructured?
  
  You need to be fully aware of the kind of data that you have in order to know how it should be applied. Structured data is clearly defined, stored in tabular forms and easily searchable (like customer phone numbers or transactional information). Unstructured data is not organised, and is stored in its native format (like audio files or images).
  
  
• Do you understand your data and the processes that you want to optimise?
  
  You should be able to separate right from wrong and know which pieces of data are actually relevant to the processes that require optimisation – processes that should also be known inside-out themselves.
  
  
• Does your data storage architecture provide seamless data usage?
  
  You should have convenient yet secure access to data, permitting easy collaboration between data engineers.
  
  
• Have you already reviewed your current reporting system?
  
  Maybe all that you need to begin with is simple analytics instead of complex machine learning.
  
  

Addressing a challenge

• Can you identify a business issue that could be solved with the use of ML?
  
  It’s essential to know exactly what you need machine learning for and the effects that you expect. This will be helpful later on, when the time comes to measure the results of your investment and make any adjustments to your strategy.
  
  
• Do you have sufficient data, or maybe you need some external data sources?
  
  Very often, companies need to reach for public government data or use social media analytics tools to gather more of it. This isn’t exactly rocket science, but it adds another layer to your business and technological process.
  
  
• Are you at a sufficient level of expertise to solve this task? Do you have enough resources?
  
  Maybe you will need to collaborate with an external IT partner or hire additional experts to evoke the full potential of ML. Plus, a tandem of data and machine learning engineers are often needed in order to implement ML solutions correctly, so you need to take this into consideration as well.
  
  
• Should this be a one-off like a discovery experiment, or a solution that will be repeated?
  
  If it’s the latter case, you will need to think about how to maintain the solution, which can sometimes be trickier and more time and resource-consuming than the ML solution itself.
  
  
• Are you able to build infrastructure for the solution?
  
  This is also an HR-related question. It’s very likely that your IT team is not capable of creating an efficient infrastructure on their own, especially if they have little to no experience with ML.
  
  
• Can you take the risk of failure?
  
  The initial phase of the ML solution is always an experiment. It requires multiple attempts at parameter tuning or making several changes to the original model. Due to the very specific nature of the ML solution development process, one would need to be conscious of the fact that there are situations in which we may not be able to receive a complete answer to our original question, though this doesn’t mean that we can’t still benefit from the insights that we’ll have gathered along the way.

What if you don’t have all the answers?

You might not be able to answer some of the above-mentioned questions. For example, you may not fully comprehend the nature of your processes, lack sufficient data, or not have all of the resources needed to implement a desired ML solution. But should this hold you back from investing in this kind of technology? Not necessarily.

Nowadays, there are many ways to bridge certain gaps:

• If you need to enrich your data, because the pieces that are at your disposal are insufficient, you can turn to external sources. Companies like Google or Facebook offer access to the data that they are constantly gathering, and you can either use this as a complementary source or build your own solution on top of their data with the help of an IT Partner. Of course, this would only apply to a certain group of specific problems.
  
  
• If your objective for using ML is not well-defined, thing about workshop with technical and business experts to understand your data better and identify possible ways to utilise ML in your organisation.
  
  
• If you don’t have the in-house resources needed to build a solution, there are outsourcing companies with experience providing either small parts to a solution or entire solutions on their own.
  

Of course, you may also come to the conclusion that machine learning is not something that you need to implement at this very moment.

A thorough evaluation of your situation is a must, in order to avoid putting all your resources into something that is not going to bring any additional benefits to your business.

Wrap-up

There are many cases in which a business is ready for machine learning. Answering the questions that I laid out above is critical in terms of getting the lay of the land and seeing how many requirements need to be fulfilled. This will allow you to estimate your costs and compare them to the expected gains from implementing ML, both in the short and long run.

However, if you are struggling with your evaluation – feel free to reach out to us, and we will help you see the bigger picture as well as all the finer details.

Data Science & Engineering 

Process data, base business decisions on knowledge and improve your day-to-day operations. 

Engage Data Science