Microsoft Dynamics AX 2012 and 365 Customization Companies in Jakarta
Microsoft Dynamics AX is an ERP solution for multi-site, international enterprises. With capabilities for financial, human resources, and operations, it provides a comprehensive set of functionalities to run your enterprise, with the agility required to accomodate a wide variety of processes, workflows and business conditions. Microsoft Dynamics AX gives organizations an exceptional customer focus by being able to take advantage of cloud services, run agile operations that exceed customer needs, and help engage customers on their terms across the Web, social, apps and mobile fronts. With Microsoft Gold Competencies in Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and as a Microsoft Master VAR, UltronIT Group can implement Microsoft Dynamics AX for your organization on time, on budget, and with a keen focus on your business goals.
A single, global ERP deployment
Replace numerous legacy applications with a single, centralized implementation of Microsoft Dynamics AX. Lower IT costs, increase productivity and enable growth.
Support the connected enterprise
Scale globally by taking advantage of cloud services. Run agile operations and connect employees by enabling real-time access to insights on any device.
Built for your business and industry
Manage all your core business functions efficiently. Choose from innovative, industry-specific functionalities to manage your supply chain, projects and more.
Rapid and flexible deployment
Built on Microsoft Azure, Lifecycle Services streamlines implementations, upgrades and customizations. UltronIT leads the charge in cloud delivery of custom industry solutions.
Whether you are getting started with a new implementation, trying to fix an existing implementation gone wrong or upgrading to the latest version, UltronIT Group enables clients to optimize their Microsoft Dynamics AX investment. We provide both on-site and remote Dynamics AX training to get your team up-to-date as quickly and efficiently as possible. With a dedicated help desk, we are readily available to help you tackle any Dynamics AX issue you might encounter.
Microsoft Gold Competencies in ERP and CRM
2015 Inner Circle Member for Microsoft Dynamics
On-site, off-site and off-shore development and support
In-house help desk for Dynamics AX and all Microsoft platform technologies including Dynamics CRM, Sharepoint, Exchange, Skype for Business/Lync and more
50+ certifications in Microsoft Dynamics AX including Projects, Human Resources Management, Financials, Trade & Logistics and Production
UltronIT Group Report Migration Tool for Upgrades reduces average conversion time from 10 hours/report to 1 hour/report
Dedicated web-based client site for collaboration, document management and project deliverables
Broadly speaking, there are three paradigms of machine learning: supervised learning, unsupervised learning, and reinforcement learning.2
Supervised learning: In supervised learning, the algorithm uses existing information or data points that come with clear labels or categories.2 For example, the algorithm is trained on a number of cat and dog pictures (of various types) to differentiate between the two animals based on features like color, size, ears, nose, and so on. Once it’s trained, it can then label any new images given to it as “cat” or “dog.”This enables the machine to identify dogs and cats of various colors, sizes, and features.The objective of supervised learning is for the machine to learn from many labeled examples.
Unsupervised learning: In unsupervised learning, the data points exist without any attached labels or categories. Unlike supervised learning, which deals with data that’s already labeled, unsupervised learning focuses on organizing data and uncovering hidden patterns.2The unsupervised learning method involves organizing data into distinct clusters or exploring different viewpoints to simplify complex data and enhance data analysis.3 The goal is to discover meaningful connections within the data rather than relying on predefined labels.While supervised learning assigns labels like “cat” and “dog” to images, unsupervised learning involves finding inherent patterns without the aid of explicit labels. Imagine a machine-learning system is tasked with sorting through a collection of wildlife photographs that don’t include information on animal species. In this example, unsupervised learning algorithms can identify similar features in images and group them without being explicitly instructed about the specific animal.
Reinforcement learning: In reinforcement learning, the algorithm determines the best course of action based on data and feedback.2 For example, a robot uses its sensors to process information at certain time intervals. Once the algorithm categorizes and labels the collected information, an immediate feedback signal evaluates the algorithm’s effectiveness.This iterative process enables the algorithm to refine the labeling process over time. The algorithm processes input data to create outputs, followed by an immediate feedback signal that gauges the output’s effectiveness. Based on this signal, the algorithm adjusts itself in order to achieve the highest reward.
