Techtalks: Software Development

Hello,

Is it possible to use Six Sigma Quality Management with Software Development Processes?

What's your experience with that?

If you're using an Agile method like Scrum or XP, isn't Six Sigma too bureaucratic?

I'm talking about quality management in software development as a whole, from requirements gathering until deployment and operations, and not only the construction phase (in which tools like TDD and unit testing are more or less already established as best practices). All these topic of quality management and software development is discussed in the six sigma green belt certification. 

Thanks

Atonal

Dear Altexsoft Team,

I'm writing to ask about your services for developing custom software for my company. I found your website and was amazed by the knowledge and skill you have in developing specialized software for different sectors.

I'd like to know more about your bespoke software development pricing policies, project management strategy, and development process. In addition, I'd want to know what technologies and frameworks you're an expert in, as well as your experience creating solutions for companies like mine.

I'd appreciate it if you could give me a ballpark figure for the price and schedule for a moderately complicated custom software project.

I appreciate you taking the time to contact me, and I hope to hear from you soon.

Basically, in the software development process, DevOps is a practice that emphasizes the collaboration and communication of both software developers and other information-technology (IT) professionals while automating the process of software delivery and infrastructure changes. DevOps aims to help an organization rapidly produce software products and services and to improve operations performance.

DevOps is a set of best practices that combines software development (Dev) and IT operations (Ops). DevOps improves communication and collaboration between these two groups. It also automates software delivery and infrastructure changes.

Various companies consider DevOps certification professionals, having strong knowledge of software development with the given skills set.

Sorry if this is something it was just asked, but I searched for it without success. I am thinking about to apply for the tensorflow certification exam. My first question is if, during the exam, the custom activation functions are allowed.

For example: Imagine a question about a regression when the data is:

features = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=float)
targets = np.array([0.0, 1.0, 4.0, 9.0, 16.0, 25.0, 36.0], dtype=float

This is clearly a x^2 problem. Could I do something like this?

tf_lpow = lambda x: tf.math.pow(x, 2)
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(units=1, activation=tf_lpow, input_shape=(1,)),
])

Considering that maybe this could not be allowed, I was thinking about another solution:

lr_scheduler = ReduceLROnPlateau(monitor='loss', factor=0.75, patience=50, min_lr=3e-80)
callbacks = [lr_scheduler]
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(units=6, activation='sigmoid', kernel_regularizer=tf.keras.regularizers.l2(0.01), kernel_initializer=tf.keras.initializers.RandomNormal(stddev=0.01), input_shape=(1,)),
    tf.keras.layers.Dense(units=1, activation='linear')
])

But even in the case the loss is decreasing, the accuracy is stack in 0.2857, not reaching the goal. In this case, what could I do?

Thanks in advance.

Hello @lance ziemann

Both gRPC and the net/rpc package in Golang are used to implement remote procedure call (RPC) in distributed systems, but they differ in several ways:

1. Protocol: net/rpc uses a custom protocol while gRPC uses Protocol Buffers (protobuf) over HTTP/2.

2. Language Support: gRPC supports multiple languages, including Go, C++, Java, Python, Ruby, and others, while the net/rpc package is only available in Go.

3. Streaming: gRPC supports both unary (single-request, single-response) and streaming RPCs (multiple requests and responses), while net/rpc only supports unary RPCs.

4. Interceptors: gRPC has interceptors, which allow middleware functions to be executed before and after the handler function, while net/rpc does not have this feature.

5. Code Generation: gRPC generates client and server code from .proto files, while net/rpc does not have this feature.

6. Transport Security: gRPC has built-in support for transport security using Transport Layer Security (TLS), while net/rpc does not have this feature.

Overall, gRPC provides more features and flexibility than the net/rpc package, making it a better choice for building distributed systems with modern requirements.

Hope it helps you. 

Here are a few softwares for picture recognition that are free and also paid

Clarifai
Vue.ai
Anyline
muse.ai
Edge AI

There is also many other software too you can check them.

