Full stack development is a buzz word nowadays. More and more companies are hiring full stack developers to save their time and cost. But most of the people still confused about the homonyms like Full stack developers, MEAN stack developers, MERN stack developers, etc. What is full stack developer? How to become a full stack developer? What does a full stack developer do? How to hire full stack developers? Let’s find answers in this article!!
Part 1 of this article discussed a few simple techniques that helped with initial scalability of machine learning… and hopefully with reducing manual ops. Since then, despite a few production hiccups due the lack of high availability, life has been pretty good. However, traffic soon starts to increase, data piles up, more models need to be trained, etc. Technical and business stakes are getting higher, and the current architecture will go underwater soon. This post focuses on scaling training to a large number of machines.
The pressing question enterprises rapidly need to answer if they want to remain competitive in the future, is how they can overcome security and collaboration challenges? One option to consider would be to decentralise the back-end infrastructure that underpins data processes using a private blockchain. Technologies such as distributed ledgers offer a viable solution. Although the technology is still in its relative infancy when compared to other technologies associated with digital transformation, its role in the corporate world is growing fast.
One thing you should do is build a portfolio of your personal machine learning projects. But, how to do that? I’ve seen hundreds of examples of personal projects that ranged from very good to very bad. So in this post, I’ll tell you how. If I had to summarize the secret to a great ML project in one sentence, it would be: Build a project with an interesting dataset that took obvious effort to collect and make it as visually impactful as possible.
Artificial intelligence has become a part of our day-to-day lives many times in a single day and most of us aren’t even aware of this. While there is a buzz on various emerging and hot technology platforms AI has arrived in 2019 where it will be leveraged in real-time within the enterprise and create true ROI as a result. It is true a few on the list like autonomous things, smart spaces, IoT, and even the empowered edge are used in some capacity today but true industry adoption of AI-centered tech initiatives are becoming mainstream in 2019.
It is now universally agreed that DevOps has revolutionised testing and development - and it is here to stay. So, there's a lot to consider, and it’s a complex road to getting it right. But, ultimately, we believe that leaders who don’t adequately support DevOps within their organisations — whether it’s in getting the right tools, hiring the right teams, employing the right processes or backing new ways of working — will pay dearly for their decisions in the long term.
Keras is the recommended library for beginners since its learning curve is very smooth compared to others. Keras is a Python library that provides, in a simple way, the creation of a wide range of Deep Learning models using as backend other libraries such as TensorFlow, Theano or CNTK. Although Keras is currently included in Tensorflow package, but can also be used as a Python library. To start in the subject I consider that this second option is the most appropriate.
There is a stark difference between large data and big data. Using Pandas with large data could help you explore another useful feature in Pandas to deal with large data by reducing memory usage and ultimately improving computational efficiency. Typically, Pandas has most of the features that we need for data wrangling and analysis. Pandas is seriously a game changer when it comes to cleaning, transforming, manipulating and analyzing data. In simple terms, Pandas helps to clean the mess.
It would seem that, finally, technology had appeared which would enable us to create, digitize, and transmit value, just as we create and transmit information on the classic Internet. A given blockchain-ecosystem, whatever it may be, is unlikely in itself to become what we could call the Internet of Value, just as WiFi could not become the internet. But all of them working together can. However, this will happen only in the event of the emergence of a Layer 3 technology which can ensure their interoperability.
Every firm is unique. Even within narrow verticals, the overlap in what two different companies need is smaller than you might think. Don’t try to fit your business into a box. If your business has a large amount of data and you are asking yourself, “How can I use AI to build something smart from our data?” Machine learning is just a tool to automate pattern discovery and then make smart predictions based on those patterns. Most of the time, it’s about improving an existing process by making it a little bit smarter.
Product-related risks could refer to the definition and the actual development/ implementation of Minimum Viable Product. A poorly-defined product, regardless of how well-built, will probably fail to solve the problem and deliver value to its users. Poor implementation of a well-defined product will also fail to create value to the user. Engineering-heavy startups tend to put more effort than needed on the technical aspects of the product. You need to apply agile and experimentation principles, an effective way to improve your product and create value to your users.
So you want to build a ML model. No Machine Learning is easier to manage than no Machine Learning. Figuring a way to use high-level services could save you weeks of work, maybe months. In this series of posts, we’ll discuss how to train ML models and deploy them to production, from humble beginnings to world domination. Along the way, we’ll try to take justified and reasonable steps, fighting the evil forces of over-engineering.