Machine learning is the process that helps a computer learn without direct instruction. Machine learning uses algorithms to discover patterns in large datasets. Those patterns are then used to create a comprehensive AI model, allowing for predictions with high accuracy that match your specific needs.2
During the creation of AI models, machine-learning models are carefully trained to make predictions using smaller portions of data. The accuracy of the predictions depends on how well the smaller datasets represent the larger dataset. When the sample data is a good representation of the larger dataset (from which it’s sampled), the machine-learning model yields more reliable outcomes.2
A helpful analogy to the machine-learning process is how an athlete learns the skills to play soccer. A soccer player starts by practicing basic skills like dribbling and passing. Through continuous practice, the soccer player begins to recognize playing patterns, anticipate the movements of opponents and teammates, and develop better coordination. Over time, the soccer player becomes more skilled at making split-second decisions during a match and adjusting strategies based on the evolving dynamics of the game.
Similarly, a machine-learning model starts with learning patterns and relationships in data during the training process. The model then uses these learned patterns to make predictions or perform tasks for which it was designed. Just as the soccer player’s expertise grows through practice and experiencing various game scenarios, the machine-learning model’s proficiency evolves through iterative learning and adaptation within the parameters set for the model.
AI continues to shape industries and influence new aspects of daily life. Having a solid grasp of fundamental AI concepts helps you understand how AI works and how you can integrate AI into your work. Familiarity with AI’s foundational concepts empowers you to navigate complex discussions, critically assess AI applications, and harness AI’s potential for innovation.
Here’s a list of foundational concepts that are important for understanding AI:
Data: Data is the raw material that AI systems use to analyze and make predictions. The dataset used to train algorithms directly impacts the trained AI models’ accuracy. Hence, high-quality data that are diverse and representative are essential to develop advanced AI models.
Algorithm: An algorithm is a set of step-by-step instructions that guide an AI model to perform tasks. Algorithms are used in data processing, feature extraction, model training, and decision making.
Machine learning: Machine learning is the process by which machines learn from data and improve their performance over time. Machine learning utilizes different types of techniques, such as supervised learning, unsupervised learning, and reinforcement learning.
Model: An AI model is a program (or algorithm) that results from training on a given set of data. In other words, the AI model represents the patterns and relationships that the machine-learning algorithm discovered during the training.
Feature extraction: Feature extraction involves selecting the most relevant attributes or features from the given data so that they can be fed into the model. Feature extraction aims to improve an AI model’s performance by reducing the number of features that the model needs to process.
Feature engineering: Feature engineering is the process of selecting and modifying features (attributes and characteristics) from data to enhance an AI model’s performance and improve the AI model’s ability to capture patterns and relationships within the data.
Prediction and classification: Prediction is the process of guessing future outcomes, while classification is the process of assigning labels or categories to data points based on what the AI models have learned from the training data.
Neural networks: Neural networks consist of interconnected nodes in a layered structure. This structure is loosely inspired by the structure of the human brain. Neural networks are particularly effective for tasks like image and speech recognition.
Training and testing: AI models are trained on a subset of data and tested on another subset of data to evaluate their performance. Training and testing ensure that the AI model can generalize new data.
Overfitting and underfitting: Overfitting occurs when an AI model becomes too specialized to the training data and performs poorly with new data. Underfitting happens when an AI model is too simple to capture the underlying patterns in the data.
Optimization: Optimization is the process of finding the best parameters for an AI model. Optimization often involves adjusting an AI model’s internal settings to minimize errors.
In a data warehouse, both surrogate keys and business keys are essential for effective data warehousing and data integration, but they serve different purposes.
Surrogate key: A surrogate key is a system-generated identifier that is used to uniquely identify a record in a table within the data warehouse. It has no business meaning and is typically an integer or a unique identifier. Surrogate keys are used to maintain consistency and accuracy in the data warehouse, especially when integrating data from multiple sources. They help to avoid issues that can arise from changes in the source systems, such as reusing or changing business keys.
