How Blockchain Technology Is Changing The World

 We see the stories every day. Feds charge 21 people in global crypto money laundering bust. FTX not only filed for bankruptcy, but hackers allegedly stole customer assets. Cryptocurrency investors have lost more than $2 trillion in the past year.

The Importance Of Considering Collaboration During Digital Transformation

The last few years have permanently changed how we live, operate businesses, and integrate technology into our everyday lives.

Most notably, we’ve shifted our mindsets from a hyper-individualistic culture to a more collaborative viewpoint. Many are emphasizing the greater good and how our actions impact the people around us. This paradigm shift in the fabric of our society is certainly not limited to how we connect and interact: It also heavily influences the way we do business and integrate new technologies.

Here’s why collaboration is the key to digital transformation and how our businesses operate as a whole.

Collaboration Boosts Employee Engagement

During the pandemic, collaboration became center stage as companies scrambled to communicate and get work done in a fully remote environment.

One study by Gartner indicates that after the first 18 months of the pandemic, 80% of workers reportedly used digital collaboration tools, up from 40% at the start of 2020. Additionally, the need for mixed meeting modalities in a remote work environment meant companies had to work together to implement the digital infrastructure necessary to support increased collaboration in the workplace. Mixed meeting modalities let employees collaborate regardless of location or time zone, ensuring everyone can participate.

Other new technologies included virtual whiteboards, in-video chat room features to foster one-on-one connections during video meetings, recording and transcription capabilities, and more. But in my experience, it wasn’t the precise technology that boosted employee engagement—it was the effort put into sincere collaboration. Without being able to just swing by someone’s office, employees needed to put more time and effort into connecting with co-workers, communicating about projects and maintaining collaborative workflows. As the Gartner study demonstrates, employees rose to the challenge, proving that new methods of work are here to stay and that collaboration is at the center of business success.

The Difference Between Communication And Collaboration—And Why It Matters

You might think your company has a firm grasp on the concept of collaboration, but it’s important to ensure you aren’t confusing collaboration with communication.

It can be tempting for companies to implement digital workplace tools and technologies for the sake of increased communication, but doing so without creating any real infrastructure to foster true collaboration isn’t effective. Sure, you might implement several different messaging apps, video conferencing tools, project management software and other applications with the goal of increased communication, but in the end, you may be multiplying the effort it takes for employees to complete their tasks. With conversations split between platforms, employees won’t be as encouraged to foster meaningful connections and collaborations with their colleagues.

The bottom line is that communication is centered around knowledge-sharing, while real collaboration puts this knowledge to good use. Streamlining communication, using effective technology and ensuring documents are in order are all essential first steps to true collaboration. And its benefits are immeasurable—it strengthens problem-solving, ensures the best ideas come to the table, creates a deeper bond among your employees and fosters a healthy sense of competition to encourage individual and collective success.

Business And Basketball: Why Team Players Win

In the past, there was a hyper-focus on individual employee performance. But in more physically isolated environments, such as hybrid or remote workplaces, the focus is less on the individual and more on the good of your entire team. Companies should focus less on the how and more on the who—who in your workplace will help you accomplish your goals? Who is on your team and willing to collaborate to bring forth the best ideas to serve your clients and company?

Take Michael Jordan as an example. Even though he is celebrated as one of the best basketball players of all time, he needed a team around him to sharpen his skills. Enter Scottie Pippin.

Pippin and Jordan were both members of the Chicago Bulls in the 1980s and 1990s and helped the team win six NBA championships during their time together on the team. Both were incredibly successful before they were on a team together, but their partnership (and sometimes their rivalry) made them even better. Pippin told the Guardian in 2020, “We grew up together, and we defended each other. That respect we had on the court, that competitiveness we took through to the top—it was special. That was the respect we had for each other because we had to be on the court to do what we did.”

You might argue that Pippin found success on the court during Jordan’s ’93 to ’94 absence, but when Jordan rejoined the team the following year, Pippin had the best season of his career thus far. There’s no way around it: Having a good team around you sharpens your skills and helps bring out your best performance.

The real beauty of true collaboration is that, at the end of the day, your teammates (or co-workers) are there to strengthen you, support you, and make sure your business as a whole wins. That’s why I believe that effectively executing this core tenet will be essential to businesses’ long-term success.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbesbusinesscouncil/2023/02/10/the-importance-of-considering-collaboration-during-digital-transformation/?sh=2c79e5c3a127

On CRM: The Inconvenient Truth About Salesforce

 Earlier this month Salesforce, the undisputed leading customer relationship management software provider in the world, announced that it was laying off 10 percent of its workforce - more than 7,350 employees - and closing some offices.

On CRM: 5 Myths About ChatGPT

 There has been a lot written about ChatGPT, the open-source, AI-driven conversational chatbot released late last year by OpenAI. It's a powerful tool and its underlying technology will have a big impact on both our professional and personal lives in the coming years. But the media loves to over-hype things - particularly tech things - so it's important to know what's real, and what's myth, about this new platform. So let's focus on the myths.

It’s Time For Software Engineering To Grow Up

 The recent market correction has been a long time coming. For over a decade, low interest rates and easy access to capital fueled a period of unprincipled growth in Silicon Valley. “Cash-flow positive” had become a distant memory of a bygone era. But as Edward Abbey put it, “Growth for the sake of growth is the ideology of the cancer cell.” He was referring to the erosion of wilderness at the hands of uncontrolled urban expansion in his beloved Arizona, but the analogy applies to companies as well.