Docker is a platform to develop, deploy, and run applications inside containers. Docker is essentially synonymous with containerization. If you’re a current or aspiring software developer or data scientist, Docker is in your future. Don’t fret if you aren’t yet up to speed — this article will help you understand the conceptual landscape — and you’ll get to make some pizza along the way. By the end of the series (and with a little practice) you should know enough Docker to be useful.
The adoption of AI is rapidly growing in the workplace; however, to take full advantage of AI’s opportunities, businesses must understand and overcome lingering doubts from their customers and employees. There’s no question that businesses are at an inflection point with their use of AI. To achieve greater impact, we must change the narrative about lingering concerns. It is critical to educate both employees and customers about AI’s potential and enable them with tools to take advantage of its benefits.
A soft skill that keeps coming to the forefront is the ability to explain complex machine learning algorithms to a non-technical person. An algorithm is the mathematical life force behind a model. What differentiates models are the algorithms they employ, but without a model, an algorithm is just a mathematical equation hanging out with nothing to do. An algorithm is what is used to train a model, all the decisions a model is supposed to take based on the given input, to give expected output.
Want to learn more about how your retail business can efficiently use Machine Learning and Data Science? Data Science and Big Data analytics is not a magic pill that can solve all your problems. However, it’s a strong competitive advantage as it gives you knowledge and a better sense of control. What you need and can do is to have a vivid picture of what is going on in your business, as the more info you have at hand, the more clearly you can see that something is going wrong and needs fixing.
Often a classifier will have some confidence value in each category. These are most often generated by probabilistic classifiers. Sometimes, we threshold the probability values. In computer vision, this happens a lot in detection. The optimal threshold varies depends on the tasks. Some performance metrics are sensitive to the threshold. This post is going to cover some very basic concepts in machine learning, from linear algebra to evaluation metrics. It serves as a nice guide to newbies looking to enter the field.
Forward-thinking business leaders are making sure digital is part of the overall strategy discussion, not just transformation but also keeping a keen perspective on the competitive and potential mergers or acquisitions. By being involved and understanding what is really needed to undergo digital transformation, boards can ensure that leadership is executing on its plan and steering the company toward a successful digital future. In response to shifts in the business landscape and changing business requirements, the role of the CIO is going to be reinvigorated and extended across a number of dimensions. Here are eight examples.
What is Automated Machine Learning? It is, quite simply, the automated process of features and algorithm selection that supports planning. Business users can leverage machine learning and assisted predictive modeling to achieve the best fit and ensure that they use the most appropriate algorithm for the data they wish to analyze. Business users can take advantage of AutoML tools to explore patterns in data and receive suggestions to help them gain insight – all without dependence on IT or data scientists.
Now is time to start to review the basic concepts of neural networks. This post will present some basic concepts of neural networks, reducing theoretical concepts as much as possible, with the aim of offering the reader a global view of a specific case to facilitate the reading of the subsequent posts where different topics in the area will be dealt with in more detail. A brief intuitive explanation of how a single neuron works to fulfill its purpose of learning from the training dataset can be helpful for the reader.
A human recruiter, likely to be juggling various tasks, can’t always give their full attention to every single potential candidate. Chatbots can be used to automate the top of funnel interactions, ensuring that each candidate gets timely, personalised responses at each stage of the recruitment process. Given the high volume of emails, calls, messages, pings, and alerts that recruiters have to try and stay on top of, chatbots make good personal assistants. They are nowhere near to replicating human capabilities beyond administrative responsiveness – but this in and of itself is already helping recruiters do better business.
Putting data in the hands of a few experts is a powerful thing, but making it available throughout an organization can be a game changer. Used properly, data can allow us to design better products, understand customers, and improve efficiency. Organizations now have access to affordable, powerful tools that make this possible, but providing access to the data is only part of the equation. Employees must be able to assess the value of the data they have and interpret it properly.
Today, deep learning has become pivotal to many of the applications we use every day such as content recommendation systems, translation apps, digital assistants, chatbots and facial recognition systems. Deep learning has also helped create advances in many special domains such as healthcare, education, and self-driving cars. The fame of deep learning has also led to confusion and ambiguity over what it is and what it can do. Here’s a brief breakdown of what deep learning and neural networks are, what they can (and can’t do) and what are their strengths and limits.
How to measure something that by its nature is abstract and unmeasurable, like team collaboration? What KPIs would you use to assess the overall state of team collaboration and ensure its long-term monitoring to draw unbiased conclusions? Overall, there are a plethora of software solutions created to evaluate personal performance and monitor employees’ development. However, those solutions can hardly deal with collaboration assessment, or they require substantial customization effort to handle such a non-trivial task. Happily, big software providers have started to incorporate relevant functionality into their core systems to prevent organizations from investing in stand-alone solutions.