Business Key: A business key, also known as a natural key, is an identifier that comes from the source system and has business meaning. It’s used to uniquely identify a record in the source system. Examples of business keys include product codes, customer IDs, and employee numbers. Business keys are important for maintaining traceability between the data warehouse and the source systems. They help to ensure that data in the warehouse can be accurately matched to the corresponding records in the source systems.
Load a dimension table
Think of a dimension table as the “who, what, where, when, why” of your data warehouse. It’s like the descriptive backdrop that gives context to the raw numbers found in the fact tables.
For example, if you’re running an online store, your fact table might contain the raw sales data – how many units of each product were sold. But without a dimension table, you wouldn’t know who bought those products, when they were bought, or where the buyer is located.
In Microsoft Fabric, there are many ways you can choose to load data in a warehouse. This step is fundamental as it ensures that high-quality, transformed, or processed data is integrated into a single repository.
Also, the efficiency of data loading directly impacts the timeliness and accuracy of analytics, making it vital for real-time decision-making processes. Investing time and resources in designing and implementing a robust data loading strategy is essential for the success of the data warehouse project.
Understand data ingestion and data load operations
While both processes are part of the ETL (Extract, Transform, Load) pipeline in a data warehouse scenario, they usually serve different purposes. Data ingestion/extract is about moving raw data from various sources into a central repository. On the other hand, data loading involves taking the transformed or processed data and loading it into the final storage destination for analysis and reporting.
Fabric data warehouses and lakehouses automatically store their data in OneLake using the Delta Parquet format.
It’s important to recognize that as new intelligent technology emerges and proliferates throughout society, with its benefits come unintended and unforeseen consequences. Some of these consequences have significant ethical ramifications and the potential to cause serious harm. While organizations can’t predict the future yet, it’s our responsibility to make a concerted effort to anticipate and mitigate the unintended consequences of the technology we release into the world through deliberate planning and continual oversight.
Threats
Each breakthrough in AI technologies brings a new reminder of our shared responsibility. For example, in 2016, Microsoft released a chatbot on X called Tay, which could learn from interactions with X users. The goal was to enable the chatbot to better replicate human communication and personality traits. However, within 24 hours, users realized that the chatbot could learn from bigoted rhetoric, and turned the chatbot into a vehicle for hate speech. This experience is one example of why we must consider human threats when designing AI systems.
Novel threats require a constant evolution in our approach to responsible AI. For example, because generative AI enables people to create or edit videos, images, or audio files so credibly that they look real, media authenticity is harder to verify. In response, Microsoft is teaming with other technology and news stakeholders to develop technical standards to address deepfake-related manipulation.
AI is the defining technology of our time. It’s already enabling faster and more profound progress in nearly every field of human endeavor and helping to address some of society’s most daunting challenges. For example, AI can help people with visual disabilities understand images by generating descriptive text for images. In another example, AI can help farmers produce enough food for the growing global population.
At Microsoft, we believe that the computational intelligence of AI should be used to amplify the innate creativity and ingenuity of humans. Our vision for AI is to empower every developer to innovate, empower organizations to transform industries, and empower people to transform society.
Societal implications of AI
As with all great technological innovations in the past, the use of AI technology has broad impacts on society, raising complex and challenging questions about the future we want to see. AI has implications on decision-making across industries, data security and privacy, and the skills people need to succeed in the workplace. As we look to this future, we must ask ourselves:
How do we design, build, and use AI systems that create a positive impact on individuals and society?
How can we best prepare workers for the effects of AI?
How can we attain the benefits of AI while respecting privacy?
Underlying the preceding values are two foundational principles that are essential for ensuring the effectiveness of the rest: transparency and accountability. It’s critical that people understand how AI systems come to conclusions when they’re used to inform decisions that have an effect on people’s lives. For example, a bank might use an AI system to decide whether a person is creditworthy, or a company might use an AI system to determine the most qualified candidates to hire.
