Know Your Process: How to Optimize Data Management with Machine Learning

Like with many projects, machine learning models require a clean, streamlined approach to succeed. This, in essence, means that preparedness is the key to crafting a viable and effective machine learning data collection. A solid foundation must first be built, then extended in a smooth and neat manner. Naturally, there may be obstacles and setbacks along the way, but these tips will help your machine learning engineers build a highly flexible and effective data management program.

Identify Objectives First, Then Base the Foundation on Them

To better understand what kind of data they’ll be looking for and how to best compile it, your machine learning engineers will need to identify the use case for which the data will be utilized. Once they know what goal or task they will need to accomplish, it will become easier to program a machine learning model to fulfill the needs of that task. Getting somewhere requires knowing where you’re going, after all; in the case of data management, the end goal is also the beginning of the machine learning model.

Know the Intricacies of and Reasons for Your Data

After the goal is determined, the next step is to understand which data sets will be necessary to accomplishing that goal. Some projects need real-time data updates, while others use sensor updates; the means of acquiring data are many, and engineers must know how to quantify their data before they build a model based on it. This naturally leads to the necessity of gathering data and the crucial aspect of analyzing usage data to induce the machines to learn and tweak their behavior.

Of course, this means that a thorough understanding of data – from how it’s gathered to how its reality affects the computing process – is essential to any data management project. Each objective will need a streamlined data pipeline and a deep well of data in a solid data platform. With the right platform and a well-crafted pipeline, your data feed will be accurate and fast, so your objectives will be met correctly and on time.

Prepare for Fast and Frequent Iteration in Testing and Production

As more data is collected, the quickness of machine learning will continually update and change your models, and the very real numbers conveyed through production may even alter outside factors, which must then be re-calculated for optimal accuracy. As your data and production environment change, they will influence each other and grow; the better your engineers’ machine learning model is, the more flexible and prepared for these changes it will be.

Change is Progress: Recalculate and Refine Your Data Streams

A well-build data management project can handle the changes in production and usage, and of course, real-time changes will affect your data inputs. This means you must constantly update your data sets as you go; the work may be almost unending, but your objectives demand constant learning from both people and machines in order to be met efficiently and accurately. Just remember that learning and adjusting are natural parts of progress – for you and for the machines.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

A Great Spread: 5 Fantastic Deep Learning Frameworks

An engineer’s mind can take them far, but for many tasks, an engineer is only as good as the tools that are currently available. This is where deep learning frameworks come into play as they provide engineers with the means to construct and tinker with many programs and applications. Like with all tools, some deep learning frameworks are better than others. Here is a handful of great ones that will see you through most machine learning tasks.

Starting Off Right: TensorFlow

Due to its Python foundation and its pre-loaded tutorials, TensorFlow is a great framework for amateur deep learning engineers. The framework is supported by Google, which is a huge vote of confidence, and its TensorBoard tool allows for accurate network modeling so your programmers can visualize the data they are working with. TensorFlow is available on desktop and mobile for maximum access, and it supports C++ and R, so it has a robust language network.

Millions of Pictures, Billions of Words: Caffe

When it comes to speed, Caffe is the top of the line. It is the fastest image processing framework available and can process 60 billion images in a day with an Nvidia K40 GPU. Its quick image processing enables it to recognize objects visually, but it does not perform as well at language modeling. Nevertheless, if you want a fast processor and great visual recognition software, Caffe is your best bet.

Complex Networks Made Simpler: The Microsoft Cognitive Toolkit

This framework uses Python and C++, so it is great for training machine learning beginners, and it is fairly easy to construct generative adversarial networks to facilitate more complex machine learning. A big bonus for the Microsoft Cognitive Toolkit is its top-end performance across multiple machines; it can scale its operation to larger networks quite well, and this makes it a valuable asset to large teams. The only downside is its limited mobile performance.

Deep Learning Gets Easier: PyTorch

PyTorch takes the scientific nuances and complexities of Torch and pares them back using Python, so it is easy to access – even for beginners. This means that it is easy to start learning how to build deep learning models with PyTorch, and once you have mastered its tools, it becomes possible to handle bigger and more complex computations. It is a flexible and powerful framework that is trusted by big companies like Facebook and Twitter.

Variety and Accessibility All in One: MXNet

MXNet, pronounced “mix-net”, is the catch-all framework for deep learning because it allows programmers to code in a wide variety of programming languages, including C++, Python, R, and Julia. No matter which programming language you are fluent in, you can build models in MXNet and hone your deep learning skills. It works fairly well on the go thanks to its portable library, and it can scale to several machines at once so big projects are possible.

