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Artificial Intelligence Services

Smart Systems Artificial Intelligence, Machine Learning, Deep Learning services enables businesses to leverage the power of computational intelligence and advanced algorithms to predict and optimize business operations and create new product lines through observational data insights. By exploring your data and transformative Decision Science techniques SSI is able to conjure AI processes that can be infused into your operational practices to expediate more effective decision making to enhance forecasting prediction, and to uncover and exploit your transformative opportunities.

Exploratory Data Analysis
Exploratory Data Analysis enables us to better understand data characteristics and find useful patterns through visual means, which allows for a more in-depth understanding of the data. It is crucial to understand your data before running it through an algorithm. EDA helps you gain insights, make sense of data, and remove unnecessary values.
  • Period: 2 Weeks
  • Resources: 2 – 6 Consultants
  • Locations: 🗹 Client Site 🗹 AI Lab
  • Platforms: Python, Google Colab, Heroku, Computer Vision, Vanguard and Detection
Decision Science Analysis
Decision Science Analysis enables us to establish the key decision points within the organizations. We then identify the mission critical data to drive those decision and based on the volume of datas and the decisions to be made we establish whether the AI techniques we adopt can better enable rapid decision making or more reliable decisions.
  • Period: 2 Weeks
  • Resources: 2 – 6 Consultants
  • Locations: 🗹 Client Site 🗹 AI Lab
  • Platforms: Python, Google Colab, Heroku, Computer Vision, Vanguard and Detection
AI Solutions Analysis
Our AI Solution analysis will facilitate the decision making or may capitalize on the insights we have discovered through the exploration of your data. Based on the data that has been analyzed, or on the results of decision analysis performed companies can decide whether the AI Solutions can be built from either a data or decision standpoint.
  • Period: 2 – 3 Weeks
  • Resources: 2 – 8 Consultants
  • Locations: 🗹 Client Site 🗹 AI Lab
  • Platforms: Python, Shell Script, Streamlit, Heroku, Machine Vision, Convolutional Neural Network, Hyper Parameter
AI Solutions Design
In AI Solution Design, first we need will identify the problems that has to be addressed then we will prepare the Data Sets that is related to the problem we have identified. After selecting the data sets we will choose an algorithm and train the algorithm and then we will identify a Programming Language in which AI Solution need to be designed.
  • Period: 2 – 3 Weeks
  • Resources: 2 – 8 Consultants
  • Locations: 🗹 Client Site 🗹 AI Lab
  • Platforms: Python, Shell Script, Streamlit, Heroku, Machine Vision, Convolutional Neural Network, Hyper Parameter
AI Solutions Modeling
AI Solutions Modelling is a software program/coding in which we will use a trained set of datas to perform specific tasks like recognizing certain patterns. Our Artificial Intelligence models use decision-making algorithms to learn from the training and data and apply that learning to achieve specific pre-defined objectives.
  • Period: 1 – 3 Weeks
  • Resources: 1 – 3 Consultants
  • Locations: 🗹 Client Site
  • Platforms: Matplotlib, Pandas, Numpy, Seaborn, Morphological Analysis, Lemmatisation, Sentiment Analysis
AI Model Training
AI Solutions Training is where our practitioners try to fit the best combination of weights and bias to a machine learning algorithm to minimize a loss function over the prediction range. The main purpose of this phase is to build the relationship between data features and a target label (supervised) or among the features themselves (unsupervised). 
  • Period: 1 – 3 Weeks
  • Resources: 1 – 3 Consultants
  • Locations: 🗹 Client Site
  • Platforms: Keras, Auto Keras, LSTM, Machine Learning, Kaggle, Principal Component Analysis
AI Model Testing
AI Model Testing process is to identify errors and correct them before the model is put into use. Cross-Validation is our common testing method, in this approach data set that the model will be trained on is divided into smaller sets and will be trained on one set and tested on the remaining sets and will be repeated until all the data sets been used.
  • Period: 1-3 Weeks
  • Resources: 2-4 Consultants
  • Locations: 🗹 AI Lab
  • Platforms: Google Colab, Numpy, Pandas, Matplotlib, Automated Machine Learning, Deep Learning, Genralized Linear Model
AI Model IVV
Independent verification and validation of AI models is essential to ensure that they are fit for purpose. This process involves checking that the models are accurate and free from bias, and that they meet the requirements for which they were designed. It is important to carry out this process independently from developers to ensure impartiality.
  • Period: 1-3 Weeks
  • Resource: 2-4 Consultants
  • Locations: 🗹 AI Lab
  • Platforms: Tkinter, Sqlite3, Python, PIL, Automated Machine Learning, Auto SK Learn Classifier, Auto SK Regression
AI Model Deployment
To deploy an AI model, we will first select a platform on which we can deploy our model. Some popular platforms are Amazon Web Services (AWS), and Google Cloud Platform (GCP). Once we have selected a platform, we will create a new project, or select an existing project in which we will add our AI and then set up our environment for deployment.
  • Period: 1 Week
  • Resources: 1 – 2 Consultants
  • Locations: 🗹 Client Site
  • Platforms: Django, Python, Virtual Environment, Pillow, Sqlite3, H2O Auto Machine Learning, Generalized Linear Model
AI User Reviews
User reviews are important for an AI solution because they can help identify potential issues and suggest improvements. By reading user reviews, developers can get an idea of how well the AI solution is working and what could be improved. Furthermore, user reviews can help to build trust with potential users of the AI Solution.
  • Period: 1 Week
  • Resources: 1 – 2 Consultants
  • Locations: 🗹 Client Site
  • Platforms: Django, Python, GitHub, Sqlite3, Machine Learning, Auto Keras, Regressor, Image Classification

AI and ML Increasing Implementation

SSI now serves corporate entities that increasingly expect more from their data by developing Artificial Intelligence and Machine Learning solution that leverage archived and customer generated data to gain insights that they would not have been able to obtain otherwise. This represent just the tip of the iceberg when it comes capabilities which SSI bring to companies though AI and ML implementation for their business operations. As these technologies continue to become more accessible and affordable, SSI is working to discover even more innovative services for businesses using them in a variety of different ways from automating repetitive tasks to providing insights that humans may not be able to see, AI is changing the way businesses operate.

Artificial Intelligence and Machine Learning Market Growth

The global artificial intelligence (AI) market size is expected to reach USD 266.28 billion by 2030, according to a new report by Grand View Research, Inc. It is projected to expand at a CAGR of 36.62% during the forecast period. Increasing demand for AI-based solutions in various industry verticals, such as healthcare, automotive, retail, and others, is expected to drive market growth. North American, specifically the United States, is expected to maintain its dominance in the AI market owing to the presence of major AI solution providers, such as IBM Corporation, Google LLC, and Microsoft Corporation, in the region. While the Asia Pacific region is expected to be the fastest-growing region in the AI market owing to the increasing investments by the governments of China and India in the development of AI-based solutions.

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Request a Quote or Demo

SmartSystems has a diverse portfolio of deployable AI Models. We encourage you to request an AI Application Quotation / Demonstration in a Business Domain of immediate value to you.