A crucial part of transparency is what we refer to as intelligibility, or the useful explanation of the behavior of AI systems and their components. Improving intelligibility requires that stakeholders comprehend how and why they function so that they can identify potential performance issues, safety and privacy concerns, biases, exclusionary practices, or unintended outcomes. We also believe that people who use AI systems should be honest and forthcoming about when, why, and how they choose to deploy them.
To ensure transparency in your AI system, you should:
Share key characteristics of datasets to help developers understand if a specific dataset is appropriate for their use case.
Improve model intelligibility by applying simpler models and generating intelligible explanations of the model’s behavior. For this task, you can use the Responsible AI Dashboard, available at the resources section.
Train employees on how to interpret AI outputs and ensure that they remain accountable for making consequential decisions based on the results.
Accountability
The people who design and deploy AI systems must be accountable for how their systems operate. Organizations should draw upon industry standards to develop accountability norms. These norms can ensure that AI systems aren’t the final authority on any decision that impacts people’s lives and that humans maintain meaningful control over otherwise highly autonomous AI systems.
To ensure accountability in your AI system, you should:
Set up internal review boards to provide oversight and guidance on the responsible development and deployment of AI systems. They can also help with tasks like defining best practices for documenting and testing AI systems during development or providing guidance for sensitive cases.
Ensure your employees are trained to use and maintain the solution in a responsible and ethical manner and understand when the solution may require extra technical support.
Keep humans with requisite expertise in the loop by reporting to them and involving them in decisions about model execution. When automation of decisions is required, ensure they’re able to inspect, identify, and resolve challenges with model output and execution.
Put in place a clear system of accountability and governance to conduct remediation or correction activities if models are seen as behaving in an unfair or potentially harmful manner.
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people.
To ensure privacy and security in your AI system, you should:
Comply with relevant data protection, privacy, and transparency laws by investing resources in developing compliance technologies and processes or working with a technology leader during the development of AI systems. Develop processes to continually check that the AI systems are satisfying all aspects of these laws.
Design AI systems to maintain the integrity of personal data so that they can only use personal data during the time it’s required and for the defined purposes that have been shared with customers. Delete inadvertently collected personal data or data that is no longer relevant to the defined purpose.
Protect AI systems from bad actors by designing AI systems in accordance with secure development and operations foundations, using role-based access, and protecting personal and confidential data that is transferred to third parties. Design AI systems to identify abnormal behaviors and to prevent manipulation and malicious attacks.
Design AI systems with appropriate controls for customers to make choices about how and why their data is collected and used.
Ensure your AI system maintains anonymity by taking into account how the system removes personal identification from data.
Conduct privacy and security reviews for all AI systems.
Research and implement industry best practices for tracking relevant information about customer data, accessing and using that data, and auditing access and use.
To build trust, it’s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It’s also important to be able to verify that these systems are behaving as intended under actual operating conditions. How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing.
To ensure reliability and safety in your AI system, you should:
Develop processes for auditing AI systems to evaluate the quality and suitability of data and models, monitor ongoing performance, and verify that systems are behaving as intended based on established performance measures.
Provide detailed explanation of system operation including design specifications, information about training data, training failures that occurred and potential inadequacies with training data, and the inferences and significant predictions generated.
Design for unintended circumstances such as accidental system interactions, the introduction of malicious data, or cyberattacks.
Involve domain experts in the design and implementation processes, especially when using AI to help make consequential decisions about people.
Conduct rigorous testing during AI system development and deployment to ensure that systems can respond safely to unanticipated circumstances, don’t have unexpected performance failures, and don’t evolve in unexpected ways. AI systems involved in high-stakes scenarios that affect human safety or large populations should be tested both in lab and real-world scenarios.
Evaluate when and how an AI system should seek human input for impactful decisions or during critical situations. Consider how an AI system should transfer control to a human in a manner that is meaningful and intelligible. Design AI systems to ensure humans have the necessary level of input on highly impactful decisions.
Develop a robust feedback mechanism for users to report performance issues so that you can resolve them quickly.