Depending on your preferences and project needs, any of the above deep learning frameworks will handle the work you need to do. Choose the one you like the best and build with confidence.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

Engineer Your Best Self: How to Become a Great CTO

Being the chief of anything in a company is a demanding and challenging job, but it is a challenge that may be met and overcome with the right attitude and tools. A competent CTO has many tasks and responsibilities to juggle and must strive to combine technical and emotional intelligence as they help develop projects and keep their teams and themselves happy. To help with this monumental undertaking, we have tips on how to balance all your responsibilities with confidence and become an awesome CTO.

Promote a Healthy, Positive Culture, and Watch Your Team Grow

Culture is a big-picture concept, and it is one that should not be taken lightly. Too often, disgruntled employees and frustrated workers complain of an indefinable feeling of unfairness, discontent or meaninglessness at work. These are all general feelings and concepts that may not sound specific enough to actually put a dent in productivity, but the opposite is true. A rotten culture produces unhappy teams, and unhappy teams do not produce positive results. This means that promoting a positive culture wherein employees and managers talk to and listen to one another is critical to a CTO’s success. When your colleagues talk about big, nebulous feelings regarding work, they will ideally talk about feeling good about the work they do. A positive culture spreads good feeling around, and a great CTO will know when to sit back and let their employees create in the great culture they’ve established.

Trust Your Team, and Allow Them to Flourish

While it may be easy to fall back on micromanagement when a project approaches its deadline, using micromanagement as a crutch must be avoided by CTOs at all costs. The reality is that developers and engineers work best when they have some breathing room to create and let their minds tackle work the way they want to. There is no avoiding the responsibility of making critical decisions about projects, but there is always room for freedom to think, and this freedom will motivate your team to be productive and happy.

Living Life Outside Work Makes Everything Go Around

Work should not be everything in anyone’s life, and this is true especially for CTOs. Overworking and stressing yourself out will lead to fatigue and poor decision-making, which will ultimately affect your business. A good CTO finds a clear separation between work and home life and devotes themselves to enjoying relaxation time to the fullest. This might mean going for a walk every day, setting aside a family day every week, or going on every vacation to which you’re entitled. As long as you find a way to maximize your fun time and your work time, you’ll be refreshed and ready to handle crucial business processes.

Staying Productive: Maximize Every Minute of the Day

Getting up early, keeping a clean email inbox, and delegating tasks to your team may sound simple at first, but they are good steps toward freeing up more minutes of the day for critical thinking, and critical thinking keeps your workflow smooth. Little changes to your routine like taking notes while on a walk or strategically moving meetings to a new day helps a great CTO stay focused and as productive as possible.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

Into the Breach: Leadership Brings New Fears to the Table

Although much of the work done in the field of machine learning and AI is performed by teams, there is still a need for a leader to synchronize each team’s efforts and move a company forward as one. This could be the CTO, CIO, or the VP of engineering. No matter who it is, however, there is no denying that this individual faces challenges above and beyond those faced by members of a team, where backup is available. Here are the most common fears that leaders in machine learning companies experience.

Having No Control is No Fun

Many CTOs and leaders in the machine learning field come from backgrounds that put them in direct control over aspects of a project such as code or infrastructure. As the leader of an entire company, however, there are many factors that hover outside the control of a CTO – juggernauts such as media, public relations, investors, and the weather are just a few of the forces that a CTO cannot personally control. While these factors won’t necessarily destroy an entire company overnight, they have the power to damage a company to the point where repairs are difficult, and therefore the uncontrollable threats are the ones that machine learning leaders fear the most.

Scaling Properly for Growth Can Be Scary

When many CTOs start out, they are part of start-up companies or just forming a team from the ground up, so keeping things at a reasonable size is simple. However, as companies find success and skyrocket toward larger goals, growth can get out of hand. Finding the right balance is another common fear that CTOs face. Figuring out how many employees to hire while maintaining the original company culture and keeping everyone in a good mood requires calculations beyond those of machines. Choosing which and how many projects to develop is another aspect of scaling; it all comes down to how big of a task a company can handle, and how a CTO can guide their company’s growth, so it is natural, organic, and easy.

Good Culture Can Go Sour, and That’s a Nightmare

Many teams start out small, and this works wonders for maintaining a positive culture and good team synergy. People get along well in small groups, and if they don’t, it is simple to begin a conversation that will patch relationships up. When a CTO strives to cultivate a positive, friendly culture at a larger company, however, the task becomes more difficult. It is not so easy to keep a large team of people on the same page, and disagreements can easily go unchecked and unaddressed. Keeping all their colleagues happy and productive is one of the end goals of a CTO, so the difficulty in maintaining that positive team vibe is a sleep-destroying fear.

