DEV Community: Daniel Ndukwe

AI Orchestration: The Human Job in the days of AI

Daniel Ndukwe — Sun, 08 Mar 2026 15:54:39 +0000

AI Orchestration: The Human Job in the days of AI

In recent years, we have seen the rapid development and improvement of Artificial Intelligence Models particularly Large Language Models and other Multimodal Models. But in 2026, we saw an explosion of these tools and models having the capabilities of doing extra human work faster and cheaper. To be very honest, this has led to lots of professionals panicking and asking the most pertinent economic question right now

"WILL AI TAKE MY JOB?"

Simple answer? Yes. Full stop!.

But!

There's a catch and possibly a glimmer of hope somewhere.

Now i am no thought-leader in the affairs of the economy or even A.I (I still learn everyday and some times it is hard to keep up) and i also have fears about job displacement but i think the answer to that question of AI taking over jobs is 'AI Orchestration'.

Now what is AI orchestration?

AI orchestration in my opinion is the coordination and management of AI models, integration of AI agents to the workflow of humans. It covers the efficient implementation, integration, deployment and maintenance of the components in a greater AI system.

So i said to myself, maybe my job description has now changed from the human builder to the human in the loop. I no longer get to just write out codes, solve mathematical problems, but rather get to plan, organise, iterate, coordinate and monitor the automation process of these AI tools because despite the advancements in these tools, the understanding of the human wants and needs are complex and the contexts of these needs can only be understood by humans themselves. So here's the shift in my mindset:

Automation of technicalities
Firstly, the technical stuff (though still important to understand for proper supervision) has been largely automated and we no longer need to deal with the constraints of this. So we move towards what matters the most when building products.
Choosing the right goals
Secondly, choosing the right goals has become increasingly important (Always has been anyway) but as mentioned in the previous texts, the human mind is complex and requires a lot of context and perspective from an experiential standpoint of the real-world rather than a descriptive standpoint from multiple data sources. Therefore, the human job becomes like this:
Define the problem
Choose the right tradeoffs acceptable
Define what a success metric should be
Defining Values, Constraints and Ethics
AI Orchestration amplifies power. Humans get to set the boundaries on what is allowed such as:
The data allowed
Actions prohibited
Risks involved
What should never be optimized for

Well till Artificial Superintelligence comes, i believe any technical role of humans is still very important only that it has been distilled into the role of an Orchestrator.

Interesting times

Data Systems

Daniel Ndukwe — Fri, 30 Jan 2026 19:17:26 +0000

In an era where software is being redefined i.e moving gradually away from human programming to more system building, it is imperative that one understands the system thinking and architecture behind modern software. Modern software is meant to be Reliable, Scalable and Maintainable

Many modern applications and Machine Learning models built in production are data intensive not necessarily compute intensive. Although recent advancements in Generative AI requires lots of compute. This means that for a lot of modern applications, the hardest part isn’t doing the heavy mathematics or CPU work, it is handling lots of data successfully.

Data Intensiveness is about storage, reading/writing data fast (Database Queries), Moving the data (APIs, distributed systems), keeping the data correct when there are many users acting at once and processing streams .

For Compute intensive applications and systems, they are about 3D rendering, Training deep learning models, Scientific simulations, and Video encoding. In Machine learning, the hardest part of getting a good ML system isn’t the model math, but the data quality. This includes the proper data collection, Data cleaning, labelling, storing, versioning and serving data correctly.

Data-heavy applications need the following:

Databases
Caches
Search Indexes
Stream Processing and Batch Processing

In the current systems for building applications and running ML models in production, many new data storage tools and processing have emerged.

MODERN DATA SYSTEMS ARCHITECTURE

The tools used in modern data systems architecture includes:

Primary Database: This is the source of truth (The real, Official data users, Orders, Posts etc.)

In-memory Cache: Very fast storage (Redis) to speed up the reads

Full-text index: Specialized search engine for fast-text search

Message Queue: A system of asynchronous tasks without slowing down the user request

Application Code: The stitching layer that talks to all of them and coordinates updates i.e. they are the bridge between databases, data warehouses, data streams or data lakes and the business models e.g. Python, SQL Queries. Therefore, in modern data systems architecture, the application code coordinates multiple specialized components to handle distinct tasks. It works as thus:

Client → Application Code (Python Script or SQL) → In-Memory Cache (If cached, read directly from the cache. If not Cached, Proceed to the primary database) →Primary Database.

Data Science: 21st Century’s ‘Sexiest Job’

Daniel Ndukwe — Tue, 29 Jul 2025 11:56:17 +0000

In October 2012, Data Science was famously tagged the “sexiest job of the 21st century” in a Harvard Business Review article by Thomas H. Davenport and D.J. Patil. Fast forward to the year 2025 and this career has seen an exponential growth and increase in demand and appeal for both top companies looking to drive revenue with data driven applications and undergrad pivoting more and more into these majors at top universities and polytechnics in the world. But what even is data science?

Data science is the application of statistical and computational techniques used to address or gain valuable insights into a real world problem. As simple as that definition of this ‘Sexy job of the 21st century is’, it packs a lot of complexity that may be intimidating to the ‘average joe’ out there. Despite its complexity, this endeavor has seen even more appeal over the years particularly with the advent of generative Artificial Intelligence (A niche within data science that uses machine learning and deep learning to communicate like humans through the use of Natural Language Processing)

But what does the data science workflow look like and what tools are used in this really exciting career path. Let’s explore this

The Data Science workflow usually starts with the understanding of the business problem. For example, A company wants to find out which advertising platform generates a lot of traffic for their product and which they should put more budget for their advertising into. Whether it is TV, Social Media or Traditional forms like Paper print and billboards. Your understanding of this problem sets the tone for more actionable insights into providing a solution.

The next involves data collection. This can be done by either collecting the data from web sources through the use of web-scraping and API calls and on-site or manual collection of data particularly those working in the geospatial industry.

Another step in the data science workflow involves the Data processing or Data pre-processing. This is the most crucial step into any data science career because real-world data is messy i.e. it contains lots of errors, missing values, duplication and redundancy. The process involved in this is usually called ‘Data Cleaning’.

Furthermore, the exploration of the data which involves checking for outliers, calculating the measure of central tendency (Mean, Median and Mode), Pearson's correlation techniques and several visualisation techniques used is another part of data processing. Also in feature engineering which simply means choosing the right features in the dataset to improve the performance of machine learning models, is done as par t of the process of transforming raw data into meaningful insights.

Machine learning, a subset of Artificial intelligence, is a technique where the machine learns from previous data to make a prediction. Think of it as a student studying for a future exam and using lots of Past Questions to get the mode and structure of the future exam. Machine learning teaches the computer (through your preprocessed data) to learn from the data so it can make valuable predictions or insights. Knowledge in statistics, calculus and probability are mostly key in this aspect.

The final workflow i’ll be talking about is ‘Visualisation’. This involves the process of turning data/numbers into charts and graphs to capture the imagination of what the data looks like. Techniques such as Bar Chart, Histogram, Box Plot, Violin plot etc. is usually used in this aspect.

There are other aspects of the data science workflow that helps to get data driven insights about a business problem. They may include Data story-telling, statistical learning, presentation skills et al.

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# load the titanic dataset
titanic = pd.read_csv('titanic.csv')
titanic.head()

# Check the shape of the dataset
titanic.shape

# Check the info
titanic.info()

# check for the missing values
titanic.isnull().sum()

Tools Used for Data Science
Programming Knowledge (Python, R, Julia, MATLAB)
ML Algorithms (Scikit-learn, Matplotlib, Seaborn, Pandas, Numpy, Scipy etc)
Visualisation tools (Tableau, PowerBI, Python)
Databases (SQL, NoSQL[MongoDB])

In October 2012, Data Science was famously tagged the “sexiest job of the 21st century” in a Harvard Business Review article. https://medium.com/@Thanyearl/data-science-21st-centurys-sexiest-job-c2e167f5f2ef

Daniel Ndukwe — Tue, 29 Jul 2025 11:53:09 +0000

Is Data Science is machine learning?

Daniel Ndukwe — Tue, 29 Jul 2025 00:25:56 +0000

Machine learning typically and primarily involves being concerned with the development of new algorithms and model building while Data science is concerned with answering scientific question to solve business or real-world problems.
While Data Science uses Machine learning algorithms to draw insights from raw data to results, although used interchangeably, data science is not machine learning.

Transformers

Daniel Ndukwe — Thu, 24 Jul 2025 01:28:44 +0000

"In 2017, the google brain and google research team introduced a paper to the world with the title 'Attention is all you need'. This paper highlights how the recent architecture, that powers current LLMs, works under the hood. The transformer model architecture replaces RNN."
https://www.linkedin.com/posts/daniel-ndukwe-2a9b6663_transformers-architecture-with-the-rise-activity-7351678448135790595-xCJh?utm_source=share&utm_medium=member_desktop&rcm=ACoAAA2FBCEBfALVe1r1vpKHAMm7LvYb5HUxDb8

YOU ARE THE JOB NOT THE TOOLS

Daniel Ndukwe — Thu, 24 Jul 2025 01:16:07 +0000

In our rapidly growing world, automation is taking over, and lots of people think learning tools, rather than the principles that governs a career, is what fast-tracks that career.

The tool doesn't make the career.

You are not a good driver because you can move a car; you are a good driver because you follow the principle of safe and smooth driving. It doesn't matter whatever vehicle it is.

Likewise the current tech and data industry trend. Python, SQL, Javascript, Scala, Apache etc while being a must have skill in our vastly automated society today, the basic principles that guide the industry is what makes you go from good to great. In our rapidly growing world, automation is taking over and lots of people think learning tools over principles that governs a career is what fast-tracks that career.

The tool doesn't make the career.

You are not a good driver because you can move a car. You are a good driver because you follow the principle of safe and smooth driving. It doesn't matter whatever vehicle it is.

[Boost]

Daniel Ndukwe — Mon, 09 Jun 2025 03:37:39 +0000

Zekeriyya Demirci

Apr 5 '22

Traffic Data Analysis with Apache Spark Based on Autonomous Transport Vehicle Data

#python #mongodb #datascience #machinelearning

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