Hi there,

If you write your API docs manually, you can do that anywhere, from WordPress to GitHub. But it’s often convenient to use a generator – a tool that will automatically populate your documentation pages with code snippets, headers, and sample requests. 

We usually prefer giving a few tips about choosing the tool rather than making a choice for others, so when looking for a generator, pay attention to these features:

• Latest OpenAPI support
• Customization capabilities
• Automatic docs updates
• Available themes and templates
• Open source features if you’re on a budget

You can start by reviewing this list of tools loved by API developers:

• Redocly
• SwaggerUI
• Postman
• DapperDox
• DocGen by LucyBot
• RapiDoc

Hope this helped! We also recommend reading our article on writing good API documentation as it gives some foundational tips.

Software architect builds the product layout and organizes how the product would be. 

Solution architect finds solution after building the application to the programmers also try to reverse engineer the solution.

To answer this research question, we draw on a unique feature of the ICO context: the possibility to trade tokens in secondary markets after the ICO List completion. This aftermarket trading adds a speculative function for ICO investors and resembles the trading of shares after an IPO (e.g., Momtaz, 2020d; Fisch, 2019; Lyandres et al., 2019). 

Thank you for your question. 

It goes without saying that corporate intranet portal software differs from one company to another based on business processes it needs to streamline. 

We’ve tried to single out several must-have intranet features for you to consider:

• A responsive notification system is probably the most crucial feature as it provides employees with all the relevant updates, upcoming tasks, etc.
• A convenient document management system not only organizes the company’s spreadsheets, text documents, and slide shows but also ensures their security.
• The direct messaging feature will improve internal communication and eliminate the time spent on sending emails and waiting for responses.
• An easy-to-navigate home page, where users can log in and search for important information such as work updates, company news, and so on.
• Automated workflows to speed up working processes and save time on more important tasks.
• Team workplaces to make it easier for different employees to collaborate on shared projects and work related to those projects.
• A mobile-friendly design to provide employees with the opportunity to do work not only on desktop computers but also smartphones and tablets.
• A comprehensive and intuitive search capability so that employees can find the information they need quickly. 

Of course, this is a non-exhaustive list of intranet portal functionalities, just the most essential ones. 

Thank you for your question.

To start with, APIs and SDKs are both involved in the process of software development, but these are different things and their roles are different too.

An API (stands for Application Programming Interface) is a set of clearly defined protocols that allow data exchange between different software programs and components. By the way, you can learn more about APIs in our dedicated article here.

An SDK (stands for Software Development Kit) is a set of pre-made tools and instructions that allow you to build software applications for a specific platform.

Simply put, an SDK helps you build a software program on top of it and with the help of it. An API is a way for programs to talk with each other. The key point is, SDKs may contain APIs as a part of the kit. APIs do not contain SDKs. So, you don’t have to choose between the two.

Data Engineering is focused on building the infrastructure to support data processing, while Data Warehousing is focused on collecting and managing the data itself to support analysis and decision-making. Both are crucial components of modern data management and play a vital role in the success of data-driven organizations.

Hello

It is essential to have a clear product vision and understand user needs. Evaluate the impact and value of each feature, considering customer demand, revenue potential, and market trends. Assess the effort and feasibility of development, taking into account resources and dependencies. Consider urgency and time constraints, and use prioritization frameworks to categorize features. Refer source : ccsp course

Hi, i want to open the new channel management company. and i need the financial feasibility plan for this, please. how you many help in this regards?

thanking you

Awaiting reply

Hello,
 
MLOps is a kind of reincarnation of DevOps for the needs of tech-driven companies relying heavily on machine learning algorithms. So, similar to DevOps, it facilitates communication between different experts  — more specifically, between data scientists who build machine learning models and operations professionals who put these models into production. 
 
The key promises of MLOps for businesses are 1) reduced time to market of AI-driven products, 2) enhanced user experience due to the fact that apps get timely updates, 3) higher quality of predictions, and 4) the ability of data scientists to focus on building new models  — instead of diving into deployment routine. 
 
You can learn more about MLOps from our dedicated article  — MLOps: Methods and Tools of DevOps for Machine Learning.