Ultimately, the CTO is just as much a part of their team as their colleagues and moving the company upward and ahead is the prime directive for any CTO. Balancing employee satisfaction with productivity and positive public reception sounds like a heinous nightmare, but the best CTOs devote themselves and their thoughts to keeping all these factors in a happy, productive balance.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

Keep the Monsters at Bay: How to Sleep Easy as a CTO

As the leaders of state-of-the-art machine learning and tech companies, CTOs have all kinds of challenges to overcome. Many of these challenges pose direct threats to the success of each CTO’s respective company, so they sometimes find themselves losing rest over the looming prospect of failure. Here is a quick look at a few of the biggest nightmare scenarios that keep CTOs lying in bed at night, and some tips for dealing with the problems.

Security is Key: How to Better Prevent Data Breaches

Over the past few years, bigger and more expensive security breaches have allowed hackers to access the personal information of millions of people around the globe. Accordingly, security is at the forefront of many CTOs’ thinking, and the fear of losing clients and patching huge security gaps is leading tech leaders into nightmare territory. Thankfully, research has indicated that many of the most vulnerable points in a company’s infrastructure are exposed during development, so the natural response is to increase the amount of security testing done during development. If security is increased and maintained even while development is ongoing, companies can expect to significantly cut down on the risk of data breaches.

Living on Cloud Nine: Meshing the Cloud with the Ground

Cloud-based servers have enabled companies to put their information within reach of globally scattered teams, which creates a new sense of connection while increasing the chance of missed connections and lost data. It is difficult to keep information pipelines clean and clear, and the scattered nature of the cloud is both a blessing and a curse; this is why many companies are turning to a more organic combination of cloud-based and real-world servers, extending their networks on the ground and in the digital space to improve the flow of information. As long as the best multi-cloud framework is constructed and maintained, CTOs should be able to rest easy.

Grassroots is Great, until it Prevents Organic Growth

In their beginnings, many companies operate with duct-tape solutions and cavalier equipment networks, patching disparate machines and programs together to make things work by the skin of their teeth. As these companies grow, however, the necessity of cobbled-together infrastructures falls away, and keeping the old patched machines is just a matter of nostalgia. To keep up with growth and development, a CTO must guide his team to upgrade their infrastructure, so every project and program runs smoothly and stays up-to-date.

Artificial Intelligence: There’s a Smart Way to Integrate It

As AI becomes the biggest development in the tech world, companies are scrambling to figure out how to handle it. Machine learning has the potential to increase work flow and promote better synergy throughout many companies, but the trick is in mastering the technology in a way that is not disruptive to the current business model. This means that CTOs must hire and train machine learning engineers and upskill their current employees in deep learning programs, so their companies remain cutting-edge and viable. Respectfully engaging with machine learning ensures that any CTO’s company may look forward to a bright future.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

Into the Future: How AI Changes Software Development

For now, most of the software development process relies on human direction to succeed. However, advances in the realm of artificial intelligence point to a future in which that is no longer the case. Ideally, the future is one that involves computers learning how to write their own computer programs and moving forward from there. Here are just a handful of ways that AI is transforming the field of software development.

Prototype Development Done Quicker

Typically, designing and planning new technology is a months-long process that can even stretch on for years before a single prototype is made. Artificial intelligence is helping to shorten this process by opening up the development field to more programmers and designers. Even designers who aren’t as technically-minded as others can design successful prototypes with the assistance of powerful AI programs.

Reading and Debugging Code Just Got Easier

A lot of time is devoted to solving problems within code and figuring out how to fix them, and AI programs can be trained to assist with these tasks. A well-trained AI assistant can sift through documents and code much faster than a person can, and such individuals can even give pointers and advice on how to best move forward when it comes to product optimization.

Learning from the Past Makes Error Correction Simple

With enough data and information about the current project, AI assistants can figure out what the most common errors and mistakes are and prepare to identify them during product development. This awareness extends beyond testing and development into launch as well, given that computers may be trained to detect errors during product use and may eventually be taught how to respond to errors in a proactive way.

Keeping Code Clear and Easy to Read Enhances Performance

When technologies and programs are upgraded, their code usually gets a major overhaul that runs the risk of throwing off the whole system. People must then perform extensive and exhaustive reworking to get the code up to the new par, but with powerful machine learning assistants, optimizing the code for future use can become a simpler, quicker task.

Past Performance May Predict Future Budget Requirements

The history of software development shows that many projects go beyond initial budget and time projections. With adaptable AI, past projects and endeavors may be analyzed to more accurately project the budget and time requirements for a new software development project. Once an intelligent computer gathers enough information, it should be able to learn from it and pass its learning to us.

Coupling Development and Business is a Reality With AI

In the same vein as budget prediction, machine learning may be fed information about past projects and current business goals in order to ascertain key strategies for moving your company forward. A lot of time and effort is spent figuring out which features and upgrades to ignore and which ones to push ahead, and competent AI assistants can make this task easier.

When human and artificial intelligence is pushed forward and united, tech and business also speed ahead. As machine learning gets more sophisticated, your future gets simpler.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS