AI, Machine Learning and Data Science – What they are and how they work together
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Artificial Intelligence, Machine Learning, Deep Learning, Data Science… these buzzwords are everywhere, but they often get mixed up. It seems to us that many companies and insitutions think they “should be doing something with AI” but aren’t sure what that actually means, where to start and what their benefit would be. And if there even is a real benefit or if those technologies are only hype after all - a belief that makes them miss out on great opportunities.
At Askim Digital Solutions, we believe clarity is the first step to progress. So let’s break down these concepts, show how they connect, and explain the role of Data Science in making them useful for your business.
AI is the broad field of creating systems that can perform tasks previously requiring human intelligence. This could be anything from the topics that recently got a lot of media attention, like generating high quality images, understanding language, making recommendations of movies on Netflix, or controlling a self-driving car. But AI also includes seemingly less fancy areas, like clustering large amounts of feedback into different topics or sentiments, anomalie detection in payment processings and recognition of industrial machine condition patterns that in the past lead to machine failure.
Think of AI as the goal: building machines that can perceive, identify certain conditions and circumstances, and potentially act upon those by sending an early warning . Machine Learning and Deep Learning are simply two approaches to reach that goal.
Our role in this is to help you identify where AI can add value to your institution—whether that’s automating decisions, personalizing customer experiences, or predicting trends—so you don’t waste time chasing “AI hype” that doesn’t fit your business.
Machine Learning is a subset of AI that focuses on algorithms that learn from a greatamount of data and then improve automatically through experience. Instead of writing a fixed set of rules, you give the machine data, and it learns patterns from that data to make predictions or decisions.
Here are some practical examples:
1) Predicting product demand, which can be used to reduce overstock or stockouts
2) Detecting fraudulent transactions to prevent execution in real time
3) Recommending similar products based on purchase history to improve customer experience and customer loyalty
At Askim Digital Solutions, we are specialized in designing ML models tailored to your needs, test them rigorously, and integrate them into your workflows so they deliver measurable impact and benefit your entity long term.
Deep Learning is a specialized branch of Machine Learning that uses neural networks with many layers. Those layers each have a specific task in processing the data and thus are the reason why this approach is called “deep” learning. It excels at processing large amounts of complex, unstructured data such as images, videos, or natural language, but time-dependent data is a typical use case as well.
Some examples for deep learning tasks are:
1) Object Detection. This could be used in security or monitoring applications, for example to detect if a person enters the company ground at night or for automated cattle counting.
2) Smart grid fault detection. Identifying unusual patterns in voltage or current measurements to act upon before they turn into costly problems.
3) Route optimization with dynamic conditions. Learning from GPS traces and real-time conditions to suggest better routes.
4) Autonomous fleet scheduling. Managing complex delivery schedules under changing constraints.
We guide you on when Deep Learning is the right tool—and when it’s not. These models can be powerful but also resource-intensive, so we make sure your problem, data, and infrastructure align before investing in them.
While AI, Machine Learning, and Deep Learning describe specific technologies or approaches, Data Science is the overarching discipline that turns those tools—and your data—into actionable strategies.
Data Science is not just about algorithms. It’s about asking the right questions, finding the right data, applying the right methods, and delivering the right insights to the right people at the right time.
A good Data Science process typically follows a cycle:
1) Business Understanding
Defining the exact problem to solve, the decisions to support, and the success metrics.
2) Data Acquisition & Preparation
Identifying relevant data sources (internal systems, sensors, third-party providers), cleaning them, and ensuring they’re in the right format for analysis.
3) Exploratory Data Analysis (EDA)
Understanding the patterns, anomalies, and correlations hidden in the data.
4) Model Selection & Development
Choosing whether the solution needs simple analytics, Machine Learning, or Deep Learning—based on business needs, not hype.
5) Validation & Testing
Ensuring results are accurate, reliable, and free from bias.
6) Deployment & Integration
Making sure insights and models are embedded into business processes, so they don't sit in a report no one uses but are of actual use.
7) Monitoring & Improvement
Continuously refining solutions as conditions and data change.
Unlike a purely technical role, Data Science requires bridging three worlds:
I) Business knowledge: Understanding the industry context, challenges, and priorities.
II) Analytical expertise: Applying statistical reasoning, predictive modeling, optimization methods and interpreting the results correctly.
III) Technology know-how: Leveraging tools, infrastructure, and automation for scalability.
A Data Scientist makes sure the problem, the data, and the solution are all aligned with the desired business outcome.
At Askim Digital Solutions, we focus on long term impact and business benefit. That means:
We clarify your goals before we write a single line of code.
We recommend the right level of complexity—sometimes you need AI, sometimes a simple rule-based system will save more money.
We advise on infrastructure choices (cloud, databases, sensor networks) without pushing you into costly setups you don’t need.
We integrate insights into your workflow—so results actually drive daily decisions.
We train your team to use and interpret data tools confidently.
Email us:
team@askimds
Whatsapp or call us:
+595 982 26 80 05