How To Manage Software Developers Who Moonlight?

 With a booming technology industry comes the potential for software developers to engage in moonlighting—working on multiple projects and earning additional income in the process. While this can be a lucrative opportunity for software developers, it can pose significant risks to employers. So, how can employers effectively manage their software developers who moonlight?

As with any business challenge, the first step is to understand why software developers engage in moonlighting in the first place. Taking on additional work can provide a significant boost to their income. Additionally, the tech industry's remote working environment makes moonlighting even more attractive, as developers can work from anywhere and still receive a paycheck.

Achieving Next-Level Value From AI By Focusing On The Operational Side Of Machine Learning

Technology research firm Gartner, Inc. has estimated that 85% of artificial intelligence (AI) and machine learning (ML) projects fail to produce a return for the business. The reasons often cited for the high failure rate include poor scope definition, bad training data, organizational inertia, lack of process change, mission creep and insufficient experimentation.

To this list, I would add another reason that I have seen many organizations struggle to achieve value from their AI projects. Companies often have invested heavily in building data science teams to create innovative ML models. However, they have failed to adopt the mindset, team, processes and tools necessary to efficiently and safely put those models into a production environment where they can actually deliver value.

To avoid this trap and achieve greater value from AI, here are four recommendations to help your organization translate your data scientists' amazing algorithms into real business impact.

1. Adopt a software mindset.

ML models are undoubtedly important, but developing ML code is just one part of the AI/ML life cycle. Data collection, feature extraction, data verification, machine resource management and other activities adjacent to the ML code actually consume the bulk of time and resources in the ML life cycle.

To be successful, companies must stop thinking of models as an end on their own. The fact is that a model is just a way to transform data written in the form of a function. In short, the model is just software.

When software engineers think about putting a model into production, their concerns are around how the model handles errors, whether the model will do what it is expected to do, whether it can respond quickly enough and whether it will integrate effectively into the organization's software stack.

Adopting a software mindset means moving away from an "artisanal" approach of handling every model as a one-off toward an "industrial" approach focused on putting the tools and processes in place to get models into production efficiently and effectively.

2. Build an ML platform team.

Since models are software, companies should look to their software organizations when they think about how to structure the ML operations team that will be responsible for bringing models into production.

Where a software organization has product development teams supported by an applications platform team (along with a core group to manage the infrastructure), the AI/ML organization should have data science teams supported by an ML engineering group—along with a team whose mandate is to assemble, manage and monitor the platform that the data science and ML teams use (i.e., an ML platform team staffed with ML platform engineers).

The ML platform engineer is a crossover role—similar to a DevOps position, plus software since they might need to build APIs or support the development of infrastructure patterns, for example. Awareness of data helps because data is so intertwined with ML. The ML platform engineer role also requires strong soft skills, curiosity and a collaborative mindset since they will work with diverse teams across the ML life cycle.

3. Establish end-to-end processes.

When a company is still in the "artisanal" stage of ML and is working with only a few use cases, it can get by with bespoke processes, treating each model as a one-off. However, as it expands the number of models that it's putting into production, it needs to standardize its processes to ensure a high level of confidence in both the processes themselves and in the models that it's putting into production.

This means establishing processes across the entirety of the model life cycle—which can be challenging because of the diverse teams involved throughout the life cycle. For example, different groups or individuals tend to be involved in promoting models from lab to staging and then to prod. As a result, different processes need to be implemented for each stage.

It's worth saying again that processes need to be established across the entire model life cycle. Yes, handoffs need to be defined all the way from experimentation to production. However, a model's life cycle doesn't end when it goes live in production, and procedures should be vetted for monitoring and retraining models as well.

4. Incorporate an operational platform.

Many companies that are successful with AI/ML invariably have a dedicated platform for operationalizing models for a variety of reasons. First and foremost, the computational workloads that a system supports in experimentation or training are very different from the workloads in the operationalizing phase.

In experimentation, the limiting factor is how quickly you can spin up resources independently so that you can use your Scikit-learn or TensorFlow and so on. When you go into the implementation phase, you care about a completely different set of capabilities. Is the platform resilient and high availability? Does it have hooks into Datadog or New Relic?

That's why even companies that have a training platform should consider incorporating an operational platform. As a rule, the ML platform itself should provide "self-service with guardrails," allowing data scientists to quickly and safely deploy models into production. At a minimum, the tools that a high-functioning ML team requires for managing operational AI workloads at scale should include:

• A training platform.

• An operational AI (or serving) platform.

• A data platform.

• DevOps to orchestrate everything.

• A workflow system, which may or may not include a batch prediction platform.

By adopting a software mindset around ML and putting in place the team, processes and tools to safely and efficiently deploy ML models, companies can significantly reduce the time required to put models into production and see value from their research innovations.

Implementing standard end-to-end processes can also improve model governance and prepare teams for upcoming regulations around AI, such as the EU's AI Act and the American Data Privacy and Protection Act (ADPPA).

Finally, these companies can free up their data scientists to develop even more innovative models to deliver intelligent products and services, ultimately increasing AI's value and impact on the business.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/01/17/achieving-next-level-value-from-ai-by-focusing-on-the-operational-side-of-machine-learning/?sh=22fd8f682d7e