Connect with us


Data science meets DevOps:
7 rules of machine learning



By: Yotam Yarden, senior data scientist at Amazon Web Services (AWS)

Machine Learning capabilities hold great potential for new revenue streams and tremendous cost savings for enterprises. Increasingly, businesses are using ML to strengthen their competitive advantage and drive innovation. Is your organization embracing this shift or are you falling behind? If you are on the “bias-for-action” side of the scale and have already started steering your organization towards digital & ML transformation, are you confident you are doing so in the right way?

Over the past decade, data has become increasingly important and has even been described as the “new oil”. Organizations with extensive user data can leverage data to increase sales and customer retention. Data of machinery can be leveraged to improve machines utilization of manufacturers. Computer Tomography images can be used to identify cancerous tumors. There is literally no industry segment which can’t leverage data to improve and create new business models. Meanwhile, data has never been easier and less expensive to collect, store, analyze, and share. Many enterprises are building their data lakes today precisely for this reason. But, is your organization taking full advantage of its data? Are you satisfied with the value you generate from your data? Do you struggle with building smart applications on top of your data lake? Big Data but not enough insights? Too much talking, not enough walking?

If so, consider the following tips:

  1. Be business driven and customer focused: What are your organization’s biggest challenges? Start from a focused business challenge and work backward towards a solution. Too many companies try to apply “self-driving cars” or “genome-sequencing” algorithms to a sales funnel optimization challenge just because they hired an expert in this field, while often there are models that better fit the task and bring higher value at lower costs. Don’t keep your data science team in the IT department alone. Rather, giving ownership of the data science team to a business stakeholder can invigorate your organization, and unlock new revenue streams and tremendous cost savings.
  2. Iterate fast and simple: Be quick and decisive about bringing your ML system into production. Conducting small iterations through tests, proof of concepts and pilots will help your team to bring ML workloads into production faster, and in a higher quality. Plan to have a production-ready prototype in 3 weeks, and a fully operational version in under 90 days. Even if your system is not using the state-of-the-art model, you will learn far more by iterating quickly than you would from an overly-long development cycle. ML transformations happen by building knowledge and experience through small, fast, and simple steps, rather than by multiple year planning. A redesign is inevitable. Only by experimentation, experience, and adaptation, can you realize the full potential of your ML product. Fail fast and improve often.
  3. Centralize or Decentralize ML teams? Centralize ML teams when necessary, but aim to decentralize when possible. ML applications, like any other piece of software, require maintenance, updates, and support. A centralized team may be effective at low-scale, but once you start expanding, innovation might suffer. Imagine a large innovation team who is working on multiple innovative projects, it is inevitable that at some point a substantial portion of the team’s work would be operating ongoing projects. It then might be a good time to distribute the team to its real home, within the business unit that it serves. It can be hard to ”give away” your “baby”, but it will help your ML team innovate on behalf of your customers.
  4. Consider the biggest roadblocks for data scientists & developers[1]: 1) dirty data, e.g. data sets which are unstructured, have missing attributes, and mixed data types in the same section; 2) lack of talent; 3) lack of management or financial support, as ML projects require focus and funding, organizations struggle to roll-out such a project without its management’s support; 4) lack of a clear questions to answer. Organizations are chasing improvement but are lacking specifications and clear targets to achieve them; 5) data not available or difficult to access. If you plan appropriately, you will find that most of these roadblocks are easily overcome. Lack of talent? Start hiring talent ahead of demand rather than have the data waiting for talent. Data not available? Start collecting data in advance of the project kick-off. Data not accessible? Don’t kick-off a workshop without first obtaining relevant data samples. Lack of management or financial support? Get the buy-in in advance. Find the stakeholders’ heroes who are enthusiastic about AI and can support you with budget & headcount approvals, data accessibility, and connections to other business stakeholders.
  5. The separation between Data Science and DevOps is over! “Our PhDs develop ML models and write specifications for our developers to implement in C++.” If you can relate to this customer quote, start changing your team’s structure today. There is a wide range of tools that enable data scientists to take a step towards engineering, and vice-versa. The separation of “science” and “production” can prolong your company’s development & innovation cycles, thus leading to quality and ownership issues. Thankfully, technology is evolving at an increasing pace and new tools are continually released. It has never been easier for experts to expand their capabilities and cross over into new domains.
  6. Keep the right Data Scientists/Data Engineers ratio: What is the optimal Data Scientists/Data Engineers ration? For most customers, the answer will depend on the maturity of the business. If your data are not accessible or you don’t maintain and track your data, you will likely need more engineering and less science. On the other hand, if you already have an established data pipeline, data warehouse, and data lake, you will likely want more science and less engineering. In some cases, your business will have specific requirements, which can affect the skills needed as well. As a rule of thumb, plan to have 2-3 engineers for every data scientist in the building phase, and 1:1 when a system is already deployed.
  7. Have clear KPIs (Key Performance Indicators)by which your project’s success can be measured. For example, imagine a Recommendation Engine project for an online media company. “Enhance user experience” might be a great goal, but without a way to measure success, this objective is overly ambiguous. Stakeholders might even disagree over whether the goal has been met, which can cause wasted resources and inefficient development. Can “enhancing the user experience” be measured by time spent on the platform? The number of videos watched? The number of new categories explored by the user? Each measure could lead to a different recommendation system.

Having clear goals & KPIs will help you plan and execute more effectively:

ML initiatives are exciting and can be extremely fruitful. However, lack of focus, limited resources, and improperly set of expectations can cause anxiety. Holding a “ML Discovery Workshop”, in which all stakeholders, both business and technical, brainstorm ideas, discuss their company’s biggest challenges, and plan can help enormously. During the workshop list all of your biggest challenges, their feasibility, estimated efforts, and missing skills and tools, and come up with a list of projects and a concrete execution plan. However, even the most well-intended execution plan will flounder without proper focus. With this in mind, remember: Be Customer Focused, Iterate Fast, Distribute data science when effective, Plan for roadblocks, Staff appropriately, and Choose specific KPIs that matter.

The writer is a senior data scientist at AWS and has been helping enterprises with their machine learning and cloud journey.

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.


Samsung clears the table with new monitor

For those who like minimalism and tidy desks, Samsung’s new Space Monitor may just do the trick, writes BRYAN TURNER.



The latest trends of narrow-bezels and minimalist designs have transcended smartphones, spilling into other designs, like laptops and monitors. 

The new Space Monitor line by Samsung follows in this new design “tradition”. The company has moved the monitor off the desk – by clipping it onto the edge of the desk.

It can be put into three configurations: completely upright, where it sits a bit high but completely off the desk; half-way to the desk, where it is a bit lower to put some papers or files underneath the display; and flat on the desk, where it is at its lowest.


The monitor sits on a weighted hinge at the edge of the desk, providing sturdy adjustment to its various height configurations. It also swivels on a hinge at the point where the arm connects to the display. This provides precise viewing angle adjustment, which is great for showing something on screen to someone who is standing.

Apart from form factor, there are some neat goodies packed into the box. It comes with a two-pin power adapter, with no adapter box on the midpoint between the plug and the monitor, and a single cable that carries HDMI-Y and power to prevent tangling. 


However, it’s slightly disappointing that there isn’t a Mini Display Port and power cable “in one cable” option for Mac and newer graphics card users, who will have to run two cables down the back of the screen. Even worse, the display doesn’t have a USB Type-C display input; a missed opportunity to connect a Samsung device to the panel.

A redeeming point is the stunning, Samsung-quality panel, which features a 4K UHD resolution. The colours are sharp and the viewing angles are good. However, this display is missing something: Pantone or Adobe RGB colour certification, as well as IPS technology. 

The display’s response rate comes in at 4ms, slightly below average for displays in this price range. 

These negatives aside, this display has a very specific purpose. It’s for those who want to create desk space in a few seconds, while not having to rearrange the room. 

Final verdict: This display is not for gamers nor for graphic designers. It is for those who need big displays but frequently need to clear their desks.

Continue Reading


Can mobile fix education?



By Ernst Wittmann, global account director for MEA and country manager for Southern Africa, at TCL Communications

Mobile technology has transformed the way we live and work, and it can be expected to rapidly change the ways in which children learn as smartphones and tablets become more widely accepted at primary and high schools. By putting a powerful computer in every learner’s schoolbag or pocket, smartphones could play an important role in improving educational outcomes in a country where so many schools are under-resourced.

Here are some ways that mobile technology will reshape education in the years to come:

Organisation and productivity

For many adults, the real benefit of a smartphone comes from simple applications like messaging, calendaring and email. The same goes for schoolchildren, many of whom will get the most value from basic apps like sending a WhatApp message to friends to check on the homework for the day, keeping track of their extramural calendar, or photographing the teacher’s notes from the blackboard or whiteboard. One study of young people’s mobile phone use in Ghana, Malawi and South Africa confirmed that many of them got the most value from using their phones to complete mundane tasks.


One of the major benefits smartphones can bring to the classroom is boosting learners’ engagement with educational materials through rich media and interactivity. For example, apps like Mathletics use gamification to get children excited about doing mathematics—they turn learning into a game, with rewards for practicing and hitting milestones. Or teachers can set up a simple poll using an app like Poll Everywhere to ask the children in a class what they think about a character’s motivation in their English set-work book.


Mobile technology opens the doors to more personalised and flexible ways to teach and learn, making more space for children to work in their own style and at their own pace. Not very child learns in the same way or excels at the same tasks and subjects – the benefit of mobile phones is that they can plug the gaps for children seeking extra enrichment or those that need some additional help with classroom work.

For example, teachers can provide recommended educational materials for children who are racing in ahead of their peers in some of their subjects. Or they can suggest relevant games for children who learn better through practical application of ideas than by listening to a teacher and taking notes. 

In future, we can expect to see teachers, perhaps aided by algorithms and artificial intelligence, make use of analytics to track how students engage with educational content on their mobile devices and use these insights to create more powerful learning experiences. 


South Africa has a shortage of teachers in key subjects such as mathematics and science, which disproportionately affects learners in poor and rural areas. According to a statement in 2017 from the Department of Basic Education, it has more than 5,000 underqualified or unqualified teachers working around the country. Though technology cannot substitute for a qualified teacher, it can supplement human teaching in remote or poor areas where teachers are not available or not qualified to teach certain subjects. Video learning and videoconferencing sessions offer the next best thing where a math or physical science teacher is not physically present in the classroom.


Knowledge is power and the Internet is the world’s biggest repository of knowledge. Schoolchildren can access information and expertise about every subject under the sun from their smartphones – whether they are reading the news on a portal, watching documentaries on YouTube, downloading electronic books, using apps to improve their language skills, or simply Googling facts and figures for a school project.

Take a mobile-first approach

Technology has a powerful role to play in the South African school of the future, but there are some key success factors schools must bear in mind as they bring mobile devices into the classroom:

  • Use appropriate technology—in South Africa, that means taking a mobile-first approach and using the smartphones many children already know and use.
  • Thinking about challenges such as security – put in place the cyber and physical security needed to keep phones and data safe and secure.
  • Ensuring teachers and children alike are trained to make the most of the tech – teachers need to take an active role in curating content and guiding schoolchildren’s use of their devices. To get that right, they will need training and access to reliable tech support.

Continue Reading


Copyright © 2018 World Wide Worx